Jump Box–Jenkins (BJ) models are a collection of a finite set of linear dynamical submodels in BJ form that switch over time, according to a Markov chain. This paper addresses the problem of maximum-a-posteriori estimation of jump BJ models from a given training input/output dataset. The proposed solution method estimates the coefficients of the BJ submodels, the state transition probabilities of the

A distributed pose localization framework based on direction measurements is proposed for a type of leader–follower multi-agent system in R3. The novelty of the proposed localization method lies in the elimination of the need for using distance measurements and relative orientation measurements for the network pose localization problem. In particular, a network localization scheme is developed based

Port-Hamiltonian system theory is a well-known framework for the control of complex physical systems. The majority of port-Hamiltonian control design methods base on an explicit input-state-output port-Hamiltonian model for the system under consideration. However in the literature, little effort has been made towards a systematic, automatable derivation of such explicit models. In this paper, we present

The last few decades have witnessed much development in filtering of systems with Gaussian noises and arbitrary unknown inputs. Nonetheless, there are still some important design questions that warrant thorough discussions. Especially, the existing literature has shown that for unbiased and minimum variance estimation of the state and the unknown input, the initial guess of the state has to be unbiased

Unlike most control systems, kinematic uncertainty is present in robot control systems in addition to dynamic uncertainty. The use of different types of external sensors in various configurations also results in different sensory transformation or Jacobian matrices and thus leads to different kinematic models. Currently, there is no systematic theoretical framework in developing data-driven neural

This paper presents a new approach for analyzing delay-dependent stability and hybrid L2×l2-gain performance of linear impulsive delay systems. The new approach is inspired by the delay-partitioning method, the timer-dependent Lyapunov functional method, and the looped-functional method. In the delay-partitioning framework, a new type of timer-dependent Lyapunov functional is constructed, which depends

In this paper, we consider the problem of computing parameters of an objective function for a discrete-time optimal control problem from state and control trajectories with active control constraints. We propose a novel method of inverse optimal control that has a computationally efficient online form in which pairs of states and controls from given state and control trajectories are processed sequentially

In this paper, a singular value decomposition (SVD) approach is developed for implementing the cubature Kalman filter. The discussed estimator is one of the most popular and widely used method for solving nonlinear Bayesian filtering problem in practice. To improve its numerical stability (with respect to roundoff errors) and practical reliability of computations, the SVD-based methodology recently

We consider a secure remote state estimation problem where the observations transmitted from the sensors to a remote estimator could be intercepted by an eavesdropper. To prevent the eavesdropper from acquiring the system states, we propose an encoding–decoding mechanism by combining linear transformation and artificial noise to ensure the exact values of observations unavailable to the eavesdropper

In this paper, we consider the security problem of dynamic state estimations in cyber–physical systems (CPSs) when the sensors are compromised by false data injection (FDI) attacks with complete stealthiness. The FDI attacks with complete stealthiness can completely remove its influences on monitored residuals, which have better stealthy performance against residual-based detectors than existing FDI

We derive a closed form solution for an optimal control problem related to an interbank lending schemes subject to terminal probability constraints on the failure of banks which are interconnected through a financial network. The derived solution applies to a real banks network by obtaining a general solution when the aforementioned probability constraints are assumed for all the banks. We also present

This paper analyzes a nonlinear opinion dynamics model which generalizes the DeGroot model by introducing a bias parameter for each individual. The original DeGroot model is recovered when the bias parameter is equal to zero. The magnitude of this parameter reflects an individual’s degree of bias when assimilating new opinions, and depending on the magnitude, an individual is said to have weak, intermediate

This paper addresses the issues of stability analysis and stabilizing controller design for two classes of discrete-time non-homogeneous semi-Markov jump linear systems (S-MJLSs). The first class is concerned with the sojourn-time probability mass functions that are independent of jump instants, while the other considers the existence of probability mass functions of sojourn time depending on jump

We study the adaptive output-feedback stabilization problem of stochastic strict-feedback systems with sensor uncertainty. Specifically, we consider the simultaneous presence of sensor uncertainty, unknown growth rate and stochastic disturbance, which has not been treated heretofore. By developing a new stochastic adaptive dual-domination approach, an adaptive observer and an output-feedback controller

This paper presents a first-order distributed continuous-time algorithm for computing the least-squares solution to a linear equation over networks. Given the uniqueness of the solution, with nonintegrable and diminishing step size, convergence results are provided for fixed graphs. The exact rate of convergence is also established for various types of step size choices falling into that category.

The coverage control problem for a network of heterogeneous mobile sensors with bound position measurement errors on a circle is addressed in this paper. A practical measurement error model is considered in which only the upper bounds of the measurement errors are known by the sensors a priori. The coverage cost function is defined as the largest arrival time from the mobile sensor network to any point

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

Recent work has shown how chemical reaction network theory may be used to design dynamical systems that can be implemented biologically in nucleic acid-based chemistry. While this has allowed the construction of advanced open-loop circuitry based on cascaded DNA strand displacement (DSD) reactions, little progress has so far been made in developing the requisite theoretical machinery to inform the

This paper presents a novel control protocol for robust distance-based formation control with prescribed performance in which agents are subjected to unknown external disturbances. Connectivity maintenance and collision avoidance among neighboring agents are also handled by the appropriate design of certain performance bounds that constrain the inter-agent distance errors. As an extension to the proposed

We present a stochastic model predictive control (MPC) method for linear discrete-time systems subject to possibly unbounded and correlated additive stochastic disturbance sequences. Chance constraints are treated in analogy to robust MPC using the concept of probabilistic reachable sets for constraint tightening. We introduce an initialization of each MPC iteration which is always recursively feasible

Robust control laws based on the back-stepping sliding mode control (SMC) and extended state observer (ESO) are designed for pressure regulation of the oxygen mask in an oxygen supply system (OSS) which is non-linear in mechanical dynamics. Firstly, the structure and working principle of OSS are introduced; the electronic oxygen regulator (EOR) used in this system is a new type of EOR with proportional

This paper proposes a novel economic Model Predictive Control algorithm aiming at achieving optimal steady-state performance despite the presence of plant-model mismatch or unmeasured nonzero mean disturbances. According to the offset-free formulation, the system’s state is augmented with disturbances and transformed into a new coordinate framework. Based on the new variables, the proposed controller

We study the kick and loss detection and attenuation problem in managed pressure drilling by modeling the well as a distributed parameter system. Two cases are considered, distinguished by whether down-hole pressure measurements are available or not. The main contribution of the paper is a theoretical result on adaptive stabilization and set-point regulation by boundary control for a general 2 × 2

This paper proposes an off-policy learning-based dynamic state feedback protocol that achieves the optimal synchronization of heterogeneous multi-agent systems (MAS) over a directed communication network. Note that most of the recent works on heterogeneous MAS are not formed in an optimal manner. By formulating the cooperative output regulation problem as an H∞ optimization problem, we can use reinforcement

This paper considers the global optimization of max-plus linear systems with affine equality constraints. For both the cases that the variables are real and non-negative, the necessary and sufficient conditions for the existence and uniqueness of globally optimal solutions are given, respectively. The proposed approaches are constructive and yield two polynomial algorithms for checking the solvabilities

This paper presents a Fault Tolerant Control (FTC) scheme for systems with L2-bounded disturbances and generalized sector nonlinearity at the input. An adaptive observer-based fault estimation (FE) scheme is used to estimate the fault. The nonlinearity causes the control input (the measurable controller output, which is injected into the FE scheme) to differ from the actuator output (which enters the

Although there are fruitful results on adaptive control of constrained parametric/nonparametric strict-feedback nonlinear systems, most of them are contingent upon “feasibility conditions”, and/or are only applicable to constant and symmetric constraints. In this work, we present a robust adaptive control solution free from “feasibility conditions” and capable of accommodating much more general dynamic

We consider a scenario in which a DoS attacker with the limited power resource and the purpose of degrading the system performance, jams a wireless network through which the packet from a sensor is sent to a remote estimator. To degrade the estimation quality most effectively with a given energy budget, the attacker aims to solve the problem of how much power to obstruct the channel each time, which

This work studies the problem of LQG control when the link between the sensor and the controller relies on a Wi-Fi network. Unfortunately, the communication on a wireless medium is sensitive to noise in the transmission band, which is characterized by the Signal-to-Noise Ratio (SNR). Wi-Fi allows to switch among different bit-rates in real-time thus permitting to trade-off lower loss probabilities

We study a multi-agent output regulation problem, where only a few agents have access to the exosystem’s dynamics. We propose a fully distributed controller that solves the problem for linear, heterogeneous, non-minimum-phase, and uncertain agent dynamics as well as time-varying directed networks. The distributed controller consists of two parts: (1) an exosystem generator that locally estimates the

In this paper, we establish a new evolution property of Lyapunov functions that satisfy the Razumikhin Stability Theorem conditions. The derived property is shown to be a valid alternative criterion to prove the asymptotic stability of a delay system without calculating the derivative of the Lyapunov function, and it is especially useful in solving stabilization problems for time delay systems when

In this paper a novel approach is presented for control design with guaranteed transient performance for multiple-input multiple-output discrete-time linear polytopic difference inclusions. We establish a theorem that gives necessary and sufficient conditions for the state to evolve from one polyhedral subset of the state-space to another. Then we present an algorithm which constructs a time-varying

The stability of arbitrarily switched discrete-time linear singular (SDLS) systems is studied. Our analysis builds on the recently introduced one-step-map for SDLS systems of index-1. We first provide a sufficient stability condition in terms of Lyapunov functions. Furthermore, we generalize the notion of joint spectral radius of a finite set of matrix pairs, which allows us to fully characterize exponential

We study derivative-dependent control of the nth-order stochastic systems where derivatives are not available for measurements. The derivatives are approximated by finite differences giving rise to a delayed feedback. In the deterministic case, an efficient simple LMI-based method for designing of such static output-feedback and its sampled-data implementation was suggested recently. In the present

We address the problem to estimate a Kronecker graphical model corresponding to an autoregressive Gaussian stochastic process. The latter is completely described by the power spectral density function whose inverse has support which admits a sparse Kronecker product decomposition. We propose a Bayesian approach to estimate such a model. We test the effectiveness of the proposed method by some numerical

One challenge in the optimization and control of societal systems is to handle the unknown and uncertain user behavior. This paper focuses on residential demand response (DR) and proposes a closed-loop learning scheme to address these issues. In particular, we consider DR programs where an aggregator calls upon residential users to change their demand so that the total load adjustment is close to a

Boundary observer design for a system of ODEs in cascade with hyperbolic PDEs is studied. An infinite dimensional observer is used to solve the state estimation problem. The interconnection of the observer and the system is written in estimation error coordinates and analyzed as an abstract dynamical system. The design of the observer is performed to achieve global exponential stability of the estimation

This paper presents a new integral sliding mode control (SMC) algorithm that can handle matched uncertainties in the presence of the given fast sensor dynamics. More precisely, by selecting the sliding surface that is sufficiently faster than the sensor dynamics, it is possible to maintain unaffected the time-scale separation between sensors and plant. A modified singular perturbation technique is

Motivated by the potential of utilizing used electric vehicle (EV) batteries as the battery energy storage system (BESS) in EV charging stations, we study the joint scheduling of BESS operation and deferrable EV charging load (with the same deadline) in the presence of random renewable generation, EV arrivals, and electricity prices. We formulate the cost-minimizing scheduling problem faced by an EV

Nonlinear robust control is pursued by overcoming the drawback of linear robust control that it ignores available information about existing nonlinearities and the resulting controllers may be too conservative, especially when the nonlinearities are significant. However, most existing nonlinear robust control approaches just consider the affine nonlinear nominal model and thereby ignore available information

We propose a controller that robustly stabilizes an interconnection of Ordinary Differential Equations with two hyperbolic linear Partial Differential Equations. The proposed design guarantees closed-loop asymptotic stability as well as robustness to small delays. The result is obtained through a backstepping approach. A boundary observer is also derived, yielding an output feedback controller with

This paper presents conditions for the synthesis of a strictly negative imaginary closed-loop system with a prescribed degree of stability under the assumption of full state feedback. A perturbation method is used to ensure the closed-loop system has both the strict negative imaginary property and a prescribed degree of stability. This approach involves the real Schur decomposition of a matrix followed

This paper is concerned with necessary and sufficient conditions for the existence of Pareto solutions in finite horizon mean-field type stochastic cooperative differential game. Based on the equivalent characterization of Pareto optimality, the problem is transformed into a set of constrained mean-field type stochastic optimal control problems with a special structure. Utilizing the mean-field type

We provide finite time reduced order observers for a class of nonlinear time-varying continuous-time systems. We use the observers to design globally asymptotically stabilizing output feedback controls. We illustrate our work in tracking dynamics for a nonholonomic system in chained form.

This work is concerned with the boundary observer-based control for a category of cascade systems with stochastic jumps. The cascade system consists of ordinary differential equations (ODEs) entering the hyperbolic partial differential equations (PDEs) through the boundary. A continuous-time, discrete-state homogeneous Markov process models the jumping parameters of the system, and its right continuous

This paper studies the suboptimal output consensus problem for a multi-agent system, where each agent is described by a weakly nonminimum phase linear system and endowed with an objective function. For each agent, a low gain feedback based protocol consisting of a local term and a distributed term and parameterized with a low gain parameter is constructed. Under the assumption that the communication

Previous approaches to constructing abstractions for control systems rely on geometric conditions or, in the case of an interconnected control system, a condition on the interconnection topology. Since these conditions are not always satisfiable, we relax the restrictions on the choice of abstractions, instead opting to select ones which nearly satisfy such conditions via optimization-based approaches

For general discrete-time nonlinear systems, we address the state estimation problem under sensor attacks. We provide a general estimation scheme, built around the idea of sensor redundancy and multi-observers, capable of reconstructing the system state in spite of sensor attacks and noise, for a large class of nonlinear plants and observers. This scheme has been proposed by others for linear systems

Event-triggered tracking control for a class of nonlinear systems with disturbances is investigated in this paper. Compared to existing related results, the nonlinearities only need to satisfy a generalized Lipschitz condition, and the time-varying external disturbances are allowed to be unmatched. By using finite-time disturbance observers, the finite-time estimation of the steady states is achieved

It is well-known that the widely popular Interconnection and Damping Assignment Passivity-based Control (IDA-PBC) is “universally stabilizing”, in the sense that it generates all asymptotically stabilizing controllers for general nonlinear systems of the form ẋ=f(x,u). Unfortunately, the proof of this fact relies in the construction of a control law that is not globally defined. Although a standard

In this technical communiqué we study the maximal robust positively invariant set for state-constrained continuous-time nonlinear systems subjected to a bounded disturbance. Extending results from the theory of barriers, we show that this set is closed and that its boundary consists of two complementary parts, one of which we name the invariance barrier, which consists of trajectories that satisfy

Design of reduced-order distributed controllers for solving the output feedback state consensus problem in a homogeneous multi-agent network is the topic of this paper. The dynamics of each agent in this network is governed by a marginally unstable single-input single-output linear system and each agent can access the relative output of its neighboring agents. Recognizing, possibly for the first time

This paper studies the performance of a continuous controller when implemented on digital devices via sampling and quantization, by leveraging passivity analysis. Degradation of passivity indices from a continuous-time control system to its sampled, input and output quantized model is studied using a notion of quasi-passivity. Based on that, the passivity property of a feedback-connected system where

Extremum-seeking control (ESC) is a useful tool for the steady-state performance optimization of plants for which limited knowledge about its dynamical behavior and disturbance situation is known. The case when the steady-state plant responses correspond to equilibrium solutions received a lot of attention. However, in many industrial applications plant performance is characterized by time-varying

We consider the problem of vehicle following, where a safety distance to the leader vehicle is guaranteed at all times and a favourite velocity is reached as far as possible. We introduce the funnel cruise controller as a novel universal adaptive cruise control mechanism which is model-free and achieves the aforementioned control objectives. The controller consists of a velocity funnel controller,

State convergence is essential in many scientific areas, e.g. multi-agent consensus/disagreement, distributed optimization, computational game theory, multi-agent learning over networks. In this paper, we study for the first time the state convergence problem in uncertain linear systems. Preliminarily, we characterize state convergence in linear systems via equivalent linear matrix inequalities. In

The present work extends known finite-dimensional constrained optimal control realizations to the realm of well-posed regular linear infinite-dimensional systems modeled by partial differential equations. The structure-preserving Cayley–Tustin transformation is utilized to approximate the continuous-time system by a discrete-time model representation without using any spatial discretization or model

Linear threshold models have been studied extensively in structured populations; however, less attention is paid to perception differences among the individuals of the population. To focus on this effect, we exclude structure and consider a well-mixed population of heterogeneous agents, each associated with a threshold in the form of a fixed ratio within zero and one that can be unique to this agent

This paper studies the distributed control of multi-agent systems with pulse-width-modulated (PWM) control inputs. A necessary and sufficient condition on the PWM control laws is given for uniform asymptotic state agreement with directed and switched information exchange topologies. The methodology is validated by a computer-based simulation.

An algorithm to select an output for desired vector relative degree, which is not restricted to be uniform (the same for all outputs), and zero dynamics in linear time-varying (LTV) multi-input multi-output (MIMO) systems is proposed. The proposed algorithm is developed under reasonable assumptions of uniform (with respect to time) and lexicography-fixed controllability (with some choice of fixed controllability