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

This paper is concerned with the hierarchical decentralized solution to mean field production output adjustment. We first introduce a mean field output adjustment model for many firms in a market, where the price is sticky and regulated by a government. Under a given policy of the regulator, we first tackle a centralized game problem, and obtain a system of coupled forward–backward stochastic differential

We consider the optimal transport problem over convex costs arising from optimal control of linear time-invariant(LTI) systems when the initial and target measures are assumed to be supported on the set of equilibrium points of the LTI system. In this case, the probability measures are singular with respect to the Lebesgue measure, thus not considered in previous results on optimal transport of linear

Vision-based cooperative motion estimation is an important problem for many multi-robot systems such as cooperative aerial target pursuit. This problem can be formulated as bearing-only cooperative motion estimation, where the visual measurement is modeled as a bearing vector pointing from the camera to the target. The conventional approaches for bearing-only cooperative estimation are mainly based

We present a novel nonlinear model predictive control (MPC) scheme with relaxed stability criteria, based on the idea of generalized discrete-time control Lyapunov functions. These functions need to satisfy an average descent over a finite window of time, rather than a descent at every time step. One feature of this scheme is that it allows for implementing a flexible number of control inputs in each

In this paper, we investigate the notion of entropy rate and its maximization for continuous-time time-homogeneous irreducible finite-state Markov chains. The definitions available in continuous-time suffer from an apparent paradox, as they do not properly account for the role of the average commutation frequency. In fact, we show that the entropy rate is the sum of a finite and an infinite component

In this work, we consider a group of n agents which interact with each other in a cooperative framework. A Laplacian-based model is proposed to govern the evolution of opinions in the group when the agents are subjected to external biases like agents’ traits, news, etc. The objective of the paper is to design a control input which leads to any desired opinion clustering even in the presence of external

Time delays pose unique challenges in control engineering, as inherent delays can introduce instabilities and compromise system performance. To overcome these complexities, the concept of predictors or compensators has been instrumental in stabilising closed-loop systems. Still, it requires an accurate delayed state estimation through the design and implementation of predictors. In this paper, we delve

This paper explores a class of distributed optimal coordination problems with coupled objectives, incorporating features of distributed optimization and non-cooperative game. In this context, the local objective functions of agents depend on their own decisions as well as the decisions made by other agents. The objective of the agents is to achieve optimal coordination by minimizing the team objective

We study the problem of computing robust controllable sets for discrete-time linear systems with additive uncertainty. We propose a tractable and scalable approach to inner- and outer-approximate robust controllable sets using constrained zonotopes, when the additive uncertainty set is a symmetric, convex, and compact set. Our least-squares-based approach uses novel closed-form approximations of the

We examine convergence properties of continuous-time variants of accelerated Forward–Backward (FB) and Douglas–Rachford (DR) splitting algorithms for nonsmooth composite optimization problems. When the objective function is given by the sum of a quadratic and a nonsmooth term, we establish accelerated sublinear and exponential convergence rates for convex and strongly convex problems, respectively

The dynamic control allocation problem for LTI systems is addressed in an uncertain setting. In the presence of unstructured uncertainties affecting the underlying plant, a completely data-driven strategy is envisioned to optimally allocate the control action in the presence of non-constant steady-state behavior of the plant, while leaving untouched the regulated output response induced by an a priori

For the enhancement of the transient stability of power systems, the key is to define a quantitative optimization formulation with system parameters as decision variables. In this paper, we model the disturbances by Gaussian noise and define a metric named Critical Escape Probability (CREP) based on the invariant probability measure of a linearized stochastic process. CREP characterizes the probability

Nonlinear state estimation with stochastic event-trigger and packet losses is studied in this paper. To handle the nonlinearity and the uncertainty of the available information, the recursive probability density functions (PDFs) are characterized as Gaussian. Then, an event-trigger and packet losses induced Gaussian filter (EPGF) and its Gaussian smoother (EPGS) are derived to develop a new Gaussian

This work considers stochastic operators in Hilbert spaces, and in particular, systems with stochastically time-varying input delays of a known probability distribution. Stochastic dissipativity and stability are defined from an operator-theoretic perspective, and the well-known open-loop dissipativity conditions for closed-loop/network stability are extended to the stochastic case. Criteria are derived

A two-player game-theoretic problem on resilient graphs in a multiagent consensus setting is formulated. An attacker is capable to disable some of the edges of the network with the objective to divide the agents into clusters by emitting jamming signals while, in response, the defender recovers some of the edges by increasing the transmission power for the communication signals. Specifically, we consider

This paper addresses non-convex constrained optimization problems that are characterized by a scalar complicating constraint. We propose an iterative bisection method for the dual problem (DualBi Algorithm) that recovers a feasible primal solution, with a performance that progressively improves throughout iterations. Application to multi-agent problems with a scalar coupling constraint results in a

This paper considers linear–quadratic non-cooperative non-zero-sum stochastic differential games with a regulator and analyzes the adaptive problem when the systems matrices are unknown to both the regulator and the players. It is a typical problem of game-based control systems(GBCS) introduced and studied recently, which have a hierarchical decision-making structure. The main purpose of the paper

Existing power and bit scheduling algorithms mostly focus on open-loop system performance, i.e., improving estimation accuracy. This paper focuses on the scheduling methods for the closed-loop Markov jump systems in the unreliable transmission environments to improve the system stability and save energy. First, a control unit including feedback controller and predictive controller is proposed which

We provide a systematic investigation of using physics-informed neural networks to compute Lyapunov functions. We encode Lyapunov conditions as a partial differential equation (PDE) and use this for training neural network Lyapunov functions. We analyze the analytical properties of the solutions to the Lyapunov and Zubov PDEs. In particular, we show that employing the Zubov equation in training neural

This paper presents a one-shot learning approach with performance and robustness guarantees for the linear quadratic regulator (LQR) control of stochastic linear systems. Even though data-based LQR control has been widely considered, existing results suffer either from data hungriness due to the inherently iterative nature of the optimization formulation (e.g., value learning or policy gradient reinforcement

We study the detection problem for a finite set of Markov decision processes (MDPs) where the MDPs have the same state and action spaces but possibly different probabilistic transition functions. Any one of these MDPs could be the model for some underlying controlled stochastic process, but it is unknown a priori which MDP is the ground truth. We investigate whether it is possible to asymptotically

Communication constraint is prominent under the networked architecture, for which the scheduling problem needs to be considered. This paper aims at validating scheduling-based stabilization for networked stochastic systems (NSSs) characterized by the presence of control-dependent noise. Remarkably, control-dependent noise has never been taken into account in the context of scheduling-based control

This paper studies the problem of 3-D relative-measurement-based leader–follower simultaneous distributed localization and formation tracking control. The position information is only available to the leaders, and the followers have inter-agent relative measurements and communication with their neighbors. The key contribution is the development of a weight-matrix-based position constraint, which can

This paper studies online noncooperative games with dynamic regrets. Existing online noncooperative games rely on the sublinear growth of the path-length of Nash equilibrium sequences when considering dynamic regrets, which implies that their cost functions cannot be rapidly time-varying. Moreover, most of related results depend on undirected communication graphs. However, in many engineering practices

We study an online mixed discrete and continuous optimization problem where a decision maker interacts with an unknown environment over T rounds. At each round, the decision maker needs to jointly choose a discrete action and a continuous action and receives a reward associated with the chosen actions. The decision maker seeks to maximize the accumulative reward after T rounds. We propose algorithms

This paper examines the robust stability of moving-horizon estimators for nonlinear discrete-time systems that are detectable in the sense of incremental input/output-to-state stability and are affected by disturbances. The estimate of a moving-horizon estimator is derived from the on-line solution of a least-squares minimization problem at each time instant. The resulting stability guarantees depend

This paper addresses the issue of delay- and its derivative-dependent stability of a linear system with a time-varying delay. Based on the sign of the delay derivative, the time-varying delay is divided into two modes, namely a monotone increasing mode and a monotone decreasing mode. Then the original delay system is described as a switched system with two modes. This description motivates to construct

Learning control methods have been widely enhanced by reinforcement learning, but it is challenging to analyze the effects of incorporating extra system information. This paper presents a novel multi-step framework that utilizes extra multi-step system information to solve optimal control problems. Within this framework, we establish and classify general multi-step value iteration (MsVI) algorithms

In this paper, we present a systematic procedure for robust model reference adaptive control design for uncertain square multiple-input multiple-output (MIMO) continuous-time linear time-invariant (LTI) systems that admit uniform vector relative degree of zero, under the assumptions of minimum phase and that the upper bounds for the observability indices of all measurement channels are known. We assume

The Kuramoto model is essential for studying synchronization. In this work, we present sufficient conditions for global synchronization in networks of heterogeneous Kuramoto oscillators in the absence of homoclinic and heteroclinic cycles. The result is established by constructing a suitable Leonov function candidate for the Kuramoto model, which provides sufficient conditions for almost global synchronization

This paper presents a novel approach towards synchronization analysis of nonlinear networked systems, directionally coupled via a generic network topology, under asynchronous, aperiodic sampled-data linear coupling. The synchronization dynamics of the networked system is remodelled as a feedback-interconnection of an operator that captures the continuous-time synchronization dynamics, i.e., in the

This paper focuses on the formation tracking control problem of multiple unmanned aerial vehicles (UAVs) interconnected through a directed communication graph. The objective is to ensure that the vehicles attain a predetermined geometric configuration while simultaneously tracking a dynamic virtual leader in the presence of unexpected actuator faults. A novel distributed model predictive control (MPC)

This paper designs scheduling schemes for a sensor with multiple power levels to minimize estimation error over a finite time. Under the sensor energy constraint, two algorithms are proposed to design scheduling schemes. One is a dynamic programming algorithm that constructs optimal scheduling schemes, but it is time-consuming and sensitive to the initial estimation error covariance. To address this

This paper deals with the fault-tolerant dynamic output feedback (DOF) control of linear parameter-varying (LPV) systems via reconfiguration blocks. The main contributions of this work are twofold. The first one is the proposal of an enhanced condition for performing a DOF control design for LPV systems. The second one is the design of a reconfiguration block that is inserted between the faulty plant

We employ the recent performance-barrier event-triggered control (P-ETC) for achieving global exponential convergence of a class of reaction–diffusion PDEs via PDE backstepping control. Rather than insisting on a strictly monotonic decrease of the Lyapunov function for the closed-loop system, P-ETC allows the Lyapunov function to increase as long as it remains below an acceptable performance-barrier

We derive time and energy-optimal policies for a Connected Autonomous Vehicle (CAV) to execute lane change maneuvers in mixed traffic, i.e., in the presence of both CAVs and Human Driven Vehicles (HDVs). These policies are also shown to be robust with respect to the unpredictable behavior of HDVs by exploiting CAV cooperation which can eliminate or greatly reduce the interaction between CAVs and HDVs

The stochastic linear–quadratic regulator problem subject to Gaussian disturbances is well known and usually addressed via a moment-based reformulation. Here, we leverage polynomial chaos expansions, which model random variables via series expansions in a suitable L2 probability space, to tackle the non-Gaussian case. We present the optimal solutions for finite and infinite horizons and we analyze

Network coherence generally refers to the emergence of simple aggregated dynamical behaviors, despite heterogeneity in the dynamics of the subsystems that constitute the network. In this paper, we develop a general frequency domain framework to analyze and quantify the level of network coherence that a system exhibits by relating coherence with a low-rank property of the system’s input–output response

In this paper, we study the absolute exponential stability of the Korteweg–de Vries–Burgers equation under two distinct types of nonlinear boundary position feedback controls. We propose criteria that adhere to the sector-bounded and parabolic-restricted conditions, thereby encompassing a broad spectrum of nonlinear controllers. For each of these two nonlinear control strategies, we establish the well-posedness

In this paper we consider the problem of leaderless consensus for networks of fully actuated Euler–Lagrange agents perturbed by unknown additive disturbances. The network is an undirected weighted graph with time delays. The proposed controller has a PD structure that incorporates the estimate of the unknown time varying disturbance, which is included in the control signal to cancel it. To the best

To mitigate the impact of infectious diseases spread, feedback decision that effectively adjusts the control amount making use of current data has been anticipated. In general, feedback control design is based on models, which are inherently subject to inaccuracy and uncertainty. Control theory seeks robustness guarantees that do not rely on the model perfection which is usually required for prediction

This paper introduces two innovative control schemes, event-triggered extremum seeking control (ET-ESC) and periodic event-triggered extremum seeking control (PET-ESC), designed for real-time optimizing multivariable systems. The classical extremum seeking control is augmented with static and dynamic triggering conditions to enable aperiodic and less frequent updates of the system’s input signals,

This paper analyzes the consensus problem for agents modeled with any number of integrators when these are subject to constant time-delays and interact over a directed network. Concretely, it provides convergence criteria that are used to devise a systematic methodology for designing the coupling gains of the consensus protocols. Such design is performed in a way that the multi-agent system attains

Stabilization of a coupled system consisting of a parabolic partial differential equation and an elliptic partial differential equation is considered. Even in a situation where the parabolic equation is exponentially stable on its own, the coupling between the two equations can cause instability in the overall system. A backstepping approach is used to derive a single boundary control input that stabilizes

This paper considers the classical Susceptible–Infected–Susceptible (SIS) network epidemic model, which describes a disease spreading through n nodes, with the network links governing the possible transmission pathways of the disease between nodes. We consider feedback control to eliminate the disease, focusing especially on scenarios where the disease would otherwise persist in an uncontrolled network

This paper addresses the stabilization of linear systems with multiple time-varying input delays. In scenarios where neither the exact delays information nor their bound is known, we propose a class of linear time-varying state feedback controllers by using the solution to a parametric Lyapunov equation (PLE). By leveraging the properties of the solution to the PLE and constructing a time-varying

This paper develops new adaptive output tracking control schemes with the reference output signal generated from an unknown reference system whose output derivatives are also unknown. To deal with such reference system uncertainties, an expanded adaptive controller structure is developed to include a parametrized estimator of an equivalent reference input signal. Without using the knowledge of the

This paper presents an adaptive geometric control method for dynamic-model-free attitude tracking on the manifold of 3D rotations (SO(3)). Utilizing well-established definitions of attitude errors on SO(3), we develop a general control-affine linear error system. The input to this system is implicitly approximated by a kinematics-informed neural network (NN), which serves as the controller. The weights

Opacity is a general framework modeling security properties of systems interacting with a passive attacker. Initial-and-final-state opacity (IFO) generalizes the classical notions of opacity, such as current-state opacity and initial-state opacity. In IFO, the secret is whether the system evolved from a given initial state to a given final state or not. There are two algorithms for IFO verification

This paper explores optimal integrity attacks with varying data access to innovations transmitted from smart sensors to remote state estimators. The data access refers to attackers’ capability or authorization to eavesdrop on and tamper with raw data packets. Unlike prior studies where the innovations were entirely intercepted and modified by attackers, different restrictions on data access are considered

In this paper, the problem of secure recursive state estimation is addressed for a networked linear system over a relay channel. We consider the scenario where the transmitted signals and the internal state of the relay node might be intercepted by potential eavesdroppers. To prevent the system states from being inferred by potential eavesdroppers via overheard measurement signals, an encryption–decryption

The stochastic H∞ control is studied for a linear stochastic Itô system with an unknown system model. It is known that the linear stochastic H∞ control issue can be transformed into the problem of solving a so-called generalized algebraic Riccati equation (GARE), which is a nonlinear equation that is typically difficult to solve analytically. Worse, model-based techniques cannot be utilized to approximately

To assess the quality of a probabilistic prediction for stochastic dynamical systems (SDSs), scoring rules assign a numerical score based on the predictive distribution and the measured state. In this paper, we propose an ϵ-logarithm score that generalizes the celebrated logarithm score by considering a neighborhood with radius ϵ. We characterize the probabilistic predictability of an SDS by optimizing

This paper addresses output regulation for a Timoshenko beam with unknown disturbances and references emanating from an exosystem. By transforming all uncertainties into measurable errors, we are able to estimate approximately the unknown references via a beam observer. However, this alone is insufficient to fully address the uncertainties inherent in state feedback controls. To bridge this gap, we

Islanded microgrids face some challenges in maintaining stable frequency and sharing proper power among distributed generators (DGs) in the presence of external disturbances. This paper develops a novel fully distributed approach to achieve accelerated secondary frequency regulation (FR) and active power sharing (APS) in islanded microgrids, which enhances system performance and robustness against

This paper delves into an open problem within the field of optimal control: the strong well-posedness of the free-endpoint regular indefinite linear quadratic optimal control (LQ). The problem is closely intertwined with the existence of a solution possessing specific properties to an algebraic Riccati equation or inequality. In this paper, some explicit necessary and/or sufficient conditions for the

We study general discrete-time Mean–Variance problems in a non-Markovian setting. The utility is a general, continuous function which may depend on the entire history of the process. It contains many recursive utility functions with non-linear aggregator as special cases. Under some continuity and compactness assumptions on the model data, we establish the existence of persistently optimal deterministic

This paper addresses smooth constrained optimization problems with unknown objective and constraint functions The main goal of this work is to generate a sequence of feasible (herein, referred to as safe) primal–dual pairs converging towards a KKT pair. Assuming to have prior knowledge on the smoothness of the unknown functions, we propose a novel zeroth-order method that iteratively computes quadratic