International Journal of Adaptive Control and Signal Processing最新文献

In this paper, we study scalable $��\delta �$-level coherent state synchronization for multi-agent systems (MAS) where the agents are subject to bounded disturbances/noises. We propose a scale-free framework designed solely based on the knowledge of agent models and agnostic to the communication graph and the size of the network. We define the level of coherency for each agent as the norm of the weighted sum of the disagreement dynamics with its neighbors. The objective is to restrict the network's coherency level to $��\delta �$ without a-priori information about the disturbance.

The aim of this paper is to study the predefined-time synchronization of coupled competitive neural networks (NNs) as well as the impacts of time-varying delays on the predefined-time synchronization performance. First, the models for drive and response systems of coupled competitive NNs with and without coupling delays are established for the first time in this work. After that, the predefined-time synchronization of the proposed network models is investigated, and some new predefined-time synchronization conditions for the considered drive and response systems are derived by designing novel bilayer controllers and using some inequality techniques. It is worth mentioning that the desired convergence time can be adjusted by presetting the parameters in the controllers in advance. Moreover, the synchronization is achieved in the predefined time regardless of the initial value of the coupled competitive NNs. At last, the proposed synchronization conditions are validated through two examples.

The article investigates the problem of asynchronous resilient filter design for cyber-physical systems (CPSs) under multiple cyber-attacks, which are characterized by singular perturbation and stochastic semi-Markov jump systems (SPSS-MJSs) with a dynamic-memory event-triggered (DMET) scheme. Firstly, we consider the vulnerability of communication network and establish a unified cyber-attack framework to address the occurrences of deception attacks and denial-of-service (DoS) attacks, simultaneously. Then, we introduce the function of the memorizer and propose the DMET scheme, which can effectively save network resources in bandwidth-limited environments. Since the mode information of the resilient filter is often unavailable, we employ a hidden Markov model (HMM) to describe the asynchronous relationship between the modes of the original system and the constructed filter. Furthermore, we judiciously select a stochastic Lyapunov functional concerning the internal dynamic variable, which results in the stochastically admissible criterion for filtering error systems under complex cyber-attacks. The co-design approach is adopted to obtain the gains of the asynchronous resilient filter and the DMET scheme. Finally, a continuous stirred-tank reactor (CSTR) system is offered to validate the effectiveness of the developed method.

This paper will investigate the adaptive backstepping friction compensator for a class of servo control systems from a switched system point of view. Firstly, by introducing a convergence-invariant unbounded transformation, the position error bounds are incorporated into the original system. Secondly, an integral term is introduced to increase the robustness and the $��{�L�}_{�G�}�V�$-backstepping technique is utilized to deal with the sign-indefinite term. Then, an adaptive controller with a Coulomb friction estimator is obtained and the generated closed-loop error system has the upper block-triangle structure, which can be treated as a pair of cascade-connected systems. Further, the common Lyapunov function (CLF) is obtained. The objects of asymptotical tracking with position error constraints and identification of the Coulomb friction coefficient can be realized. By similar methods, we propose adaptive friction compensators for the viscous friction model and the Coulomb & viscous model. Finally, the simulation results demonstrate the advantages of the proposed methods.

This paper aims to solve the adaptive fuzzy inverse optimal output regulation problem for a class of uncertain nonlinear systems with the exosystem. Both unknown nonlinear functions and time-delay are considered here. The output regulation problem is first converted into a stabilization problem by employing the internal model principle. Then, fuzzy logic systems (FLSs) are employed to approximate unknown nonlinear functions. A new cost functional related to the internal model and time-delay is established. An auxiliary system connected to time-delay is constructed, and a new fuzzy state observer based on the auxiliary system is designed to estimate the unmeasured states. Further, by utilizing the adaptive backstepping control technique, the inverse optimal control method, and the Lyapunov–Krasovskii functional, a novel adaptive fuzzy inverse optimal output feedback controller and adaptive law are presented. It is also proved that all signals of the closed-loop system are globally uniformly ultimately bounded (UUB); the proposed controller can achieve inverse optimization by minimizing the cost functional. Finally, a simulation example is provided to prove the effectiveness of the method in this paper.

This article investigates the tracking control problem for a class of uncertain nonlinear systems with time-varying asymmetric state constraints of arbitrary time period. State constraints of arbitrary time period refer to the system states that are constrained during arbitrary finite time and are free for the other time (i.e., unconstrained), it is more common. This article addresses this issue for the first time. A novel shifting function is defined which moves any states or tracking errors out of the constraint area to the desired positions. Then, a barrier Lyapunov function is designed for the tracking error after shifting transformation, which ensures the satisfaction of state constraints. Dynamic surface control is used to avoid the high-order derivation of functions which solves the complexity problem caused by item explosion in backstepping design. Lastly, a simulation illustration is given to verify the effectiveness and outstanding features of the proposed method. It demonstrates that state constraints of arbitrary time period are satisfied, all signals in the closed-loop system are ultimately bounded, and the tracking error converges to an adjustable neighborhood of origin.

This article considers a distributed identification problem for linear stochastic systems whose input and output observations are scheduled by an adaptive event-triggered scheme. An event detector with time-varying thresholds is designed to control the transmission of measurements from the sensors to the estimators, which leads to that only a subset of input and output data is available for identification. The estimators exchange information over a network and cooperatively identify the unknown parameters. A distributed recursive identification algorithm under the event-triggered scheme is proposed based on the distributed stochastic approximation algorithm with expanding truncations (DSAAWET). Under mild assumptions, the strong consistency of the algorithm is proved, that is, the estimates generated from each estimator achieve consensus and converge to the true parameters with probability one. Finally, two numerical examples are provided to validate the theoretical results of the algorithm.

This article investigates the problem of event-triggered distributed resilient state estimator (ET-DRSE) for nonlinear cyber-physical systems (NCPSs) under hybrid cyberattacks. A stochastic model for hybrid cyberattacks is constructed by considering both false data injection (FDI) and Denial-of-Service (DoS) attacks. By proposing an event-triggered mechanism, the network burden is reduced by decreasing the transmission rate of measurement information. To improve the flexibility, a detector-based distributed resilient state estimator framework is derived. Coupled Riccati-like difference equations (CRDEs) are proposed to obtain an upper bound (UB) of the estimation error covariance (EEC) of every subsystem. An estimation gain is designed to minimize the UB upon the error covariance of the resilient estimator by recursively solving the CRDEs. It is proved that the estimation error is probabilistically bounded through the application of random analytic theory. Finally, the performance of the proposed ET-DRSE is verified through a numerical example and a chemical process example.

This article investigates the issue of finite-time tracking control for uncertain robotic manipulator systems with unknown actuator faults and shifting loads based upon a fuzzy sliding mode control strategy. A novel fuzzy adaptive sliding mode fault-tolerant control law is synthesized, where a fuzzy approximation algorithm is utilized to fit the unknown plant parameters, potential disturbances, and relational actuator faults, and the corresponding adaptive robust term is designed to estimate the unidentified boundary of the estimated errors, and the boundedness of all the relevant design parameters of the manipulator systems is ensured combining with the average dwell time mechanism of switched system control theory. Furthermore, the reachability of the pre-devised sliding surface and the finite-time convergence of tracking error are achieved under the presented controller design. Ultimately, simulation results exhibit the feasibility and superiority of the proposed algorithm.