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This study concentrates on the finite-time protocol-based stabilization for networked unmanned marine vehicle (UMV) systems with denial-of-service (DoS) attacks. To save limited communication resources, a novel event-driven protocol is proposed, in which Q-learning mechanism is adopted to adjust the threshold of the driven condition intelligently. In light of aperiodic DoS attacks, the corresponding switched system is constructed in light of the average dwell time strategy. Employing switching Lyapunov functional, sufficient conditions are made for finite-time boundedness of the UMV systems with disturbance, respectively, in which the relationship among the DoS parameter, the triggered parameter, the finite-time parameter, and the sampling period is accurately characterized. Moreover, the co-design approach of driven parameter and controller gain is proposed for the corresponding UMV systems. Finally, the benchmark UMV systems are shown to verify the effectiveness of the proposed strategy.

This article addresses the stability and optimal control problem (OCP) for nonlinear Itô stochastic Markov jump systems (NISMJSs) by employing the Takagi–Sugeno (T–S) fuzzy model. First, the NISMJSs undergo transformation through T–S fuzzy model into a class of Itô stochastic Markov jump fuzzy systems (ISMJFSs), which is a composite of a set of linear subsystems. Then, based on the Lyapunov stability theorem, a sufficient condition is provided for establishing the mean-square stability of ISMJFSs. For solving the OCP, the most commonly used method is policy iteration (PI), but it requires an initial stabilizing control policy to be given, which relies on precise information about the system dynamics. To remove this limitation, we introduce hybrid iteration (HI) to design the optimal controller for the Itô stochastic systems for the first time without requiring the initial stabilizing control gain, and then the stability and convergence of the offline HI algorithm for the ISMJFSs are proven. Moreover, by incorporating reinforcement learning (RL) techniques, we develop an online HI algorithm to successfully solve the OCP for ISMJFSs with unknown system dynamics matrices, which initializes the algorithm without the need for a set of initial stabilizing control gain matrices. Finally, the efficiency of the proposed methods is illustrated through simulation results using the RLC electric circuit.

This article focuses on fuzzy structural adaptive optimal control issue of discrete-time nonlinear complex networks (CNs) via adopting the reinforcement learning (RL) and Takagi–Sugeno fuzzy modeling approaches, where the control gains are subjected to structured constraints. In accordance with the Bellman optimality theory, the modified fuzzy coupled algebraic Riccati equations (CAREs) are constructed for discrete-time fuzzy CNs, while the modified fuzzy CAREs are difficult to solve directly through mathematical approaches. Then, a model-based offline learning iteration algorithm is developed to solve the modified fuzzy CAREs, where the network dynamics information is needed. Moreover, a novel data-driven off-policy RL algorithm is given to compute the modified fuzzy CAREs, and the structural optimal solutions can be obtained directly by using the collected state and input data in the absence of the network dynamics information. Furthermore, the convergence proofs of the presented learning algorithms are provided. In the end, the validity and practicability of the theoretical results are explicated via two numerical simulations.

This article focuses on solving the secure control problem by developing a novel resilient hybrid learning scheme for discrete-time Markov jump cyber-physical systems with malicious attacks. Within the zero-sum game framework, the secure control problem is converted into solving a set of game coupled algebraic Riccati equations. However, it contains the coupling terms arising from the Markov jump parameters, which are difficult to solve. To address this issue, we propose a framework for parallel reinforcement learning. Thereafter, a model-based resilient hybrid learning scheme is first designed to obtain the optimal policies, where the system dynamics are required during the learning process. Furthermore, a novel online model-free resilient hybrid learning scheme combining the advantages of value iteration and policy iteration is proposed without using the system dynamics. Besides, the convergence of the proposed hybrid learning schemes is discussed. Eventually, the effectiveness of the designed algorithms is demonstrated with the inverted pendulum model.

A novel reinforcement learning-based predefined-time tracking control scheme with prescribed performance is presented in this article for nonlinear systems in the presence of external disturbances. First, by employing the backstepping strategy, an adaptive optimized controller is developed under the identifier-critic–actor framework. Therein, the unknown nonlinear dynamics and the system control behavior can be learned effectively through neural networks. Moreover, aiming at obtaining the preset tracking performance, the prescribed performance control is integrated with the predefined-time control. In contrast to previous studies, the proposed scheme can not only constrain the tracking error rapidly to a prearranged vicinity of origin, but also ensure that the upper bound of convergence time can be adjusted in advance via a separate control parameter. In terms of the predefined-time stability theory, the boundedness of all system states can be proven within a predefined time. Finally, the availability and improved performances of the proposed control scheme are demonstrated by a numerical example and a single-link manipulator example.

This article is concerned with the proportional-integral observer-based distributed cooperative voltage control issue of the networked islanded microgrid subject to probabilistic communication delay. Due to the fact that the reference signal is usually received by partial distributed generators, the system model with a constant reference signal in some existing results is difficult for control synthesis. To solve this problem, a distributed control law based on the measured voltage is designed to estimate the reference signals for all distributed generators. Then, by treating the deviation between the estimated and measured voltages as the small signal, a novel small-signal model of the networked microgrid voltage control system is established. With the utilization of the probabilistic feature of communication delay, a distribution-dependent delay handling manner is taken into account. To improve the estimation accuracy of the system state, a proportional-integral observer is adopted based on the local measured voltage. With the aid of Lyapunov theory and linear matrix inequality technology, sufficient criteria are proposed for co-designing the controller and observer gains to guarantee that the reference voltage is tracked by all distributed generators. Lastly, some simulation results are carried out to manifest the merits of the developed strategy.

This article tackles the predefined-time control problem for nonlinear systems subject to preassigned performance metrics (PPMs) and state constraints. To meet the PPMs and state constraints, the preassigned performance control-based barrier Lyapunov functions are designated to acquire the performance metrics and the satisfaction of state constraints. The proposed control approach combines the predefined-time control with the recursive design of command-filter backstepping to achieve the preset convergence time based on designing indicators while also ensuring the satisfaction of the desired PPMs and constraint conditions. In addition, the command-filter design eliminates the requirement of proving the continuity of high-order derivatives of the virtual control signals, resulting in a significant reduction in the complexity of stability analyses related to predefined-time convergence of the closed-loop signals. The stability analyses and simulation results of two examples provide support for the effectiveness and applicability of the proposed command-filter predefined-time control approach.

This article presents a novel command-filter-based finite-time fuzzy preassigned performance adaptive control approach for nonlinear systems with exogenous disturbances and asymmetric time-varying constraints. Combining the asymmetric barrier Lyapunov function (ABLF) with preassigned performance control (PPC), it is ensured that the state $x_{1}$ satisfies the required asymmetric time-varying state constraints (ATSCs), and the system output y can track the desired signal with achieving preassigned performance indices (PPIs). The command-filter-based control design not only circumvents the computing complexity in the backstepping but also reduces the conservativeness of the assumption regarding the desired signal and the time-varying asymmetric constraints. Additionally, the nonlinear disturbance observer method (NDOM) is employed to effectively estimate unknown exogenous disturbances and enhance the robustness of the closed-loop system. Through rigorous theoretical analysis, the proposed finite-time command filter-based control method effectively ensures that all variables are bounded within a finite time. The practicality and feasibility of the proposed approach are further validated by simulation results.