An important issue in model-based control design is that an accurate dynamic model of the system is generally nonlinear, complex, and costly to obtain. This limits achievable control performance in practice. Gaussian Process (GP) based estimation of system models is an effective tool to learn unknown dynamics directly from input/output data. However, conventional GP-based control methods often ignore

This article proposes a constructive design of an input observer for linear time invariant systems, grounded on asymptotical left inversion. Based on necessary and sufficient conditions for its existence, such a design leads to a causal and asymptotically convergent input estimation, for any initial condition and any input. Such design is iterative and decomposed as a forward–backward algorithm. Starting

This paper proposes a passivity-based port-Hamiltonian (pH) framework for multi-agent displacement-based and rigid formation control and velocity tracking. The control law consists of two parts, where the internal feedback is to track the velocity and the external feedback is to achieve formation stabilization by steering variables of neighboring agents that prescribe the desired geometric shape. Regarding

This manuscript presents a sliding-mode control design on a general Lie group for fully-actuated simple mechanical systems. By equipping the state space with the structure of a Lie group and building a sliding subgroup immersed in the state space, the sliding mode is guaranteed to exist and therefore allows the proposed sliding-mode control on Lie groups to leverage the salient features of the sliding-mode

This technical communique studies decentralized state feedback control and stabilization for networked control systems with time delay. Unlike previous studies with conservative conditions — such as lower block triangular systems, uncorrelated process noises, and classical information structures — we investigate a more general coupled system with a non-classical d-step delayed state sharing pattern

This paper develops equivalent characterizations for input-to-state stability of discrete-time linear, time-varying (DTLTV) systems. Unlike prior results on input-to-state stability for discrete-time nonlinear time-varying systems, the characterizations provided in the present paper do not use Lyapunov methods. Furthermore, this paper develops necessary and sufficient conditions for input-to-state

This paper proposes a distributed gain-scheduled observer-based technique for the practical state consensus of linear parameter-varying (LPV) multi-agent systems (MAS). In the proposed approach, the effects of considering different scheduling parameters for each agent are modeled as an internal disturbance. By applying the concepts of the Lyapunov theory, sufficient conditions are obtained for the

This paper presents the digital redesign of the event-triggered sliding mode control, which emulates the analog (control) signal for a sampled data system. The proposed controller consists of a sensor module and an actuator module, communicating with each other intermittently on the occurrence of events at the sensor. Both of these modules comprise a signal generator that mimics the plant model exactly

Set-membership estimation over open sensor networks is a current research hotspot. It remains a risk of data leakage under highly open network channels. This paper focuses on establishing set-membership estimator design strategy under a privacy-preserving framework. Firstly, with the aid of state decomposition methods, a new group of set-membership estimators with privacy-preserving are originally

Outliers can contaminate the communication and measurement processes of many sensor network systems, which may be induced by environmental disturbances, model uncertainties, sensor faults or errors, subnetwork faults or malicious cyberattacks. Once the distributed set-membership filter (DSMF) is used into such sensor network systems with outliers for distributed state estimation, the estimation performance

This paper investigates the problem of H∞ filtering for a class of nonlinear discrete time systems under a dynamic event-triggered scheme. First, a new general form of event-triggered filters is considered so that the filtering error system achieves full decoupling from the so-called event-triggered transmission error, which is the major concern of H∞ filtering performance degradation caused by an

The formation of platoons, where groups of vehicles follow each other at close distances, has the potential to increase road capacity. In this paper, a decentralized control approach is presented that extends the well-known constant headway vehicle following approach to the two-dimensional case, i.e., lateral control is included in addition to the longitudinal control. The presented control scheme

This paper studies the privacy-preserving distributed optimization problem under limited communication, where each agent aims to keep its cost function private while minimizing the sum of all agents’ cost functions. To this end, we propose two differentially private distributed algorithms under compressed communication. We show that the proposed algorithms achieve sublinear convergence for smooth (possibly

This paper addresses the stabilization and trajectory-tracking problems for two classes of weakly coupled electromechanical systems. To this end, we formulate an energy-based model for these systems within the port-Hamiltonian framework. Then, we employ Lyapunov theory and the notion of contractive systems to develop control approaches in the port-Hamiltonian framework. Remarkably, these control methods

This paper focuses on the learning of Kalman gain of a finite-horizon Kalman filter with unknown noise covariance through the policy optimization method. Firstly, we reformulate the finite-horizon Kalman filter as an optimization problem featuring a doubly-summed objective function Secondly, we establish the global linear convergence of exact gradient descent method in the scenario where the model

This work investigates the security verification problem against covert learning attackers. These are attackers that do not know the supervisor model and thus may require passive learning by collecting observations of the system’s runs. From the attacker’s point of view, any supervisor consistent with the set of observations may have been deployed; thus, a successful attacker needs to remain covert

This paper addresses the solvability of linear mean-field (MF) forward–backward stochastic difference equations (FBSΔEs) associated with discrete-time MF linear quadratic (LQ) optimal control problems. First of all, the relationships are investigated among the concerned equations and three different types of FBSΔEs arising from the available literature. It is found that the various formulations of

This paper presents a fast projected primal–dual method for solving linear-quadratic optimal control problems. The computational efficiency comes from a heavy-ball acceleration and specific (sparse) choices of preconditioning matrices. To analyse convergence, we first assume that the weighing matrices in the linear quadratic optimal control problems are diagonal, allowing us to propose the preconditioning

This article proposes a closed-form adaptive tracking control approach for linear heat equations with unknown parameters to achieve full temperature profile tracking by leveraging Fourier regularization and bi-orthogonal series. A state predictor which copies the plant with state partial derivatives and unknown parameters replaced by their estimates is built and an adaptive law is designed to estimate

This paper explores the distributed tracking control problem for networked multi-input multi-output (MIMO) strict-feedback nonlinear systems under the influence of unpredictable reversed control direction faults. Based on an intentionally imposed and more lenient controllability condition, we establish a novel hierarchical prescribed performance control (PPC) design framework, comprising two layers:

In this paper, we develop the first result employing neural operators in adaptive PDE control, presented for a benchmark 1-D hyperbolic PDE with recirculation. Particularly, we introduce neural operators for approximating the mapping from the adaptive estimation of the plants’ functional coefficients to the corresponding controller gain kernel. This nonlinear mapping is computationally prohibitive

An Inertial Navigation System (INS) is a system that integrates acceleration and angular velocity readings from an Inertial Measurement Unit (IMU), along with other sensors such as Global Navigation Satellite Systems (GNSS) position, GNSS velocity, and magnetometer, to estimate the attitude, velocity, and position of a vehicle. This paper shows that the INS problem can be analysed using the automorphism

This paper investigates a finite-horizon two-player zero-sum risk-sensitive stochastic game in continuous-time Markov chains with Borel state and action spaces. The model accommodates unbounded reward rates, transition rates, and terminal reward functions, while permitting history-dependent policies. The risk metric is the exponential utility function. Under appropriate conditions, we establish the

This paper synthesizes anytime algorithms, in the form of continuous-time dynamical systems, to solve monotone variational inequalities. We introduce three algorithms that solve this problem: the projected monotone flow, the safe monotone flow, and the recursive safe monotone flow. The first two systems admit dual interpretations: either as projected dynamical systems or as dynamical systems controlled

This paper considers the estimation and prediction problems for large models with saturated output observations. Here large models are referred to models with a large or infinite number of unknown parameters. The investigation of such models appears to be necessary even for finite dimensional linear stochastic systems when the output observations are saturated or binary-valued, since the regressors

Conventional graphs capture pairwise interactions; by contrast, higher-order networks (hypergraphs, simplicial complexes) describe the interactions involving more parties, which have been rapidly applied to characterize a growing number of complex real-world systems. However, such dynamics evolving on higher-order networks modeled by hypergraphs require new mathematical tools to carry out rigorous

Geometric pooling of probability densities (pd) is an old but basic technique of the fusion of probabilistic knowledge. Among its many justification, the use of the axiomatic minimum relative entropy principle (MREP) is the simplest one. Up to now, however, the common choice of the pooling weights is unavailable. It is done by a range of techniques. Mostly, they are of a heuristic nature and often

We consider uncertain, MIMO, high-order, pure-feedback systems, affected by possibly aperiodic, time-dependent impulses at the state. The frequency of the impulsive sequence is bounded, yet the exact impulse time instants are unknown in advance. The proposed control solution is constructive and guarantees convergence of the tracking error to a residual set of pre-selected size, within a predefined

In this brief paper we propose a systematic method to construct approximate solutions to classes of state-feedback control problems involving stochastic nonlinear systems. The method avoids solving partial differential equations and instead requires only the solution of simpler algebraic equations. Exploiting a dynamic extension, we provide approximate solutions, with an exactly quantifiable level

In this paper, the zonotopic set-membership state estimation (SMSE) problem for nonlinear systems is investigated. To handle the nonlinear dynamics of the system, a semi-infinite programming (SIP) scheme based on zonotope analysis is established and solved. The SIP scheme aims to obtain a tight zonotope to enclose the nonlinear dynamics of the system. Subsequently, the robustness performance of the

Concurrent learning (CL) leverages strategic data collection and usage to deliver effective learning under conditions less demanding than persistency of excitation. Using a linear-in-the-parameters model to approximate a general discrete-time (DT) uncertainty, we develop a DT normalized recursive least squares-based CL algorithm, thereby mixing CL and least squares. Our algorithm not only preserves

This paper delves into an in-depth exploration of the Variable Projection (VP) algorithm, a powerful tool for solving separable nonlinear optimization problems across multiple domains, including system identification, image processing, and machine learning. We first establish a theoretical framework to examine the effect of the approximate treatment of the coupling relationship among parameters on

The problem of angle-constrained distributed formation control was studied recently for double integrator systems. In this paper, we further study the problem of angle-constrained distributed formation control with disturbance rejection for double integrator systems. The disturbances are in the class of trigonometric polynomial with arbitrary unknown amplitudes, unknown initial phases, and unknown

In this paper, the distributed state estimation of the target system and the distributed fault diagnosis of the sensor network are investigated for networked systems under jointly connected switching networks. In this scenario, to eliminate the effect of external disturbance, we first construct a distributed observer system consisting of N distributed unknown input observers (UIOs), each of which is

This paper addresses the problem of task-space robust regulation of robot manipulators subject to external disturbances. A velocity-free control law is proposed by combining the internal model principle and the passivity-based output-feedback control approach. The resulting controller not only ensures asymptotic convergence of the regulation error but also rejects unwanted external sinusoidal disturbances

This paper discusses the secure fusion estimation issue for cyber–physical systems subject to malicious attacks. Smart sensors equipped with computing modules transmit local posteriori estimations to remote fusion center through wireless communication channels. The transmission process is considered to be vulnerable to false data injection attacks. Therefore, to enhance the resilient of the estimator

This paper focuses on three critical aspects of designing distributed optimization algorithms in real-world scenarios: feasibility, convergence time, and applicability to unbalanced networks. A specific class of resource allocation problems (RAPs) are addressed with these challenges in mind. These RAPs occur over unbalanced digraphs, have strict time constraints, and require continuous resource-demand

In this paper, we will study the problem of stochastic Nash equilibrium seeking in an N-player non-cooperative game for players with general linear dynamics. Our focus is on minimizing the expectation value with respect to the distribution of uncertainties in players’ individual cost functions. We adopt two regularization approaches, the Tikhonov regularization scheme and the iterative proximal point

The “robustness of attacks” characterizes the responsiveness of attack strategies to time-varying defense strategies. When attack strategies lack robustness, once the defender changes its strategies, it will lead to ineffective actions by attackers. To address it, this paper designs the optimal stealthy robust attacks within the framework of the Stackelberg game, where the optimal robust attacks serve

This paper presents a novel Tracking Model Predictive Control (TMPC) strategy, specifically designed for managing systems that track targets with unpredictable (priori unknown) trajectories. Traditional Model Predictive Control (MPC) is adept at set point tracking; however, it struggles with maintaining recursive feasibility when faced with variable references. Our TMPC approach addresses this by introducing

This paper investigates the smooth finite-dimensional time-varying control problem for chained nonholonomic systems with distributed input delays. For the scalar distributed delay system, a smooth finite-dimensional nonhomogeneous controller is proposed. By using a smooth time-varying state transformation, a chained nonholonomic system with distributed input delay is transformed into a linear time-varying

This work focuses on the iterative learning model predictive control (ILMPC) design for nonlinear discrete-time batch systems. Different from the existing results, a novel efficient two-dimensional (2-D) ILMPC approach is firstly proposed based on the 2-D system theory, which is able to guarantee the H∞ tracking performance with lower computation load. Furthermore, based on the newly established event-triggered

This paper presents a minimax design approach based on model-free reinforcement learning (RL) to solve the robust linear quadratic regulator (LQR) problem. The proposed method derives a controller that guarantees stability and tends to be optimal as the data amount increases, even in the presence of unknown system dynamics. Initially, the robust LQR problem is transformed into a zero-sum differential

This brief deals with the asynchronous stabilization problem for discrete-time piecewise homogeneous Markov jump linear systems (MJLSs) with stochastic switching transition probabilities (TPs) that are governed by a higher-level Markov chain. Two practical scenarios are considered: (i) the modes of the system and its TP matrix cannot always be accurately detected, leading to the mismatched modes phenomenon;

In this paper, we study how the Koopman operator framework can be combined with kernel methods to effectively control nonlinear dynamical systems. While kernel methods have typically large computational requirements, we show how random subspaces (Nyström approximation) can be used to achieve huge computational savings while preserving accuracy. Our main technical contribution is deriving theoretical

Active techniques have been introduced to give better detectability performance for cyber-attack diagnosis in cyber–physical systems (CPS). In this paper, switching multiplicative watermarking is considered, whereby we propose an optimal design strategy to define switching filter parameters. Optimality is evaluated exploiting the so-called output-to-output gain of the closed-loop system, including

This paper presents the following research findings on a Boolean network (BN) and the invariant dual subspaces with respect to the BN. First, we establish a bijection between the dual subspaces over the BN’s state set X and the partitions of X. Furthermore, we prove that a dual subspace is M-invariant if and only if the associated partition of the BN’s state-transition graph is equitable (i.e., for

While event-triggered control theory guarantees system stability and simultaneously reduces control data transmissions between an embedded processor and a physical system, the resulting closed-loop system performance may deviate significantly from its ideal (i.e., non-event-triggered) closed-loop system performance. The contribution of this paper is to address this problem by proposing a corrective

A parameter estimation alike solution to the angular velocity estimation problem of rigid-body spacecraft is presented in this paper, where direct inertial vector measurements rather than attitude information are considered as available outputs. The main idea is to transform the original state estimation problem into an equivalent linear regression problem of an unknown time-varying parameter through

In this paper, the remote state estimation problem is addressed for a class of discrete time-varying nonlinear complex networks subject to non-Gaussian noises, uncertain inner coupling, and amplify-and-forward (AF) relays. The coupling strengths are unknown but belong to predefined intervals, and the measurement signals are transmitted to the remote estimator via AF relays with stochastic channel gains

Neural networks have become increasingly popular in controller design due to their versatility and efficiency. However, their integration into feedback systems can pose stability challenges, particularly in the presence of uncertainties. This work addresses the problem of certifying robust stability in neural network control systems with interval matrix uncertainties. Leveraging classical robust stability

Most existing prescribed-time (PT) control results focus on the stabilization/regulation problem, with only a few results considering the tracking control problem. In this paper, an adaptive PT tracking control method is proposed for a class of strict feedback nonlinear systems with linearly parametric uncertainties which can be non-vanishing and include both matched and mismatched parts. By using

We introduce a new class of numerical methods for solving McKean–Vlasov stochastic differential equations, which are relevant in the context of distribution-dependent or mean-field models, under super-linear growth conditions for both the drift and diffusion coefficients. Under certain non-globally Lipschitz conditions, the proposed numerical approaches have half-order convergence in the strong sense

In this paper, we develop a unifying and comprehensive framework for stochastic fixed-time input-to-state stability (SFT-ISS), whose contributions are characterized by the following novel features: (i) We introduce the original definition of stochastic fixed-time input-to-state stability and obtain three important properties. (ii) Some powerful mathematical tools including two SFT-ISS small-gain conditions

To better understand opinion dynamics on social networks, especially when antagonistic interactions exist, we propose a novel coevolution model of multidimensional opinions and coopetitive (cooperative–competitive) influence networks. In this model, agents update their opinions according to the designed multidimensional Altafini-type rule. Additionally, the asynchronous evolutionary dynamics of influence

This paper is about operator-theoretic methods for solving nonlinear stochastic optimal control problems to global optimality. These methods leverage on the convex duality between optimally controlled diffusion processes and Hamilton–Jacobi–Bellman (HJB) equations for nonlinear systems in an ergodic Hilbert–Sobolev space. In detail, a generalized Bakry–Emery condition is introduced under which one

This paper considers a class of thrust vectoring systems, which are nonlinear, overactuated, and time-invariant. We assume that the system is composed of two subsystems and there exist singular points around which the linearized system is uncontrollable. Furthermore, we assume that the system is stabilizable through a two-level control allocation. In this particular setting, we cannot do much with

This paper investigates the problem of privacy protection in distributed estimation for interconnected dynamic systems. The exchange of information between subsystems during weighted sum aggregation poses significant privacy risks to distributed estimation. To address these concerns, we propose a noise contamination mechanism for private matrix–vector multiplication and private weighted sum aggregation

The concept of “dynamic adaptation gain/learning rate” has been introduced in Landau et al. (2023) in order to accelerate the adaptation/learning transients in the context of adaptation/learning algorithms using constant scalar adaptation gain/learning rate. The present paper shows that inserting a ”dynamic adaptation gain/learning rate” into adaptation/learning algorithms with time varying matrix

This paper addresses the distributed cooperative robust output regulation problem of nonlinear multi-agent systems in normal form with nonidentical relative degrees. First, a new distributed event-triggered internal model-based observer is proposed to estimate the exosystem with uncertain parameters in its output. To ensure that the estimation error of the observer converges to 0 asymptotically without