This paper solves the simultaneous localization and formation (SLAF) problem for a multi-agent system moving in 2D plane. The multi-agent system consists of leaders who have the knowledge of their absolute positions in the global coordinate frame, and followers who do not know their absolute positions but have angle-only measurements and communication with respect to their neighboring agents. The aim

Boolean functions can be represented in many ways including logical forms, truth tables, and polynomials. Additionally, Boolean functions have different canonical representations such as minimal disjunctive normal forms. Another canonical representation is based on the polynomial representation of Boolean functions and the biologically motivated concept of canalization: any Boolean function can be

Motivated by applications in reinforcement learning (RL), we study a nonlinear stochastic approximation (SA) algorithm under Markovian noise, and establish its finite-sample convergence bounds under various stepsizes. Specifically, we show that when using constant stepsize (i.e., αk≡α), the algorithm achieves exponential fast convergence to a neighborhood (with radius O(αlog(1/α))) around the desired

A hybrid observer is described for estimating the state of an m>0 channel, n-dimensional, continuous-time, linear system of the form ẋ=Ax,yi=Cix,i∈{1,2,…,m}. The system’s state x is simultaneously estimated by m agents assuming each agent i senses yi and receives appropriately defined data from each of its current neighbors. Neighbor relations are characterized by a time-varying directed graph N(t)

This paper investigates sensor scheduling for state estimation of complex networks over shared transmission channels. For a complex network of dynamical systems, referred to as nodes, a sensor network is adopted to measure and estimate the system states in a distributed way, where a sensor is used to measure a node. The estimates are transmitted from sensors to the associated nodes, in the presence

Safety-critical control is characterized as ensuring constraint satisfaction for a given dynamical system. Recent developments in zeroing control barrier functions (ZCBFs) have provided a framework for ensuring safety of a superlevel set of a single constraint function. Euler–Lagrange systems represent many real-world systems including robots and vehicles, which must abide by safety-regulations, especially

In this paper, we study closed-loop equilibrium strategies for mean–variance portfolio selection problems in a hidden Markov model with dynamic attention behavior. In addition to the investment strategy, the investor’s attention to news is introduced as a control of the accuracy of the news signal process. The objective is to find equilibrium strategies by numerically solving an extended HJB equation

This paper proposes a novel clustering-based approach to the bounded-error identification of switched and piecewise affine autoregressive exogenous systems. We address the problem of determining a minimal collection of linear-in-the-parameters models (called modes) fitting with a given accuracy ɛ a set of input–output data while complying with the switched or piecewise affine nature of the system.

We propose a novel, system theoretic analysis of the Alternating Direction Method of Multipliers (ADMM) applied to a convex constraint-coupled optimization problem. The resulting algorithm can be interpreted as a linear, discrete-time dynamical system (modeling the multiplier ascent update) in closed loop with a static nonlinearity (representing the minimization of the augmented Lagrangian). When expressed

This paper is devoted to the time-varying gain controller synthesis problem for a class of discrete-time piecewise homogeneous semi-Markov jump linear systems (SMJLSs). Both sojourn-time probability mass functions and embedded Markov chain transition probability are simultaneously regulated by high-level Markov chain, which leads to a more general piecewise homogeneous semi-Markov kernel. A novel class

Economic Model Predictive Control (MPC) dissipativity theory is central to discussing the stability of policies resulting from minimizing economic stage costs. In its current form, the dissipativity theory for economic MPC applies to problems based on deterministic dynamics or to very specific classes of stochastic problems, and does not readily extend to generic Markov decision processes. In this

In this paper, the input-to-state stability (ISS) problem of switched linear system with unstabilizable modes is investigated under denial-of-service (DoS) attacks and external disturbance. In contrast to earlier results which consider only stabilizable modes and usually impose certain constraints on the switching behavior and the DoS attacks, the switched system with unstabilizable modes is studied

This paper studies distributed optimization problem over a fixed network. We develop and analyze an accelerated distributed gradient descent method, named Acc-DGDlm, which utilizes gradient tracking technique and local memory. Specifically, we add two memory slots per agent to store two past estimates, namely, an estimate of the optimal solution and an estimate of the average gradient. For strongly

This paper presents a novel anti-windup proportional–integral controller for stable multi-input multi-output nonlinear plants. We use tools from projected dynamical systems theory to force the integrator state to remain in a desired (compact and convex) region, such that the plant input steady-state values satisfy the operational constraints of the problem. Under suitable monotonicity assumptions on

In this note, a formula for the Bohl exponent of a discrete system with slowly varying coefficients was proved. This formula expresses the Bohl exponent through the eigenvalues of the coefficients. Based on these formulae a necessary and sufficient condition for uniform exponential stability of such systems are also presented.

In this paper, a necessary and sufficient condition for finite-time identification of a regression model, obtained using the dynamic regressor extension and mixing (DREM) method, is established. Estimators designed to satisfy transient and robust specifications via a time-varying gain are then proposed to have this condition as necessary and sufficient for their convergence to the true values when

We study the strong structural controllability (SSC) of networks, where the external control inputs are injected to only some nodes, namely the leaders. For such systems, one measure of controllability is the dimension of strong structurally controllable subspace (SSCS), which is equal to the smallest possible rank of controllability matrix under admissible coupling weights among the nodes In this

Estimating a function from noisy measurements is a crucial problem in statistics and engineering, with an impact on machine learning predictions and identification of dynamical systems. In view of robust control design and safety-critical applications such as autonomous driving and smart healthcare, estimates are required to be complemented with uncertainty bounds quantifying their reliability. Most

The combination of learning methods with Model Predictive Control (MPC) has attracted a significant amount of attention in the recent literature. The hope of this combination is to reduce the reliance of MPC schemes on accurate models, and to tap into the fast developing machine learning and reinforcement learning tools to exploit the growing amount of data available for many systems. In particular

Sliding Mode Observer (SMO) based methods have been extensively used for Fault Estimation (FE). However, the fault detection (FD) problem for these SMO based FE methods has not been completely solved. In this paper a robust threshold on the so-called Equivalent Output Injection (EOI) is presented which enables FD for systems with measurement noise and unmatched uncertainties. This threshold is applicable

This paper studies the problem of the prescribed-time tracking control for a class of strict-feedback nonlinear systems with guaranteed performance. We will design a controller to achieve the following: one is to achieve the prescribed-time tracking, in which the settling time is not only independent of the design parameters, nor does it depend on the initial conditions, and can be set according to

This paper studies the design of feedback controllers to steer a switching linear dynamical system to the solution trajectory of a time-varying convex optimization problem. We propose two types of controllers: (i) a continuous controller inspired by the online gradient descent method, and (ii) a hybrid controller that can be interpreted as an online version of Nesterov’s accelerated gradient method

Abstract not available

This paper studies the target tracking problem over wireless sensor networks (WSNs). While most existing works on this problem focus on the single-target case and the few existing two-target tracking methods are based on linear observation models, we pay attention to the two-target tracking over WSNs, considering nonlinear targets’ dynamics and observation models. We divide all the sensors in the WSN

In this study, we extend the theory of negative imaginary (NI) systems to a nonlinear framework using a frequency-domain approach. The extended notion is completely characterized via a finite-frequency integration over a “kernel function” on energy-bounded input and output signal pairs. The notion is closely related to and carefully contrasted with the well-studied extension of negative imaginariness

In this paper, we study the structural state and input observability of continuous-time switched linear time-invariant systems and unknown inputs. First, we provide necessary and sufficient conditions for their structural state and input observability that can be efficiently verified in O((m(n+p))2), where n is the number of state variables, p is the number of unknown inputs, and m is the number of

In this note it is shown that J. Willems’ and R. Brockett’s time-varying version of the powerful multiplier class of R. O’Shea, G. Zames and P. Falb does not enhance absolute stability tests for Lur’e interconnections with sector-bounded nonlinearities if the main plant is time-invariant.

This paper presents a new method to find α-exponential state estimates, where α>0 is a given decay rate, for a class of positive systems with bounded time-varying delays. Here, our novel idea is to construct a new class of “comparison” systems, from which we can derive a smaller factor vector for an α-exponential estimate on the solution of the considered system, i.e. derive a tighter α-exponential

This paper investigates a distributed optimization problem based on the framework of a multi-agent system over a directed communication network, where the global cost function is the sum of the local cost functions of agents. The communication network is abstracted as a weight-unbalanced directed graph. First, a surplus-based accelerated algorithm with a fixed stepsize (SAAFS) is proposed by integrating

By enabling the nodes or agents to solve small-sized subproblems to achieve coordination, distributed algorithms are favored by many networked systems for efficient and scalable computation. While for convex problems, substantial distributed algorithms are available, the results for the more broad nonconvex counterparts are extremely lacking. This paper develops a distributed algorithm for a class

This paper is concerned with the design of a human-in-the-loop system for deployment on a smart pedelec (e-bike). From the control-theoretic perspective, the goal is not only to use the power assistance of the e-bike to reject disturbances along the route but also to manage the possibly competitive interactions between a human and the motor intervention. Managing the competitive/cooperative nature

Prescribed-performance control (PPC) for high-power dynamics with time-varying unknown control coefficients requires to address two open problems: (a) given a Nussbaum function, which properties hold for the power of the Nussbaum function? (b) to avoid high gains, how to design a switching gain that increases only when the tracking error is close to violate the performance bounds? To address the first

The control parameterization method used together with time-scaling transformation is an effective approach to transform optimal control problems into optimal parameter selection problems. The approximate problems can then be solved by gradient-based optimization methods. However, the conventional time-scaling transformation requires that all the control component functions switch simultaneously, which

This paper considers the fixed-time control problem of a multi-agent system composed of a class of Euler–Lagrange dynamics with parametric uncertainty and a dynamic leader under a directed communication network. A distributed fixed-time observer is first proposed to estimate the desired trajectory and then a fixed-time robust controller is constructed by transforming uncertain Euler–Lagrange systems

Reinforcement learning provides a powerful tool for designing a satisfactory controller through interactions with the environment. Although off-policy learning algorithms were recently designed for tracking problems, most of these results either are full-state feedback or have bounded control errors, which may not be flexible or desirable for engineering problems in the real world. To address these

This paper addresses Lyapunov characterizations on input-to-state stability (ISS) of time-varying nonlinear systems with infinite delays. With novel ISS definitions in the case of nonlinear systems with infinite delays, we present several results on their ISS Lyapunov characterizations in the form of both ISS Lyapunov theorems and converse ISS Lyapunov theorems. It is shown that an infinite-delayed

Leader election, is a fundamental coordination problem in distributed systems. It has been addressed in many ways for different systems. Among these approaches, resilient leader election algorithms are of particular interest due to the ongoing emergence of open, complex distributed systems such as smart cities and the Internet of Things. However, previous algorithms attaining the optimal scaling of

We give the explicit solution of the optimal control problem which consists in minimizing the epidemic peak in the SIR model when the control is an attenuation factor of the infectious rate, subject to a L1 constraint on the control which represents a budget constraint. The optimal strategy is given as a feedback control which consists in a singular arc maintaining the infected population at a constant

Gain-scheduling is a popular control technique to deal with nonlinear and time varying systems. The linear parameter-varying (LPV) system approach offers systematic tools to design gain-scheduled controllers. However, the implementation of these controllers might demand complex mathematical operations to be performed in real-time. This limits the hardware and the applications in which LPV controllers

The issue of overall fault diagnosability evaluation is addressed to provide a standard for system design from the fault diagnosis perspective. An overall fault diagnosability measure is first defined statistically by combining quantitative and qualitative evaluation results. On this basis, a randomized algorithm-based method is developed for estimating this quantitative–qualitative measure with a

Input–output properties are studied for a linear network model with homogeneous single-input single-output subsystems. Specifically, the finite- and infinite- zero structure of a channel in the network, defined by a single external actuation and measurement, is characterized. This is done by developing a two-layer transformation for the input–output model, which expresses the zero structure in terms

A mechanical system under strongly nonlinear potential and dissipative forces, with nonlinear nonstationary perturbations having zero mean values, is studied. Proposing a special construction of Lyapunov function, the conditions are found, under which the perturbations do not influence the asymptotic stability of the trivial equilibrium position of the system. These conditions include the requirements

Recent research has shown that one can obtain a less conservative stability criterion for a continuous-time linear system with a time-varying delay by introducing a Lyapunov-Krasovskii functional with a polynomial matrix on the time-varying delay. This paper aims at analysing the stability of discrete-time linear systems with time-varying delays by introducing a delay-square-dependent Lyapunov functional

The design of feedback control systems to block observability in a network synchronization model, i.e. to make the dynamics unobservable from measurements at a subset of the network’s nodes, is studied. First, a general design algorithm is presented for blocking observability at any group of m nodes, by applying state feedback controls at m+2 specified actuation nodes. The algorithm is based on a method

This paper presents a frequency domain resilient consensus problem of time-delayed multi-agent systems (MASs) under attacks. An internal model principle-based attack, a kind of network attack, is firstly analyzed and modeled in frequency domain. Then, a Quasi-H∞ robust controller is designed for resilient consensus of the MAS with the help of an internal model controller. This scheme is different from

This paper addresses an optimal output regulation problem for linear time-invariant systems with unknown dynamics. First, a new augmented virtual system is designed to replace the original system and the internal model. Then, by incorporating the data from the augmented virtual system with adaptive dynamic programming (ADP) method, a new iterative learning equation without requiring integral operations

We study a coordinated maintenance problem for multi-turbine offshore wind-farms. The deterioration of the turbines follows a Poisson process and the optimal order quantity for a single wind-farm, as well as for a coalition of wind-farms, is obtained using the analogy with the risk-averse newsvendor model considering the conditional value at risk (CVaR) criterion. We apply the theory of robust dynamic

This paper investigates the finite-time optimal control problems for positive linear systems with a time-varying control input. Cost functional of the system state is constructed. Under some assumptions on the designed parameters and cost functionals, the optimization problem with piecewise-constant matrix functions is first proved to be log–log convex and can be solved via geometric programming. Then

Dynamic event-triggered mechanisms (ETMs) have shown potential in further reducing the number of events in comparison to their static counterparts while delivering similar system performance. In this paper, we provide a framework to design dynamic periodic event-triggered controllers for multi-agent systems (MASs) with nonlinear dynamics. A preliminary version of this work on single-agent systems was

This paper studies the stabilization and safety problems of nonlinear time-delay systems. Following both Razumikhin and Krasovskii approaches, we propose novel control Lyapunov functions/functionals for the stabilization problem and novel control barrier functions/functionals for the safety problem. The proposed control Lyapunov and barrier functions/functionals extend the existing ones from the delay-free

Industrial control systems have been frequent targets of cyber attacks during the last decade. Adversaries can hinder the safe operation of these systems by tampering with their sensors and actuators, while ensuring that the monitoring systems are not able to detect such attacks in time. This paper presents methods to design and overcome stealthy attacks on linear time-invariant control systems that

This paper is concerned with the problem of dissipative output feedback tracking control for Markov jump systems under compensation scheme. The measured output of the system is assumed to be dropped in a probabilistic way before being transmitted to the controller. By virtue of the single exponential smoothing technique, a novel compensation strategy is proposed to help offset the impact of missing

By time discretization of a second-order primal–dual dynamical system with damping α/t where an inertial construction in the sense of Nesterov is needed only for the primal variable, we propose a fast primal–dual algorithm for a linear equality constrained convex optimization problem. Under a suitable scaling condition, we show that the proposed algorithm enjoys a fast convergence rate for the objective

This paper proposes scalable distributed least square algorithms for solving large-scale linear algebraic equations via multi-agent network. We consider two kinds of different decomposition structures of Ax=b, in which each agent only knows a sub-block of linear equation instead of the complete row or column information in the existing results. By introducing one extra variable, the least square problem

In this paper, the robust adaptive tracking control problem is investigated for nonlinear time-delay systems with changing system powers. Different from the existing results, the discussed system has complicated nonlinear growing conditions and various uncertainties including the control coefficients, parameters, and external disturbances. By introducing a new robust adaptive control method, a dynamic-gain

Active–passive networks are composed of nodes that meet specific criteria depending on applications (active nodes) and nodes that do not (passive nodes). This paper investigates the network size estimation problem, which involves determining the number of active nodes in anonymous active–passive networks in a distributed way. To this end, a distributed counting architecture is proposed to locally manipulate

A new notion of robust F-stability is introduced, based on the recently introduced notion of F-passivity. Necessary and sufficient conditions for the robust F-stabilization under dynamical structured uncertainties are given, in terms of matrix inequalities. If besides dynamical, also statical uncertainties are included, the given conditions are only sufficient. The conditions are not convex, but for

This paper considers the problem of solving distributed online optimization over a network that consists of multiple interacting nodes. Each node in the network is endowed with a sequence of loss functions, each of which is revealed to the node after a decision has been committed. The goal of the network is to minimize the cumulative loss functions of nodes in a distributed fashion, while subject to

Abstract not available

This paper presents a new approach to enforce state, output, and control constraints in closed-loop nonlinear systems. The approach is based on the Reference Governor (RG) scheme and leverages set-theoretic methods in control, as well as data-driven offline optimization. Specifically, the approach decomposes the design of the constraint management strategy into two parts: enforcement at steady-state