This paper concentrates on network congestion and security issue for networked control systems under the hybrid attacks. The hybrid attacks including deception attack, replay attack and denial‐of‐service attack are modelled as Bernoulli random process. A general form adaptive event‐triggered scheme (AETS) under the hybrid attacks is designed to alleviate the network congestion and save communication resources utilizing adaptive threshold and the weighted average of data packets. Meanwhile, the security issue under the hybrid attacks is addressed by a dynamic output feedback controller (DOFC) based on the AETS. Moreover, the sufficient conditions are obtained by a piecewise Lyapunov function to guarantee that the closed‐loop system is exponentially mean‐square stable. A practical experiment on networked motor control system verifies the effectiveness of the proposed scheme. The proposed scheme can not only save communication resources to further alleviate network congestion, but also defense the hybrid attacks in the network.

An adaptive event‐triggered dynamic output feedback control for networked control systems is proposed. The proposed scheme can not only save communication resources to further alleviate network congestion, but also defense the hybrid attacks in the network. image

This paper studies the active fault tolerant load frequency control of multi‐area power systems with electric vehicles under deception attacks. An integrated design of fault estimation and fault‐tolerant control is proposed to guarantee the stability of the system under sensor faults and deception attacks. Considering the uncertainty caused by the demand of the owner and the state of the battery, a multi‐area power system model is proposed. Then, an active fault tolerant load frequency control scheme is designed. The proportional‐derivative sliding mode observer is used to estimate the fault and system status in real‐time. During the fault occurrence, the estimated value obtained by the observer is utilized to design the controller without any fault diagnosis scheme, which simplifies the controller design process. A sufficient Lyapunov‐Krasovskii criterion is derived to ensure the stability performance of the multi‐area power system. Finally, simulation examples are provided for a three‐area power system contains electric vehicles, and the results prove the correctness and feasibility of the proposed fault‐tolerant control scheme.

The proportional‐derivative sliding mode observer is used to estimate the fault and system status in real‐time. During the fault occurrence, the estimated value obtained by the observer is utilized to design the controller without any fault diagnosis scheme, which simplifies the controller design process. A sufficient Lyapunov‐Krasovskii criterion is derived to ensure the stability performance of the multi‐area power system.image

This paper presents a study of the cooperative optimal swarm control problem for two‐order multi‐agent systems with partially unknown nonlinear functions. Unlike traditional approaches that consider a single error, this paper proposes to use multi‐order errors in the performance index function to achieve optimal control performance. Additionally, different proportional coefficients are assigned to illustrate the varying influences of each sequence error, and a two‐order cooperative (TOC)performance index function is designed. To address the influence of unknown nonlinear functions, a swarm control system based on sliding mode control with an actor‐critic network is constructed, which increases the applicability of the proposed method to a variety of dynamic models. Furthermore, to alleviate the computational pressure caused by the multi‐order errors in the TOC performance index function, a new reinforcement learning (RL)‐based sliding mode swarm controller is designed. The stability of the proposed controller is demonstrated using the Lyapunov function. Finally, the control model and control rate are applied to a quadrotor unmanned aerial vehicle system, and simulation results demonstrate that the multi‐agent systems can effectively achieve swarm control.

Impact Statement: This paper proposes a reinforcement learning‐based sliding mode control strategy for the cooperative optimal swarm control problem, where the nonlinear functions of two‐order multi‐agent systems are only partially known. In addition, we also propose a cooperative performance index function, which takes into account multi‐order errors for optimizing the performance. This contribution is significant for research in sliding mode control strategies and error co‐optimization.

In this paper, we propose a reinforcement learning based sliding mode control strategy for the cooperative optimal swarm control problem where the nonlinear functions of two‐order multi‐agent systems are partially unknown. In addition, we also propose a two‐order cooperative performance index function, the performance function can be optimized according to the multi‐order errors at the same time to achieve the purpose of cooperative optimization. This article is very helpful for the research of sliding mode control strategy and error co‐optimization.image

This paper deals with the optimal control problem for partially observed leader–follower stochastic differential game. By virtue of the classical variational method and Girsanov's theorem, the stochastic maximum principles for the follower under one type of partially observed case and for the leader under the complete information structure are derived. As applications, two partially observed cases are considered for the linear–quadratic models. Then by the stochastic filtering technique, the optimal feedback controls for the follower and the leader are represented by the new stochastic Riccati equations.

Here, we study the maximum principles for partially observed leader–follower stochastic differential game. By using the the classical variational method and Girsanov's theorem, we obtain the maximum principle for the follower and the leader. Numerical analysis is given to illustrate the theoretical results.image

This paper is concerned with optimal security problem of cyber‐physical systems with state attacks. Consider a linear physical system, assuming that the control input signal of the network layer is vulnerable to state attack. For a system that cannot measure all states, by combining output integral sliding mode, robust observer, and adaptive methods, an adaptive output integral sliding mode control method is proposed to maintain the safe operation of cyber physical systems under state attacks. Moreover, by optimizing the linear control gain matrix, the control strategy the authors designed can use the minimum cost to compensate the impact on the CPS system. Different from the existing results, (1) It is assumed that the system information of the attack signal is not fully known, we assume that the attacker can only measure a small amount of state information. (2) Not only the stability of ideal sliding mode is proved, but also the upper bound of the augmented state composed of system states and the error dynamic is given. (3) The sliding mode compensator can eliminate the impact of state attack, on the one hand, the damage caused by the state attack has been eliminated by using the upper bound of error, and on the other hand, the cost is reduced by optimizing the linear control gain. Finally, a power system with 3 generators and 6 buses is used to prove the effectiveness of the adaptive output integral sliding mode control scheme.

This paper focuses on the security problem of cyber‐physical systems with state attacks. Consider a linear physical system, assuming that the control input signal of the network layer is vulnerable to cyber attacks. For a system that cannot measure all states, by combining output integral sliding mode, robust observer, and adaptive methods, an adaptive output integral sliding mode control method is proposed to maintain the safe operation of cyber physical systems under process attacks. Moreover, by using optimal control theory, the linear control law can minimize the upper bound of system performance.image