• arXiv.cs.SY Pub Date : 2020-01-18
Diganta Bhattacharjee; Kamesh Subbarao

In this technical note, a recursive set membership filtering algorithm for discrete-time nonlinear dynamical systems subject to unknown but bounded process and measurement noise is proposed. The nonlinear dynamics is represented in a pseudo-linear form using the state dependent coefficient (SDC) parameterization. Matrix Taylor expansions are utilized to expand the unknown state dependent matrices about the corresponding state estimates. Upper bounds on the remainders in the matrix Taylor expansions are calculated on-line using a non-adaptive random search algorithm at each time step. Utilizing these upper bounds and the ellipsoidal set description of the uncertainties, a two-step filter is derived that utilizes the `correction-prediction' structure of the standard Kalman Filter variants. At each time step, correction and prediction ellipsoids are constructed that contain the true state of the system by solving the corresponding semi-definite programs (SDPs). Sufficient conditions for boundedness of those ellipsoidal sets are derived. Finally, a simulation example is included to illustrate the effectiveness of the proposed approach.

更新日期：2020-01-22
• arXiv.cs.SY Pub Date : 2020-01-18
Iasson Karafyllis

This paper presents a fundamental relation between Output Asymptotic Gains (OAG) and Input-to-Output Stability (IOS) gains for linear systems. For any Input-to-State Stable, strictly causal linear system the minimum OAG is equal to the minimum IOS-gain. Moreover, both quantities can be computed by solving a specific optimal control problem and by considering only periodic inputs. The result is valid for wide classes of linear systems (involving delay systems or systems described by PDEs). The characterization of the minimum IOS-gain is important because it allows the non-conservative computation of the IOS-gains, which can be used in a small-gain analysis. The paper also presents a number of cases for finite-dimensional linear systems, where exact computation of the minimum IOS gain can be performed.

更新日期：2020-01-22
• arXiv.cs.SY Pub Date : 2020-01-18
Rodrigo Aldana-López; David Gómez-Gutiérrez; Marco Tulio Angulo; Michael Defoort

Algorithms having uniform convergence with respect to their initial condition (i.e., with fixed-time stability) are receiving increasing attention for solving control and observer design problems under time constraints. However, we still lack a general methodology to design these algorithms for high-order perturbed systems when we additionally need to impose a user-defined upper-bound on their settling time, especially for systems with perturbations. Here, we fill this gap by introducing a methodology to redesign a class of asymptotically, finite- and fixed-time stable systems into non-autonomous fixed-time stable systems with a user-defined upper-bound on their settling time. Our methodology redesigns a system by adding time-varying gains. However, contrary to existing methods where the time-varying gains tend to infinity as the origin is reached, we provide sufficient conditions to maintain bounded gains. We illustrate our methodology by building fixed-time online differentiators with user-defined upper-bound on their settling time and bounded gains.

更新日期：2020-01-22
• arXiv.cs.SY Pub Date : 2020-01-18
Cihan Emre Kement

Fine-grained energy usage data collected by Smart Meters (SM) is one of the key components of the smart grid (SG). While collection of this data enhances efficiency and flexibility of SG, it also poses a serious threat to the privacy of consumers. Through techniques such as nonintrusive appliance load monitoring (NALM), this data can be used to identify the appliances being used, and hence disclose the private life of the consumer. Various methods have been proposed in the literature to preserve the consumer privacy. This paper focuses on load shaping (LS) methods, which alters the consumption data by means of household amenities in order to ensure privacy. An overview of the privacy protection techniques, as well as heuristics of the LS methods, privacy measures, and household amenities used for privacy protection are presented in order to thoroughly analyze the effectiveness and applicability of these methods to smart grid systems. Finally, possible research directions related to privacy protection in smart grids are discussed.

更新日期：2020-01-22
• arXiv.cs.SY Pub Date : 2020-01-18
Yangdi Lyu; Prabhat Mishra

Assertions are widely used for functional validation as well as coverage analysis for both software and hardware designs. Assertions enable runtime error detection as well as faster localization of errors. While there is a vast literature on both software and hardware assertions for monitoring functional scenarios, there is limited effort in utilizing assertions to monitor System-on-Chip (SoC) security vulnerabilities. In this paper, we identify common SoC security vulnerabilities by analyzing the design. To monitor these vulnerabilities, we define several classes of assertions to enable runtime checking of security vulnerabilities. Our experimental results demonstrate that the security assertions generated by our proposed approach can detect all the inserted vulnerabilities while the functional assertions generated by state-of-the-art assertion generation techniques fail to detect most of them.

更新日期：2020-01-22
• arXiv.cs.SY Pub Date : 2020-01-19
Hamzeh Davarikia; Faycal Znidi; Masoud Barati

Controlled islanding, which splits the whole power system into islands, is an effective strategy against rolling blackout during severe disturbances. Finding the islanding solutions in a real-time manner is complicated because of the combinatorial explosion of the solution space occurs for a large power network. In this work, a computationally efficient controlled islanding algorithm is proposed that uses constrained spectral clustering while addressing the generator coherency problem. The objective function used in this controlled islanding algorithm is the minimal power-flow disruption. The sole constraint applied to this solution is related to generator coherency. An undirected edge-weighted graph is created based on absolute values of apparent power flow and constraints related to transmission line availability and coherent generator groups are included by altering the edge weights of the graph and using a subspace projection. Spectral clustering is then applied to the constrained solution subspace to find the islanding solution. The methodology is tested on an IEEE-39 test system with a fully dynamic model. Simulation results demonstrate the efficacy of our approach.

更新日期：2020-01-22
• arXiv.cs.SY Pub Date : 2020-01-19
Rui Oliveira; Oskar Ljungqvist; Pedro F. Lima; Bo Wahlberg

Maneuvering an articulated vehicle on narrow road stretches is often a challenging task for a human driver. Unless the vehicle is accurately steered, parts of the vehicle's bodies may exceed its assigned drive lane, resulting in an increased risk of collision with surrounding traffic. In this work, an optimization-based path-planning algorithm is proposed targeting on-road driving scenarios for articulated vehicles composed of a tractor and a trailer. To this end, we model the tractor-trailer vehicle in a road-aligned coordinate frame suited for on-road planning. Based on driving heuristics, a set of different optimization objectives is proposed, with the overall goal of designing a path planner that computes paths which minimize the off-track of the vehicle bodies swept area, while remaining on the road and avoiding collision with obstacles. The proposed optimization-based path-planning algorithm, together with the different optimization objectives, is evaluated and analyzed in simulations on a set of complicated and practically relevant on-road planning scenarios using the most challenging tractor-trailer dimensions.

更新日期：2020-01-22
• arXiv.cs.SY Pub Date : 2020-01-19
Yiwei QiuState Key Laboratory of Control and Simulation of Power Systems and Generation Equipment, Department of Electrical Engineering, Tsinghua University; Jin LinState Key Laboratory of Control and Simulation of Power Systems and Generation Equipment, Department of Electrical Engineering, Tsinghua University; Xiaoshuang ChenState Key Laboratory of Control and Simulation of Power Systems and Generation Equipment, Department of Electrical Engineering, Tsinghua University; Feng LiuState Key Laboratory of Control and Simulation of Power Systems and Generation Equipment, Department of Electrical Engineering, Tsinghua University; Yonghua SongDepartment of Electrical and Computer Engineering, University of MacauState Key Laboratory of Control and Simulation of Power Systems and Generation Equipment, Department of Electrical Engineering, Tsinghua University

Continuous-time random disturbances (also called stochastic excitations) due to increasing renewable generation have an increasing impact on power system dynamics; However, except from the Monte Carlo simulation, most existing methods for quantifying this impact are intrusive, meaning they are not based on commercial simulation software and hence are difficult to use for power utility companies. To fill this gap, this paper proposes an efficient and nonintrusive method for quantifying uncertainty in dynamic power systems subject to stochastic excitations. First, the Gaussian or non-Gaussian stochastic excitations are modeled with an It\^{o} process as stochastic differential equations. Then, the It\^{o} process is spectrally represented by independent Gaussian random parameters, which enables the polynomial chaos expansion (PCE) of the system dynamic response to be calculated via an adaptive sparse probabilistic collocation method. Finally, the probability distribution and the high-order moments of the system dynamic response and performance index are accurately and efficiently quantified. The proposed nonintrusive method is based on commercial simulation software such as PSS/E with carefully designed input signals, which ensures ease of use for power utility companies. The proposed method is validated via case studies of IEEE 39-bus and 118-bus test systems.

更新日期：2020-01-22
• arXiv.cs.SY Pub Date : 2020-01-19
Wayes Tushar; Tapan K. Saha; Chau Yuen; David Smith; H. Vincent Poor

Peer-to-peer trading is a next-generation energy management technique that economically benefits proactive consumers (prosumers) transacting their energy as goods and services. At the same time, peer-to-peer energy trading is also expected to help the grid by reducing peak demand, lowering reserve requirements, and curtailing network loss. However, large-scale deployment of peer-to-peer trading in electricity networks poses a number of challenges in modeling transactions in both the virtual and physical layers of the network. As such, this article provides a comprehensive review of the state-of-the-art in research on peer-to-peer energy trading techniques. By doing so, we provide an overview of the key features of peer-to-peer trading and its benefits of relevance to the grid and prosumers. Then, we systematically classify the existing research in terms of the challenges that the studies address in the virtual and the physical layers. We then further identify and discuss those technical approaches that have been extensively used to address the challenges in peer-to-peer transactions. Finally, the paper is concluded with potential future research directions.

更新日期：2020-01-22
• arXiv.cs.SY Pub Date : 2020-01-20
Indu Yadav; Ankur A. Kulkarni; Abhay Karandikar

To address the exponentially increasing data rate demands of end users, necessitates efficient spectrum allocation among co-existing operators in licensed and unlicensed spectrum bands to cater to the temporal and spatial variations of traffic in the wireless network. In this paper, we address the spectrum allocation problem among non-cooperative operators via auctions. The classical Vickrey-Clarke-Groves (VCG) approach provides the framework for a strategy-proof and social welfare maximizing auction at high computational complexity, which makes it infeasible for practical implementation. We propose sealed bid auction mechanisms for spectrum allocation which are computationally tractable and hence applicable for allocating spectrum by performing auctions in short durations as per the dynamic load variations of the network. We establish that the proposed algorithm is strategy-proof for uniform demand. Furthermore, for non-uniform demand we propose an algorithm that satisfies weak strategy-proofness. We also consider non-linear increase in the marginal valuations with demand. Simulation results are presented to exhibit the performance comparison of the proposed algorithms with VCG and other existing mechanisms.

更新日期：2020-01-22
• arXiv.cs.SY Pub Date : 2020-01-20
Aritra Mitra; John A. Richards; Saurabh Bagchi; Shreyas Sundaram

We study the problem of designing a distributed observer for an LTI system over a time-varying communication graph. The limited existing work on this topic imposes various restrictions either on the observation model or on the sequence of communication graphs. In contrast, we propose a single-time-scale distributed observer that works under mild assumptions. Specifically, our communication model only requires strong-connectivity to be preserved over non-overlapping, contiguous intervals that are even allowed to grow unbounded over time. We show that under suitable conditions that bound the growth of such intervals, joint observability is sufficient to track the state of any discrete-time LTI system exponentially fast, at any desired rate. In fact, we also establish finite-time convergence based on our approach. Finally, we develop a variant of our algorithm that is provably robust to worst-case adversarial attacks, provided the sequence of graphs is sufficiently connected over time. The key to our approach is the notion of a "freshness-index" that keeps track of the age-of-information being diffused across the network. Such indices enable nodes to reject stale estimates of the state, and, in turn, contribute to stability of the error dynamics.

更新日期：2020-01-22
• arXiv.cs.SY Pub Date : 2020-01-20
Aritra Mitra; Faiq Ghawash; Shreyas Sundaram; Waseem Abbas

We address the problem of distributed state estimation of a linear dynamical process in an attack-prone environment. Recent attempts to solve this problem impose stringent redundancy requirements on the measurement and communication resources of the network. In this paper, we take a step towards alleviating such strict requirements by exploring two complementary directions: (i) making a small subset of the nodes immune to attacks, or "trusted", and (ii) incorporating diversity into the network. We define graph-theoretic constructs that formally capture the notions of redundancy, diversity, and trust. Based on these constructs, we develop a resilient estimation algorithm and demonstrate that even relatively sparse networks that either exhibit node-diversity, or contain a small subset of trusted nodes, can be just as resilient to adversarial attacks as more dense networks. Finally, given a finite budget for network design, we focus on characterizing the complexity of (i) selecting a set of trusted nodes, and (ii) allocating diversity, so as to achieve a desired level of robustness. We establish that, unfortunately, each of these problems is NP-complete.

更新日期：2020-01-22
• arXiv.cs.SY Pub Date : 2020-01-20
Mohit Srinivasan; Matthew Abate; Gustav Nilsson; Samuel Coogan

Safety requirements in dynamical systems are commonly enforced with set invariance constraints over a safe region of the state space. Control barrier functions, which are Lyapunov-like functions for guaranteeing set invariance, are an effective tool to enforce such constraints and guarantee safety when the system is represented as a point in the state space. In this paper, we introduce extent-compatible control barrier functions as a tool to enforce safety for the system including its volume (extent) in the physical world. In order to implement the extent-compatible control barrier functions framework, a sum-of-squares based optimization program is proposed. Since sum-of-squares programs can be computationally prohibitive, we additionally introduce a sampling based method in order to retain the computational advantage of a traditional barrier function based quadratic program controller. We prove that the proposed sampling based controller retains the guarantee for safety. Simulation and robotic implementation results are also provided.

更新日期：2020-01-22
• arXiv.cs.SY Pub Date : 2020-01-20
Yuxiao Chen; Sumanth Dathathri; Tung Phan-Minh; Richard M. Murray

There is a growing interest in building autonomous systems that interact with complex environments. The difficulty associated with obtaining an accurate model for such environments poses a challenge to the task of assessing and guaranteeing the system's performance. We present a data-driven solution that allows for a system to be evaluated for specification conformance without an accurate model of the environment. Our approach involves learning a conservative reactive bound of the environment's behavior using data and specification of the system's desired behavior. First, the approach begins by learning a conservative reactive bound on the environment's actions that captures its possible behaviors with high probability. This bound is then used to assist verification, and if the verification fails under this bound, the algorithm returns counter-examples to show how failure occurs and then uses these to refine the bound. We demonstrate the applicability of the approach through two case-studies: i) verifying controllers for a toy multi-robot system, and ii) verifying an instance of human-robot interaction during a lane-change maneuver given real-world human driving data.

更新日期：2020-01-22
• arXiv.cs.SY Pub Date : 2020-01-20
Andrew Mackey; Petros Spachos; Konstantinos N. Plataniotis

Urban centers and dense populations are expanding, hence, there is a growing demand for novel applications to aid in planning and optimization. In this work, a smart parking system that operates both indoor and outdoor is introduced. The system is based on Bluetooth Low Energy (BLE) beacons and uses particle filtering to improve its accuracy. Through simple BLE connectivity with smartphones, an intuitive parking system is designed and deployed. The proposed system pairs each spot with a unique BLE beacon, providing users with guidance to free parking spaces and a secure and automated payment scheme based on real-time usage of the parking space. Three sets of experiments were conducted to examine different aspects of the system. A particle filter is implemented in order to increase the system performance and improve the credence of the results. Through extensive experimentation in both indoor and outdoor parking spaces, the system was able to correctly predict which spot the user has parked in, as well as estimate the distance of the user from the beacon.

更新日期：2020-01-22
• arXiv.cs.SY Pub Date : 2020-01-21
Colin Summers; Kendall Lowrey; Aravind Rajeswaran; Siddhartha Srinivasa; Emanuel Todorov

We introduce Lyceum, a high-performance computational ecosystem for robot learning. Lyceum is built on top of the Julia programming language and the MuJoCo physics simulator, combining the ease-of-use of a high-level programming language with the performance of native C. In addition, Lyceum has a straightforward API to support parallel computation across multiple cores and machines. Overall, depending on the complexity of the environment, Lyceum is 5-30x faster compared to other popular abstractions like OpenAI's Gym and DeepMind's dm-control. This substantially reduces training time for various reinforcement learning algorithms; and is also fast enough to support real-time model predictive control through MuJoCo. The code, tutorials, and demonstration videos can be found at: www.lyceum.ml.

更新日期：2020-01-22
• arXiv.cs.SY Pub Date : 2020-01-21

Optimal tracking of continuous time nonlinear systems has been extensively studied in literature. However, in several applications, absence of knowledge about system dynamics poses a severe challenge to solving the optimal tracking problem. This has found growing attention among researchers recently, and integral reinforcement learning (IRL)-based method augmented with actor neural network (NN) have been deployed to this end. However, very few studies have been directed to model-free $H_{\infty}$ optimal tracking control that helps in attenuating the effect of disturbances on the system performance without any prior knowledge about system dynamics. To this end a recursive least square-based parameter update was recently proposed. However, gradient descent-based parameter update scheme is more sensitive to real-time variation in plant dynamics. And experience replay (ER) technique has been shown to improve the convergence of NN weights by utilizing past observations iteratively. Motivated by these, this paper presents a novel parameter update law based on variable gain gradient descent and experience replay technique for tuning the weights of critic, actor and disturbance NNs.

更新日期：2020-01-22
• arXiv.cs.SY Pub Date : 2020-01-21

A fixed-order set-valued observer is presented for linear parameter-varying systems with bounded-norm noise and under completely unknown attack signals, which simultaneously finds bounded sets of states and unknown inputs that include the true state and inputs. The proposed observer can be designed using semidefinite programming with LMI constraints and is optimal in the minimum \mathcal{H}_{\infty}-norm sense. We show that the strong detectability of each constituent linear time-invariant system is a necessary condition for the existence of such an observer, as well as the boundedness of set-valued estimates. Furthermore, sufficient conditions are provided for the upper bounds of the estimation errors to converge to steady state values and finally, the results of such a set-valued observer are exhibited through an illustrative example.

更新日期：2020-01-22
• arXiv.cs.SY Pub Date : 2020-01-21

A simultaneous mode, input and state set-valued observer is proposed for hidden mode switched linear systems with bounded-norm noise and unknown input signals. The observer consists of two constituents: (i) a bank of mode-matched observers and (ii) a mode estimator. Each mode-matched observer recursively outputs the mode-matched sets of compatible states and unknown inputs, while the mode estimator eliminates incompatible modes, using a residual-based criterion. Then, the estimated sets of states and unknown inputs are the union of the mode-matched estimates over all compatible modes. Moreover, sufficient conditions to guarantee the elimination of all false modes are provided and the effectiveness of our approach is exhibited using an illustrative example.

更新日期：2020-01-22
• arXiv.cs.SY Pub Date : 2020-01-21
Lukas P. Fröhlich; Edgar D. Klenske; Christian G. Daniel; Melanie N. Zeilinger

Bayesian Optimization (BO) is an effective method for optimizing expensive-to-evaluate black-box functions with a wide range of applications for example in robotics, system design and parameter optimization. However, scaling BO to problems with large input dimensions (>10) remains an open challenge. In this paper, we propose to leverage results from optimal control to scale BO to higher dimensional control tasks and to reduce the need for manually selecting the optimization domain. The contributions of this paper are twofold: 1) We show how we can make use of a learned dynamics model in combination with a model-based controller to simplify the BO problem by focusing onto the most relevant regions of the optimization domain. 2) Based on (1) we present a method to find an embedding in parameter space that reduces the effective dimensionality of the optimization problem. To evaluate the effectiveness of the proposed approach, we present an experimental evaluation on real hardware, as well as simulated tasks including a 48-dimensional policy for a quadcopter.

更新日期：2020-01-22
• arXiv.cs.SY Pub Date : 2020-01-21
Roope Sarala; Jussi Kiljander

As more and more energy is produced from renewable energy sources (RES), the challenge for balancing production and consumption is being shifted to consumers instead of the power grid. This requires new and intelligent ways of flexibility management at individual building and district levels. To this end, this paper presents a model based optimal control (MPC) algorithm embedded with deep neural network for day-ahead consumption and production forecasting. The algorithm is used to optimize a medium-sized grocery store energy consumption located in Finland. System was tested in a simulation tool utilising real-life power measurements from the grocery store. We report a $8.4\%$ reduction in daily peak loads with flexibility provided by a $20$ kWh battery. On the other hand, a significant benefit was not seen in trying to optimize with respect to the energy spot price. We conclude that our approach is able to significantly reduce peak loads in a grocery store without additional operational costs.

更新日期：2020-01-22
• arXiv.cs.SY Pub Date : 2020-01-21
Fangzhou Liu; Shaoxuan Cui; Xianwei Li; Martin Buss

Networked epidemic models have been widely adopted to describe propagation phenomena. The endemic equilibrium of these models is of great significance in the field of viral marketing, innovation dissemination, and information diffusion. However, its stability conditions have not been fully explored. In this paper we study the stability of the endemic equilibrium of a networked Susceptible-Infected-Susceptible (SIS) epidemic model with heterogeneous transition rates in a discrete-time manner. We show that the endemic equilibrium, if it exists, is asymptotically stable for any nontrivial initial condition. Under mild assumptions on initial conditions, we further prove that during the spreading process there exists no overshoot with respect to the endemic equilibrium. Finally, we conduct numerical experiments on real-world networks to demonstrate our results.

更新日期：2020-01-22
• arXiv.cs.SY Pub Date : 2019-01-24
Bo Pang; Zhong-Ping Jiang

This paper studies the infinite-horizon adaptive optimal control of continuous-time linear periodic (CTLP) systems. A novel value iteration (VI) based off-policy ADP algorithm is proposed for a general class of CTLP systems, so that approximate optimal solutions can be obtained directly from the collected data, without the exact knowledge of system dynamics. Under mild conditions, the proofs on uniform convergence of the proposed algorithm to the optimal solutions are given for both the model-based and model-free cases. The VI-based ADP algorithm is able to find suboptimal controllers without assuming the knowledge of an initial stabilizing controller. Application to the optimal control of a triple inverted pendulum subjected to a periodically varying load demonstrates the feasibility and effectiveness of the proposed method.

更新日期：2020-01-22
• arXiv.cs.SY Pub Date : 2019-09-29
Giuseppe L'Erario; Luca Fiorio; Gabriele Nava; Fabio Bergonti; Hosameldin Awadalla Omer Mohamed; Silvio Traversaro; Daniele Pucci

The paper contributes towards the modeling, identification, and control of model jet engines. We propose a nonlinear, second order model in order to capture the model jet engines governing dynamics. The model structure is identified by applying sparse identification of nonlinear dynamics, and then the parameters of the model are found via gray-box identification procedures. Once the model has been identified, we approached the control of the model jet engine by designing two control laws. The first one is based on the classical Feedback Linearization technique while the second one on the Sliding Mode control. The overall methodology has been verified by modeling, identifying and controlling two model jet engines, i.e. P100-RX and P220-RXi developed by JetCat, which provide a maximum thrust of 100 N and 220 N, respectively.

更新日期：2020-01-22
• arXiv.cs.SY Pub Date : 2019-10-10
Michael H. Lim; Claire J. Tomlin; Zachary N. Sunberg

Partially observable Markov decision processes (POMDPs) with continuous state and observation spaces have powerful flexibility for representing real-world decision and control problems but are notoriously difficult to solve. Recent online sampling-based algorithms that use observation likelihood weighting have shown unprecedented effectiveness in domains with continuous observation spaces. However there has been no formal theoretical justification for this technique. This work offers such a justification, proving that a simplified algorithm, partially observable weighted sparse sampling (POWSS), will estimate Q-values accurately with high probability and can be made to perform arbitrarily near the optimal solution by increasing computational power.

更新日期：2020-01-22
• arXiv.cs.SY Pub Date : 2019-11-30
Andrei Bogatyrev

The best uniform rational approximation of the \emph{sign} function on two intervals separated by zero was explicitly solved by E.I. Zolotar\"ev in 1877. This optimization problem is the initial step in the staircase of the so called approximation problems for multiband filters which are of great importance for electrical engineering. We show that known in the literature optimality criterion for this problem may be contradictory since it does not take into account the projective invariance of the problem. We propose a new consistently projective formulation of this problem and give a constructive optimality criterion for it.

更新日期：2020-01-22
• arXiv.cs.SY Pub Date : 2020-01-15
Krzysztof Łakomy; Radosław Patelski; Dariusz Pazderski

Proper operation of the Active Disturbance Rejection (ADR) controller requires a precise determination of the so-called total disturbance affecting the considered dynamical system, usually estimated by the Extended State Observer (ESO). The observation quality of total disturbance has a significant impact on the control error values, making room for a potential improvement of control system performance using different structures of ESO. In this article, we provide a quantitative comparison between the Luenberger and Astolfi/Marconi (AM) observers designed for three different extended state representations and utilized in the trajectory tracking ADR controller designed for a mechanical system. Included results were obtained in the simple simulation case, followed by the experimental validation on the main axis of a telescope mount.

更新日期：2020-01-22
• arXiv.cs.SY Pub Date : 2020-01-15
Shiva Geraei; Saeed Hasanpour Aghdam

The reliability of power semiconductor switches is important when considering their vital role in power electronic converters for downhole subsea applications. Respect to technology advancements in material sciences, power MOSFETs with wide band gap materials have been proposed such as silicon carbide (SiC) and gallium nitride (GaN) as an alternative to existing silicon (Si) based MOSFETs and IGBTs. However, reliability analysis should be performed before substituting SiC-MOSFETs in the place of existing Si-MOSFETs and IGBTs. Due to costly equipment of experimental test setup for accelerated life test, a good reliable and precise simulation-based test bench should be used to test the life test procedure before implementing actual hardware. Therefore, this paper introduces a power cycle (PC) test bench for accelerated life testing for reliability assessment of SiC-MOSFET in harsh offshore environment. The introduced test bench is a simulation-based of power switch in SimScape and LTspice and has been validated with datasheet of 1.2 kV SiC-MOSFET, CAS300M12BM2 by CREE. Preliminary hardware circuits are also shown for further experimental tests. The captured data from the Device-Under-Test (DUT) in different ambient temperatures are envisioned and provide critical information about the failure mechanisms and lifetime characteristics of power devices. The provided lifetime characteristics data of SiC-MOSFET can be used to statistically estimate the Remaining-Useful-Lifetime (RUL) of component in a real application such as downhole motor drives.

更新日期：2020-01-17
• arXiv.cs.SY Pub Date : 2020-01-16
Chong Xiao Wang; Yang Song; Wee Peng Tay

Each agent in a network makes a local observation that is linearly related to a set of public and private parameters. The agents send their observations to a fusion center to allow it to estimate the public parameters. To prevent leakage of the private parameters, each agent first sanitizes its local observation using a local privacy mechanism before transmitting it to the fusion center. We investigate the utility-privacy trade-off in terms of the Cram\'er-Rao lower bounds for estimating the public and private parameters. We study the class of privacy mechanisms given by linear compression and noise perturbation, and derive necessary and sufficient conditions for achieving arbitrarily strong utility-privacy trade-off in a decentralized agent network for both the cases where prior information is available and unavailable, respectively. We also provide a method to find the maximum estimation privacy achievable without compromising the utility and propose an alternating algorithm to optimize the utility-privacy trade-off in the case where arbitrarily strong utility-privacy trade-off is not achievable.

更新日期：2020-01-17
• arXiv.cs.SY Pub Date : 2020-01-16
Deepanshu Vasal; Rajesh K Mishra; Sriram Vishwanath

In this paper, we present a sequential decomposition algorithm to compute graphon mean field equilibrium (GMFE) of dynamic graphon mean field game (GMFG). We consider a large population of players sequentially making strategic decisions where the actions of each player affect their neighbors which is captured in a graph, generated by a known graphon. Each player observes a private state and also a common information as a graphon mean-field population state which represents the empirical networked distribution of other players' types. We consider non-stationary population state dynamics and present a novel backward recursive algorithm to compute GMFE that depend on both, a player's private type, and the current (dynamic) population state determined through the graphon. Each step in this algorithm consists of solving a fixed-point equation. We provide conditions on model parameters for which there exists such a GMFE. Using this algorithm, we obtain the GMFE for a specific security setup in cyber physical systems for different graphons that capture the interactions between the nodes in the system.

更新日期：2020-01-17
• arXiv.cs.SY Pub Date : 2020-01-16
Haibo Wang; Zaichen Zhang; Bingcheng Zhu; Jian Dang; Liang Wu; Lei Wang; Kehan Zhang; Yidi Zhang

It is difficult for free space optical communication to be applied in mobile communication due to the obstruction of obstacles in the environment, which is expected to be solved by reconfigurable intelligent surface technology. The reconfigurable intelligent surface is a new type of digital coding meta-materials, which can reflect, compute and program electromagnetic and optical waves in real time. We purpose a controllable multi-branch wireless optical communication system based on the optical reconfigurable intelligent surface technology. By setting up multiple optical reconfigurable intelligent surface in the environment, multiple artificial channels are built to improve system performance and to reduce the outage probability. Three factors affecting channel coefficients are investigated in this paper, which are beam jitter, jitter of the reconfigurable intelligent surface and the probability of obstruction. Based on the model, we derive the closed-form probability density function of channel coefficients, the asymptotic system's average bit error rate and outage probability for systems with single and multiple branches. It is revealed that the probability density function contains an impulse function, which causes irreducible error rate and outage probability floors. Numerical results indicate that compared with free-space optical communication systems with single direct path, the performance of the multi-branch system is improved and the outage probability is reduced.

更新日期：2020-01-17
• arXiv.cs.SY Pub Date : 2020-01-16
Andrea Cristofaro; Francesco Ferrante

The problem of stabilization of a system of coupled PDEs of the forth-order by means of boundary control is investigated. The considered setup arises from the classical Euler-Bernoulli beam model, and constitutes a generalization of flexible mechanical systems. A linear feedback controller is proposed, and using an abstract formulation based on operator semigroup theory, we are able to prove the well-posedness and the stability of the closed-loop system. The performances of the proposed controller are illustrated by means of numerical simulations.

更新日期：2020-01-17
• arXiv.cs.SY Pub Date : 2020-01-16
Eleftherios Vlahakis; George Halikias

In this paper, network of agents with identical dynamics is considered. The agents are assumed to be fed by self and neighboring output measurements, while the states are not available for measuring. Viewing distributed estimation as dual to the distributed LQR problem, a distributed observer is proposed by exploiting two complementary distributed LQR methods. The first consists of a bottom-up approach in which optimal interactions between self-stabilizing agents are defined so as to minimize an upper bound of the global LQR criterion. In the second (top-down) approach, the centralized optimal LQR controller is approximated by a distributed control scheme whose stability is guaranteed by the stability margins of LQR control. In this paper, distributed observer which minimizes an upper bound of a deterministic performance criterion, is proposed by solving a dual LQR problem using bottom-up approach. The cost function is defined by considering minimum-energy estimation theory where the weighting matrices have deterministic interpretation. The presented results are useful for designing optimal or near-optimal distributed control/estimation schemes.

更新日期：2020-01-17
• arXiv.cs.SY Pub Date : 2020-01-16
Yichen Zhang; Chen Chen; Guodong Liu; Tianqi Hong; Feng Qiu

In this paper, we introduce a deep learning aided constraint encoding method to tackle the frequency-constraint microgrid scheduling problem. The nonlinear function between system operating condition and frequency nadir is approximated by using a neural network, which admits an exact mixed-integer formulation (MIP). This formulation is then integrated with the scheduling problem to encode the frequency constraint. With the stronger representation power of the neural network, the resulting commands can ensure adequate frequency response in a realistic setting in addition to islanding success. The proposed method is validated on a modified 33-node system. Successful islanding with a secure response is simulated under the scheduled commands using a detailed three-phase model in Simulink. The advantages of our model are particularly remarkable when the inertia emulation functions from wind turbine generators are considered.

更新日期：2020-01-17
• arXiv.cs.SY Pub Date : 2020-01-16
Thomas Gellrich; Clara Sester; Max Okraschevski; Stefan Schwab; Hans-Joerg Bauer; Soeren Hohmann

For the thermal control of electronic components in aerospace, automotive or server systems, the heat sink is often located far from the heat sources. Therefore, heat transport systems are necessary to cool the electronic components effectively. Loop heat pipes (LHPs) are such heat transport systems, which use evaporation and condensation to reach a higher heat transfer coefficient than with sole heat conduction. The operating temperature of the LHP governs the temperature of the electronic components, but depends on the amount of dissipated heat and the temperature of the heat sink. For this reason, a control heater on the LHP provides the ability to control the operating temperature at a fixed setpoint temperature. For the model-based control design of the control heater controller, the current LHP state-space model in the literature focuses on the setpoint response without modeling the fluid's dynamics. However, the fluid's dynamics determine the disturbance behavior of the LHP. Therefore, the fluid's dynamics are incorporated into a new LHP state-space model, which is not only able to simulate the LHP behavior under disturbance changes, but is also used for the model-based design of a robust nonlinear controller, which achieves an improved control performance compared to the nonlinear controller based on the previous LHP state-space model.

更新日期：2020-01-17
• arXiv.cs.SY Pub Date : 2020-01-16
Benjamin Matthiss; Arghavan Momenifarahani; Jann Binder

With the increasing penetration of renewable resources in the distribution grid, the demand for alternatives to grid reinforcement measures rises. One possible solution is the use of battery systems to balance the power flow at crucial locations in the grid. Hereby the optimal location and size of the system has to be determined in regards of investment and grid stabilizing effect. In this paper the optimal placement and sizing of battery storage systems for grid stabilization in a small distribution grid in southern Germany with high PV- penetration is investigated and compared to a grid heuristical reinforcement strategy.

更新日期：2020-01-17
• arXiv.cs.SY Pub Date : 2020-01-16
Gurcan Comert; Stacey Franklin Jones

This paper discusses the real-time prediction of queue lengths from probe vehicles for the Bunch arrival headways at an isolated intersection for undersaturated conditions. The paper incorporates the bunching effect of the traffic into the evaluation of the accuracy of the predictions as a function of proportion of probe vehicles to entire vehicle population. Formulations are presented for predicting the expected queue length and its variance based on Negative Exponential and Bunched Exponential vehicle headways. Numerical results for both vehicle headway types are documented to show how prediction errors behave by the volume to capacity ratio and probe proportions. It is found that the Poisson arrivals generate conservative confidence intervals and demand higher probe proportions compared to Bunched Exponential headways at the same arrival rate and probe proportion.

更新日期：2020-01-17
• arXiv.cs.SY Pub Date : 2020-01-16
Benjamin Montavon; Martin Peterek; Robert H. Schmitt

Metrology assisted assembly systems constitute cyber physical production systems relying on in-process sensor data as input to model-based control loops. These range from local, physical control loops, e.g. for robots to closed-loop product lifecycles including quality management. The variety and amount of involved sensors, actors and data sources require a distinct infrastructure to ensure efficient, reliable and secure implementation. Within the paradigm of Internet of Production a reference architecture for such an infrastructure is established by four layers: Raw data (1), provisioning of proprietary systems (2), data aggregation and brokering (3) and decision support (4). In modern metrology assisted assembly systems, a virtual reference frame is constituted by one or multiple predominantly optical Large-Scale Metrology instruments, e.g. laser trackers, indoor GPS or multilateration based on ultra wideband communication. An economically efficient implementation of the reference frame can be achieved using cooperative data fusion, both by increasing the operative volume with existing systems and by optimizing the utilization of highly precise and therefor typically cost-intensive instruments. Herewith a harmonization is required as well from a physical perspective as in terms of communication interfaces to the raw data provided by the individual instruments. The authors propose a model-based approach to obtain a protocol-agnostic interface description, viewing a Large-Scale Metrology instrument as an abstract object oriented system consisting of one or multiple base units and mobile entities.

更新日期：2020-01-17
• arXiv.cs.SY Pub Date : 2019-03-13
Xiaoqiang Ren; Yilin Mo; Jie Chen; Karl H. Johansson

This paper studies static state estimation in multi-sensor settings, with a caveat that an unknown subset of the sensors are compromised by an adversary, whose measurements can be manipulated arbitrarily. The attacker is able to compromise $q$ out of $m$ sensors. A new performance metric, which quantifies the asymptotic decay rate for the probability of having an estimation error larger than $\delta$, is proposed. We develop an optimal estimator for the new performance metric with a fixed $\delta$, which is the Chebyshev center of a union of ellipsoids. We further provide an estimator that is optimal for every $\delta$, for the special case where the sensors are homogeneous. Numerical examples are given to elaborate the results.

更新日期：2020-01-17
• arXiv.cs.SY Pub Date : 2019-05-04
Masakazu Sano

Stochastic quantization in physics has been considered to provide a path integral representation of a probability distribution for Ito processes. It has been indicated that the stochastic quantization can involve a potential term, if the Ito process is limited to Langevin equation. In this paper, in order to apply the stochastic quantization to engineering problems, we propose a novel method to incorporate a potential term into stochastic quantization of the general Ito process. This method indicates that weighted distribution gives rise to the potential term for the discrete-time path integral and preserves the role of the path integral as the probability distribution, without making any assumptions on the drift term. A second order approximation on the stochastic fluctuations for the path integral gives difference equations which represent the time evolution of expectation value and covariance matrix for the stochastic processes. The difference equations explicitly derive Extended Kalman Filter and models on the constrained Ito processes by the identification of the potential function with a penalty or barrier function. The numerical simulations of the constrained stochastic systems show that the potential term can constrain the nonlinear dynamics towards a minimum or a decreasing direction of the potential function.

更新日期：2020-01-17
• arXiv.cs.SY Pub Date : 2019-08-02
Karthik R. Ramaswamy; Paul M. J. Van den Hof

The identification of local modules in dynamic networks with known topology has recently been addressed by formulating conditions for arriving at consistent estimates of the module dynamics, under the assumption of having disturbances that are uncorrelated over the different nodes. The conditions typically reflect the selection of a set of node signals that are taken as predictor inputs in a MISO identification setup. In this paper an extension is made to arrive at an identification setup for the situation that process noises on the different node signals can be correlated with each other. In this situation the local module may need to be embedded in a MIMO identification setup for arriving at a consistent estimate with maximum likelihood properties. This requires the proper treatment of confounding variables. The result is a set of algorithms that, based on the given network topology and disturbance correlation structure, selects an appropriate set of node signals as predictor inputs and outputs in a MISO or MIMO identification setup. Three algorithms are presented that differ in their approach of selecting measured node signals. Either a maximum or a minimum number of measured node signals can be considered, as well as a preselected set of measured nodes.

更新日期：2020-01-17
• arXiv.cs.SY Pub Date : 2020-01-15
Zhenya Zhang; Paolo Arcaini; Ichiro Hasuo

Falsification of hybrid systems is attracting ever-growing attention in quality assurance of Cyber-Physical Systems (CPS) as a practical alternative to exhaustive formal verification. In falsification, one searches for a falsifying input that drives a given black-box model to output an undesired signal. In this paper, we identify input constraints---such as the constraint "the throttle and brake pedals should not pressed simultaneously" for an automotive powertrain model---as a key factor for the practical value of falsification methods. We propose three approaches for systematically addressing input constraints in optimization-based falsification, two among which come from the lexicographic method studied in the context of constrained multi-objective optimization. Our experiments show the approaches' effectiveness.

更新日期：2020-01-16
• arXiv.cs.SY Pub Date : 2020-01-15
Giulio Reina; Antonio Leanza; Arcangelo Messina

The extent of vibrations experienced by a vehicle driving over natural terrain defines its ride quality. Generally, surface irregularities, ranging from single discontinuities to random variations of the elevation profile, act as a major source of excitation that induces vibrations in the vehicle body through the tire-soil interaction and suspension system. Therefore, the ride response of off-road vehicles is tightly connected with the ground properties. The objective of this research is to develop a model-based observer that estimates automatically terrain parameters using available onboard sensors. Two acceleration signals, one coming from the vehicle body and one from the wheel suspension, are fed into a dynamic vehicle model that takes into account tire/terrain interaction to estimate ground properties. To solve the resulting nonlinear simultaneous state and parameter estimation problem, the cubature Kalman filter is used, which is shown to outperform the standard extended Kalman filter in terms of accuracy and stability. An extensive set of simulation tests is presented to assess the performance of the proposed estimator under various surface roughness and deformability conditions. Results show the potential of the proposed observer to estimate automatically terrain properties during operations that could be implemented onboard of a general family of intelligent vehicles, ranging from off-road high-speed passenger cars to lightweight and low-speed planetary rovers.

更新日期：2020-01-16
• arXiv.cs.SY Pub Date : 2020-01-15
Dhruv Khandelwal; Maarten Schoukens; Roland Tóth

Model structure and complexity selection remains a challenging problem in system identification, especially for parametric non-linear models. Many Evolutionary Algorithm (EA) based methods have been proposed in the literature for estimating model structure and complexity. In most cases, the proposed methods are devised for estimating structure and complexity within a specified model class and hence these methods do not extend to other model structures without significant changes. In this paper, we propose a Tree Adjoining Grammar (TAG) for stochastic parametric models. TAGs can be used to generate models in an EA framework while imposing desirable structural constraints and incorporating prior knowledge. In this paper, we propose a TAG that can systematically generate models ranging from FIRs to polynomial NARMAX models. Furthermore, we demonstrate that TAGs can be easily extended to more general model classes, such as the non-linear Box-Jenkins model class, enabling the realization of flexible and automatic model structure and complexity selection via EA.

更新日期：2020-01-16
• arXiv.cs.SY Pub Date : 2020-01-15
Joseph M. Lukens; Nicholas Lagakos; Victor Kaybulkin; Christopher J. Vizas; Daniel J. King

We design, test, and analyze fiber-optic voltage sensors based on optical reflection from a piezoelectric transducer. By controlling the physical dimensions of the device, we can tune the frequency of its natural resonance to achieve a desired sensitivity and bandwidth combination. In this work, we fully characterize sensors designed with a 2 kHz characteristic resonance, experimentally verifying a readily usable frequency range from approximately 10 Hz to 3 kHz. Spectral noise measurements indicate detectable voltage levels down to 300 mV rms at 60 Hz, along with a full-scale dynamic range of 60 dB, limited currently by the readout electronics, not the inherent performance of the transducer in the sensor. Additionally, we demonstrate a digital signal processing approach to equalize the measured frequency response, enabling accurate retrieval of short-pulse inputs. Our results suggest the value and applicability of intensity-modulated fiber-optic voltage sensors for measuring both steady-state waveforms and broadband transients which, coupled with the straightforward and compact design of the sensors, should make them effective tools in electric grid monitoring.

更新日期：2020-01-16
• arXiv.cs.SY Pub Date : 2020-01-15
Tobias Schoels; Per Rutquist; Luigi Palmieri; Andrea Zanelli; Kai O. Arras; Moritz Diehl

Robots have been operating in dynamic environments and shared workspaces for decades. Most optimization based motion planning methods, however, do not consider the movement of other agents, e.g. humans or other robots, and therefore do not guarantee collision avoidance in such scenarios. This paper builds upon the Convex Inner ApprOximation (CIAO) method and proposes a motion planning algorithm that guarantees collision avoidance in predictable dynamic environments. Furthermore it generalizes CIAO's free region concept to arbitrary norms and proposes a cost function to approximate time-optimal motion planning. The proposed method, CIAO$^\star$, finds kinodynamically feasible and collision free trajectories for constrained robots using a \ac*{mpc} framework and accounts for the predicted movement of other agents. The experimental evaluation shows that CIAO$^\star$ reaches close to time optimal behavior.

更新日期：2020-01-16
• arXiv.cs.SY Pub Date : 2018-09-30
David Métivier; Michael Chertkov

We pose an engineering challenge of controlling an Ensemble of Energy Devices via coordinated, implementation-light and randomized on/off switching as a problem in Non-Equilibrium Statistical Mechanics. We show that Mean Field Control} with nonlinear feedback on the cumulative consumption, assumed available to the aggregator via direct physical measurements of the energy flow, allows the ensemble to recover from its use in the Demand Response regime, i.e. transition to a statistical steady state, significantly faster than in the case of the fixed feedback. Moreover when the nonlinearity is sufficiently strong, one observes the phenomenon of "super-relaxation" -- where the total instantaneous energy consumption of the ensemble transitions to the steady state much faster than the underlying probability distribution of the devices over their state space, while also leaving almost no devices outside of the comfort zone.

更新日期：2020-01-16
• arXiv.cs.SY Pub Date : 2019-06-15
Yuanyuan Shi; Baosen Zhang

In this work, we study the interaction of strategic players in continuous action Cournot games with limited information feedback. Cournot game is the essential model for many socio-economic systems where players learn and compete. In addition, in many practical settings these players do not have full knowledge of the system or of each other. In this limited information setting, it becomes important to understand the dynamics and limiting behavior of the players. Specifically, we assume players follow strategies such that in hindsight their payoffs are not exceeded by any single deviating action. Given this no-regret guarantee, we prove that under standard assumptions, the players' joint action (both in the sense of time average and final iteration convergence) converges to the unique Nash equilibrium. In addition, our results naturally extend the existing regret analysis on time average convergence to obtain final iteration convergence rates. Together, our work presents significantly sharper and generalized convergence results, and shows how exploiting the game information feedback can influence the convergence rates.

更新日期：2020-01-16
• arXiv.cs.SY Pub Date : 2019-09-18
Tobias Schoels; Luigi Palmieri; Kai O. Arras; Moritz Diehl

Even though mobile robots have been around for decades, trajectory optimization and continuous time collision avoidance remains subject of active research. Existing methods trade off between path quality, computational complexity, and kinodynamic feasibility. This work approaches the problem using a model predictive control (MPC) framework, that is based on a novel convex inner approximation of the collision avoidance constraint. The proposed Convex Inner ApprOximation (CIAO) method finds kinodynamically feasible and continuous time collision free trajectories, in few iterations, typically one. For a feasible initialization, the approach is guaranteed to find a feasible solution, i.e. it preserves feasibility. Our experimental evaluation shows that CIAO outperforms state of the art baselines in terms of planning efficiency and path quality. Experiments on a robot with 12 states show that it also scales to high-dimensional systems. Furthermore real-world experiments demonstrate its capability of unifying trajectory optimization and tracking for safe motion planning in dynamic environments.

更新日期：2020-01-16
• arXiv.cs.SY Pub Date : 2020-01-13
Eric B. Jones; Eliot Kapit; Chin-Yao Chang; David Biagioni; Deepthi Vaidhynathan; Peter Graf; Wesley Jones

Using optimal phasor measurement unit placement as a prototypical problem, we assess the computational viability of the current generation D-Wave Systems 2000Q quantum annealer for power systems design problems. We reformulate minimum dominating set for the annealer hardware, solve the reformulation for a standard set of IEEE test systems, and benchmark solution quality and time to solution against the CPLEX Optimizer and simulated annealing. For some problem instances the 2000Q outpaces CPLEX. For instances where the 2000Q underperforms with respect to CPLEX and simulated annealing, we suggest hardware improvements for the next generation of quantum annealers.

更新日期：2020-01-15
• arXiv.cs.SY Pub Date : 2020-01-13
Shenyu Liu; Yinai Fan; Mohamed-Ali Belabbas

The problem of motion planning for affine control systems consists of designing control inputs that drive a system from a well-defined initial to final states in a desired amount of time. For control systems with drift, however, understanding which final states are reachable in a given time, or reciprocally the amount of time needed to reach a final state, is often the most difficult part of the problem. We address this issue in this paper and introduce a new method to solve motion planning problems for affine control systems, where the motion desired can have indefinite boundary conditions and the time required to perform the motion is free. The method extends on our earlier work on motion planning for systems without drift. A canonical example of parallel parking of a unicycle with constant linear velocity is provided in this paper to demonstrate our algorithm.

更新日期：2020-01-15
• arXiv.cs.SY Pub Date : 2020-01-14
Andrew Polar; Michael Poluektov

Adaptive filtering is a common approach to a system modeling by recursively adjusting model parameters. The algorithms for adaptive filtering have common concept but designed individually for each pre-selected model such as linear regression, Volterra series, kernel least mean squares or other. This article provides the new version of adaptive filtering algorithms capable of constructing models in a form of multiple sequential and parallel Urysohn operators. It is shown that multiple interconnected discrete Urysohn operators may in fact form Kolmogorov-Arnold representation for the generic mulitvariable continuous functions and that suggested approach allows to build such generic representation.

更新日期：2020-01-15
• arXiv.cs.SY Pub Date : 2020-01-14
Ryohei Oura; Ami Sakakibara; Toshimitsu Ushio

This letter proposes a novel reinforcement learning method for the synthesis of a control policy satisfying a control specification described by a linear temporal logic formula. We assume that the controlled system is modeled by a Markov decision process (MDP). We transform the specification to a limit-deterministic B\"uchi automaton (LDBA) with several accepting sets that accepts all infinite sequences satisfying the formula. The LDBA is augmented so that it explicitly records the previous visits to accepting sets. We take a product of the augmented LDBA and the MDP, based on which we define a reward function. The agent gets rewards whenever state transitions are in an accepting set that has not been visited for a certain number of steps. Consequently, sparsity of rewards is relaxed and optimal circulations among the accepting sets are learned. We show that the proposed method can learn an optimal policy when the discount factor is sufficiently close to one.

更新日期：2020-01-15
• arXiv.cs.SY Pub Date : 2020-01-14

Observability is a fundamental concept in system inference and estimation. This paper is focused on structural observability analysis of Cartesian product networks. Cartesian product networks emerge in variety of applications including in parallel and distributed systems. We provide a structural approach to extend the structural observability of the constituent networks (referred as the factor networks) to that of the Cartesian product network. The structural approach is based on graph theory and is generic. We introduce certain structures which are tightly related to structural observability of networks, namely parent Strongly-Connected-Component (parent SCC), parent node, and contractions. The results show that for particular type of networks (e.g. the networks containing contractions) the structural observability of the factor network can be recovered via Cartesian product. In other words, if one of the factor networks is structurally rank-deficient, using the other factor network containing a spanning cycle family, then the Cartesian product of the two nwtworks is structurally full-rank. We define certain network structures for structural observability recovery. On the other hand, we derive the number of observer nodes--the node whose state is measured by an output-- in the Cartesian product network based on the number of observer nodes in the factor networks. An example illustrates the graph-theoretic analysis in the paper.

更新日期：2020-01-15
• arXiv.cs.SY Pub Date : 2020-01-14
Astghik Hakobyan; Insoon Yang

In this paper, a risk-aware motion control scheme is considered for mobile robots to avoid randomly moving obstacles when the true probability distribution of uncertainty is unknown. We propose a novel model predictive control (MPC) method for limiting the risk of unsafety even when the true distribution of the obstacles' movements deviates, within an ambiguity set, from the empirical distribution obtained using a limited amount of sample data. By choosing the ambiguity set as a statistical ball with its radius measured by the Wasserstein metric, we achieve a probabilistic guarantee of the out-of-sample risk, evaluated using new sample data generated independently of the training data. To resolve the infinite-dimensionality issue inherent in the distributionally robust MPC problem, we reformulate it as a finite-dimensional nonlinear program using modern distributionally robust optimization techniques based on the Kantorovich duality principle. To find a globally optimal solution in the case of affine dynamics and output equations, a spatial branch-and-bound algorithm is designed using McCormick relaxation. The performance of the proposed method is demonstrated and analyzed through simulation studies using a nonlinear car-like vehicle model and a linearized quadrotor model.

更新日期：2020-01-15
• arXiv.cs.SY Pub Date : 2020-01-14
Kuize Zhang

The state inference problem and fault diagnosis/prediction problem are fundamental topics in many areas. In this paper, we consider discrete-event systems (DESs) modeled by finite-state automata (FSAs). There exist many results for decentralized versions of the latter but there is almost no result for a decentralized version of the former. In this paper, we propose a decentralized version of strong detectability called co-detectability which implies that once a system satisfies this property, for each generated infinite-length event sequence, at least one local observer can determine the current and subsequent states after a common time delay. We prove that the problem of verifying co-detectability of FSAs is NP-hard. Moreover, we use a unified concurrent-composition method to give PSPACE verification algorithms for co-detectability, co-diagnosability, and co-predictability of FSAs, without any assumption or modifying the FSAs under consideration, where co-diagnosability and co-predictability are notions that have been widely studied in the literature, but most of their verification algorithms in the literature work under two widely-used assumptions of deadlock-freeness and having no unobservable reachable cycle, or after modifying the FSA under consideration by adding at each deadlock state a self-loop. Under the unified framework used in the paper, one can see that in order to verify co-detectability, more technical difficulties will be met compared to verifying the other two properties, because in co-detectability, generated outputs are counted, but in the latter two properties, only occurrences of events are counted. For example, when one output was generated, any number of unobservable events could have occurred. The PSPACE-hardness of verifying co-diagnosability is already known in the literature. In this paper, we prove the PSPACE-hardness of verifying co-predictability.

更新日期：2020-01-15
• arXiv.cs.SY Pub Date : 2020-01-14
Xu Fang; Chen Wang; Lihua Xie; Jie Chen

This paper addresses a multi-pursuer single-evader pursuit-evasion game where the free-moving evader moves faster than the pursuers. Most of the existing works impose constraints on the faster evader such as limited moving area and moving direction. When the faster evader is allowed to move freely without any constraint, the main issues are how to form an encirclement to trap the evader into the capture domain, how to balance between forming an encirclement and approaching the faster evader, and what conditions make the capture possible. In this paper, a distributed pursuit algorithm is proposed to enable pursuers to form an encirclement and approach the faster evader. An algorithm that balances between forming an encirclement and approaching the faster evader is proposed. Moreover, sufficient capture conditions are derived based on the initial spatial distribution and the speed ratios of the pursuers and the evader. Simulation and experimental results on ground robots validate the effectiveness and practicability of the proposed method.

更新日期：2020-01-15
• arXiv.cs.SY Pub Date : 2020-01-13

The kinematic model for the planar Purcell's swimmer - a low Reynolds number microswimmer is derived and used extensively in the literature. We revisit the derivation and give the explicit expression of the local form of the connection form in this note.

更新日期：2020-01-15
• arXiv.cs.SY Pub Date : 2020-01-14
Tom R. V. Steentjes; Mircea Lazar; Paul M. J. Van den Hof

This work is concerned with the analysis, existence and synthesis of distributed output-feedback controllers, that achieve stability and $\mathscr{H}_2$ performance for discrete-time linear interconnected systems. We consider an interconnection structure of local controllers that resembles the plant's interconnection structure, which may correspond to an arbitrary graph. The dissipativity-based approach to distributed discrete-time $\mathscr{H}_2$ control presented in this paper complements other dissipativity-based approaches in the literature to distributed continuous-time $\mathscr{H}_2$ control and distributed $\mathscr{H}_\infty$ control. Moreover, the developed method yields a convex alternative to state-of-the-art methods for distributed discrete-time $\mathscr{H}_2$ control, which are typically not convex or consider unstructured problems. We provide an overview of related results and show the relation between sufficient conditions for $\mathscr{H}_2$ and $\mathscr{H}_\infty$ performance, for both discrete- and continuous-time interconnected linear systems. Sufficient conditions are stated for the existence of a distributed controller achieving a pre-specified $\mathscr{H}_2$ performance. A method for subsequent controller reconstruction is provided by an algebraic procedure. We illustrate the controller synthesis for a large-scale oscillator network, for which the central $\mathscr{H}_2$ control problem can be computationally intractable on a modern PC.

更新日期：2020-01-15
• arXiv.cs.SY Pub Date : 2020-01-14
Mingwu Li; Harry Dankowicz

We consider a class of optimal control problems on networks that generically permits a reduction to a universal set of reference problems without differential constraints that may be solved analytically. The derivation shows that input homogeneity across the network results in universally constant optimal control inputs. These predictions are validated using numerical analysis of problems of synchronization of coupled phase oscillators and spreading dynamics on time-varying networks.

更新日期：2020-01-15
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