Barrier function-based adaptive sliding mode control (BFASMC) is analyzed in presence of deterministic measurement noise. It is shown that, considering only boundedness of the measurement noise, it is impossible to select the controller parameters to track some perturbation with unknown bound. Nonetheless, under the assumption of continuity of the noise, the tracking of such a perturbation is possible;

This paper presents two model-free control strategies for the rejection of unknown disturbances in continuum robots. The strategies utilize a neural network-based approximation technique to estimate the uncertain Jacobian matrix using position measurements. The first strategy is designed for periodic disturbances and employs an adaptive model-free controller in conjunction with an adaptive disturbance

We derive two types of stochastic Hegselmann–Krause opinion formation models with leadership, study their different asymptotic behaviors and the noise effect to the leader’s control. Firstly, we propose a stochastic model with a multiplicative noise, which mimics the randomness from communication uncertainty. Using the Lyapunov functional approach, we provide a sufficient condition leading to the almost

In this paper, we propose a new property of of an automaton with respect to a given cover on its set of marker states. This property the standard nonblocking property by capturing the practical requirement that every subset (i.e. cell) of marker states can be reached within a prescribed number of steps from any reachable state and following any trajectory of the system. Accordingly, we formulate a

An energy provider faced with energy generation risks and a large homogeneous pool of customers designs its energy price as a time-varying function of a risk-related quantile of the total energy demand, which generalizes pricing through the mean of the total energy demand. In the infinite population limit, we model the pricing problem with a class of linear quadratic Gaussian quantilized mean field

This study focuses on the problem of optimal mismatched disturbance rejection control for uncontrollable linear discrete-time systems. In contrast to previous studies, by introducing a quadratic performance index such that the regulated state can track a reference trajectory and minimize the effects of disturbances, mismatched disturbance rejection control is transformed into a linear quadratic tracking

This paper is concerned with approximately solving the optimal predictor-feedback control problem of multiplicative-noise systems with input delay in infinite horizon. The optimal predictor-feedback control, provided by the analytical method, is determined by Riccati–ZXL equations and is hard to obtain in the case of unknown system dynamics. We aim to propose a policy iteration (PI) algorithm for solving

This paper addresses a resilient bipartite output consensus issue for high-order heterogeneous multi-agent systems (MASs) with Byzantine attacks. Output regulator equations as well observers are introduced for bipartite leader-following issue with heterogeneous dynamics. For the security concerns, a multidimensional-bipartite-absolute-mean-subsequence-reduced (MBA-MSR) algorithm is developed for each

This paper develops a distributed primal–dual algorithm via an event-triggered mechanism to solve a class of convex optimization problems subject to local set constraints, coupled equality and inequality constraints. Different from some existing distributed algorithms with the diminishing step-sizes, our algorithm uses the constant step-sizes, and is shown to achieve an exact convergence to an optimal

Given a fixed simulation budget, the problem of selecting the best and top- alternatives among a finite set of alternatives have been studied separately in simulation optimization literature, because the existing sampling procedures are often dedicated to one problem. Under a Bayesian framework, we formulate the top- selection into a stochastic dynamic program, and characterize the optimal sampling

In this paper, we focus on for in a dense time context, i.e., the time constraints of the automata can be given according to real numbers. Given a sequence of timed observations (i.e., pairs of logical observations with their time stamps) collected from a system within a finite time window, a state estimation method is proposed to find the set of states in which the system might reside by the end of

A class of homogeneous systems with sector nonlinearities is considered in the paper. Sufficient conditions of finite-time (input-to-state) stability are established with the use of new constructive modification of the Implicit Lyapunov Function approach. The proposed conditions are given in the form of linear matrix inequalities. The theoretical results are supported with numerical examples.

In all the references on stochastic fixed-time stability, the customary treatment of the stochastic noise in the worst-case sense is that it is treated as the unfavorable factor for system stability. Consequently, stochastic fixed-time Lyapunov-type conditions are rather restrictive. Realizing this limitation, we revisit stochastic fixed-time stability and present a generalized fixed-time stability

In this paper, the problem of online distributed optimization with stochastic and nonconvex objective functions is studied by employing a multi-agent system. When making decisions, each agent only has access to a noisy gradient of its own objective function in the previous time and can only communicate with its immediate neighbors via a time-varying digraph. To handle this problem, an online distributed

This paper studies the optimal cooperative output regulation problem for unknown linear multi-agent systems by the integral reinforcement learning technique. Existing results on this problem were obtained by a non-fully distributed learning process. In contrast, we propose a distributed learning algorithm over the jointly connected switching communication networks. Moreover, by modifying the existing

With the massive popularization of distributed generators, optimal economic dispatch has been a key optimization problem to maintain stable and efficient work of the whole system. In this paper, a new smooth reconstruction penalty function with continuous and piecewise linear differential is designed to deal with generation power constraints, which promotes to obtain a better suboptimal solution compared

The identification problem in case of data with missing values is challenging and currently not fully understood. For example, there are no general nonconservative identifiability results, nor provably correct data efficient methods. In this paper, we consider a special case of periodically missing output samples, where all but one output sample per period may be missing. The novel idea is to use a

Linear continuous-time systems with switched pointwise delays are studied. A technique enabling to establish the stability of these systems with a rapidly varying periodic delay is proposed. It relies on two ingredients: a representation of the systems as time-varying systems with constant delays and an averaging approach. Illustrative examples show the effectiveness of the proposed methodology.

This paper presents a novel approach called physics-informed probabilistic slow feature analysis. The probabilistic slow feature analysis method has been employed to extract slowly varying latent patterns from high-dimensional measured data. The extracted slow features have proven effective in industrial applications such as soft sensing and process monitoring. However, industrial processes come with

Given a set of full-rank matrices , this brief paper proposes a method based on linear feasibility tests to determine whether a convex combination , with in the unit simplex , may result in a rank-deficient matrix. The method is based on a sequence of linear programs with increasingly tightened constraints, and is guaranteed to reach an outcome after a finite number of iterations. Given a tolerance

A flaw is uncovered in the proof of a central theorem (Theorem 1) in Zhang and Gao (2010). This sheds doubt on the validity of the results claimed in that paper.

This paper considers consensus strategy design for double-integrator multi-agent systems with matched disturbances under a directed graph. Specifically, we consider the case where both the control inputs and velocities of the agents are subject to different and asymmetric constraints. A novel distributed algorithm exploiting saturation functions is proposed to achieve asymptotic consensus without violating

Control of mixed-autonomy traffic where Human-driven Vehicles (HVs) and Autonomous Vehicles (AVs) coexist on the road has gained increasing attention over the recent decades. This paper addresses the boundary stabilization problem for mixed traffic on freeways. The traffic dynamics are described by uncertain coupled hyperbolic partial differential equations (PDEs) with Markov jumping parameters, which

The stabilizing switching signal design of discrete-time linear compartmental switched systems (DT-LCSSs) has been heretofore unsolved. It has been proven that a DT-LCSS is stabilizable if and only if it is stabilizable by a periodic switching signal. However, it still needs to be determined whether the period of a stabilizing switching signal can be confined within a bound. Moreover, the existing

This paper shows how the interconnection of two controlled Irreversible port Hamiltonian Systems has to be state and co-state modulated in order to ensure the closed-loop Irreversible port Hamiltonian structure, satisfying the first and second laws of Thermodynamics. It proposes a precise parametrization of this modulation from the open-loop systems structures in order to guarantee the consistency

This paper considers output feedback stabilization of linear systems with both distributed infinite input and output delays. We first propose a PDE-based observer to deal with distributed infinite output delays. The main idea is to transform infinite-delayed output into a transport PDE with a semi-infinite domain. It is proved that the resulted error systems are exponentially stable and thus the observer

By computing Lyapunov functions of a certain, convenient structure, Lyapunov-based methods guarantee stability properties of the system or, when performing synthesis, of the relevant closed-loop or error dynamics. In doing so, they provide conclusive affirmative answers to many analysis and design questions in systems and control. When these methods fail to produce a feasible solution, however, they

This paper explores a displacement-based formation control problem in the presence of misaligned orientations among different body coordinate frames. When agents sense and adjust their relative positions, these misalignments suggest the attitudes of the agents in the absence of an agreement, thereby distorting collective behavior. To mitigate this distortion, an angular velocity control protocol is

We give a simple proof of the (perhaps not so) well known fact that exponential polynomials of order with real exponents have at most real zeros. We deduce several results that relate to impulsive controllability and sampled observability of finite-dimensional linear time-invariant dynamical systems. We prove that the initial state of a continuous-time linear time-invariant dynamical system of dimension

It poses technical difficulty to achieve distributed consensus tracking control with input quantizations and deception attacks for nonlinear multi-agent systems subject to mismatched parametric uncertainties via backstepping design. The underlying problem becomes even more complicated because ) the system contains mismatched uncertainties; ) the output becomes unavailable after attacks, making the

In this paper, disturbance rejection approaches are suggested to stabilize Korteweg–de Vries–Burgers (KdVB) equation under the averaged measurements. Here two approaches—active disturbance rejection control (ADRC) and disturbance observer-based control (DOBC), are introduced to reject the external unknown disturbance actively. The main challenging issue is to design the effective extended state observer

This paper addresses a nonsmooth aggregative game to control multiple noncooperative players, each with a nonsmooth cost function that depends not only on its own decision but also on some aggregate effect among all the agents. In addition, the decision of each player is restricted by private and coupling constraints. To address these concerns, a distributed generalized Nash equilibrium (GNE) seeking

This paper investigates the prescribed-time fully distributed Nash equilibrium seeking (PT-FDNES) problem for nonlinear multi-agent systems (MASs) over weight-unbalanced directed graphs (digraphs). To counterbalance unweighted communication flows caused by the unbalanced network, the temporal transformation technique is first introduced to derive a consensus-based algorithm for calculating the left

The opinion evolution problem is studied in this paper for stubborn individuals in cooperation–competition networks, where the individuals’ opinion dynamics is described by the Friedkin–Johnsen model and the competitive relationship between individuals is characterized by negative weights. Then the lifting approach is successfully applied to the Friedkin–Johnsen model to deal with the influence of

We consider the speed planning problem for a vehicle moving along an assigned trajectory, under maximum speed, tangential and lateral acceleration, and jerk constraints. The problem is a nonconvex one, where nonconvexity is due to jerk constraints. We propose a convex relaxation, and we present various theoretical properties. In particular, we show that the relaxation is exact under some assumptions

This paper revisits a classical adaptive control problem: adaptive state tracking control of a state-space plant model, and solves the open discrete-time state tracking model reference adaptive control problem. Adaptive state tracking control schemes for continuous-time systems have been reported in the literature, using a Lyapunov-algorithm based design and analysis procedure. Such a procedure has

We take a divide and conquer approach to design controllers for reachability problems given large-scale linear systems with polyhedral constraints on states, controls, and disturbances. Such systems are made of small subsystems with coupled dynamics. We treat the couplings as additional disturbances and use assume-guarantee (AG) contracts to characterize these disturbance sets. For each subsystem,

This paper proposes a robust formation control scheme for networked multi-tilt tricopter UAVs utilizing the Negative Imaginary (NI) and Positive Real (PR) theory. A Sliding Mode Control (SMC) scheme is designed for a multi-tilt tricopter to ensure stable hovering at a desired height. Then, a modified Subspace-based system identification algorithm is devised to identify a six-by-six NI model of the

This paper addresses distributed state estimation in a peer-to-peer heterogeneous sensor network characterized by varying qualities of local estimators. The proposed approach employs weighted Kullback–Leibler average of local posteriors, considering both and protocols to efficiently disseminate information throughout the network. Our consensus and flooding methods extend communication and fusion to

This paper deals with cooperative output tracking problems for heterogeneous networks of linear agents. To refine high-precision tracking performances of agents, a graph-based distributed learning control (DLC) law is proposed, for which a new bounded-initialization, bounded-updating (BIBU) stability property is explored under any bounded initial conditions. Moreover, a class of heterogeneous-to-homogeneous

We present a study of a class of closed Michaelis–Menten network models, which includes models of two categories of biochemical networks previously studied in the literature namely, processive and mixed mechanism phosphorylation futile cycle networks. The main focus of our study is on the uniqueness and stability of equilibrium points of this class of models. Firstly, we demonstrate that the total

The model reduction problem in the Loewner framework for port-Hamiltonian and network systems on graphs is studied. In particular, given a set of right-tangential interpolation data, the (subset of) left-tangential interpolation data that allow constructing an interpolant possessing a port-Hamiltonian structure is characterized. In addition, conditions under which an interpolant retains the underlying

This paper is concerned with identifying the instantaneous modal parameters of forced oscillatory systems with response-dependent generalized inertia (mass, inductance, or equivalent) based on their measured dynamics. An identification method is proposed, which is a variation of the ”FORCEVIB” method. The method utilizes analytic signal representation and the properties of the Hilbert transform to

This paper studies the leader–follower circumnavigation problem of two non-holonomic robots using just an angular velocity adjustment. Specifically, two robots orbit around a stationary target along a circle trajectory with a pre-specified spacing distance apart and velocity. However, two robots have no global positioning information and cannot localize the target. Instead, they can just obtain real-time

Leveraging recent results on bounded stabilization of linear plants using shifted equilibria, we propose a novel anti-windup scheme for linear input-saturated plants with (asymmetric) saturation limits. We show that, with open-loop plants involving a continuum of equilibria, the proposed anti-windup solution provides an unbounded estimate of the basin of attraction, thereby overcoming typical limits

This study aims to develop an efficient budget allocation procedure for the problem of selecting an optimal subset of designs from a finite number of alternative designs in stochastic environments. The optimal subset might contain more alternative designs beyond the Pareto optimal ones. In this study, we adopt the Pareto rank to measure the performance of each design and define the optimal subset.

In this paper, we study distributed constrained online convex optimization (OCO) problem in a system consisting of a parameter server and clients. Each client is associated with a local constraint function and time-varying local loss functions, which are disclosed sequentially. The clients seek to minimize the accumulated total loss subject to the total constraint by choosing sequential decisions based

In this paper, exponential stability of discrete-time Markov jump linear systems (MJLSs) with the Markov chain on a Borel space is studied, and bounded real lemmas (BRLs) are given. The work generalizes the results from the previous literature that considered only the Markov chain taking values in a countable set to the scenario of an uncountable set and provides unified approaches for describing exponential

How can non-communicating agents learn to share congested resources efficiently? This is a challenging task when the agents can access the same resource simultaneously (in contrast to multi-agent multi-armed bandit problems) and the resource valuations differ among agents. We present a fully distributed algorithm for learning to share in congested environments and prove that the agents’ regret with

In this paper, the resilient control for switched systems in the presence of deception attack and denial-of-service (DoS) attack is addressed. Due to the interaction of two kinds of attacks and the asynchronous phenomenon of controller mode and subsystem mode, the system dynamics becomes much more complex. A criterion is derived to ensure the mean square security level of the closed-loop system. This

This paper studies the density regulation problem for a large-scale robotic swarm with homogeneous agents. A novel robust model predictive mean-field control method is proposed to significantly improve the control performance and the algorithm scalability with the population size. To that end, a top-down control philosophy is first employed, under which the physical space is divided into finite disjoint

This work presents a novel decentralized control strategy with a guaranteed cost for bilinear multi-agent systems subjected to products between the state and the control input, state constraints, and limitations on the amplitude and total energy of the control action, which can prevent the consensus from reaching the desired value. We propose state feedback and switching control laws to deal with these

This paper deals with the leader–follower consensus control for a homogeneous, discrete-time, multi-agent system (MAS) with agents having non-minimum phase (NMP) zeros. Linear time-invariant (LTI) or single-rate distributed output feedback schemes in which, the controllers are not allowed to exchange their states with the neighbours, may fail to achieve this objective, if a certain speed of consensus

In this paper, the robust digital stabilization problem of nonlinear systems is investigated. In particular, a methodology for the design of robust quantized sampled-data stabilizers updated via an event-triggered mechanism is provided for time-varying control-affine nonlinear systems affected by actuation disturbances and measurement noises. The notion of time-varying steepest descent feedback (TSDF)

This paper proposes an extended interconnection and damping assignment passivity-based control technique to control the pressure dynamics in the fuel delivery subsystem of proton exchange membrane fuel cells. The fuel cell stack is a distributed parameter model which can be modeled by partial differential equations. In this paper, the segmentation concept is used to approximate the partial differential

This paper investigates the problem of resilient distributed state estimation for a linear system in multi-hop communication networks. First, we introduce a graph-theoretic property, called multiple-hop strong robustness, to formalize the notion of communication and measurement redundancy in multi-hop communication networks. Second, we propose multi-hop communication-based resilient distributed state

This paper proposes a cooperative pursuit strategy against a maneuvering target by introducing a cascaded optimization framework for assignment and guidance. The cooperative guidance algorithm, guaranteeing both encirclement and simultaneous attack constraints, is derived by optimizing the normal impact–direction and tangential impact–time control efforts, respectively. Then, by minimizing the performance

We present a novel -learning algorithm tailored to solve distributionally robust Markov decision problems where the corresponding ambiguity set of transition probabilities for the underlying Markov decision process is a Wasserstein ball around a (possibly estimated) reference measure. We prove convergence of the presented algorithm and provide several examples also using real data to illustrate both

This paper revisits classical work of Rauch et al. (1965) and develops a novel statistical method for maximum likelihood (ML) recursive state estimation in general state–space models. The new method is based on statistical estimation theory for incomplete information, which has been well developed primarily for ML parameter estimation (Dempster et al., 1977). Distributional identities for the posterior