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This paper develops a novel guaranteed cost control (GCC) approach under the event-triggered mechanism for completely unknown systems using integral reinforcement learning (IRL) algorithm. First, based on the adaptive dynamic programming (ADP) method, the GCC problem is addressed by transforming it into the optimal control problem. Second, without using the accurate information of system dynamics, a model-free data-based GCC approach is designed via IRL algorithm. Moreover, for the purpose of reducing the waste of communication resources, a GCC algorithm is presented under the event-triggered mechanism for completely unknown system by utilizing the explorized IRL algorithm. The critic–actor–disturbance neural networks (NNs) are applied to approximate near optimal solution. In addition, the weight estimations of NNs are tuned synchronously according to the designed novel triggering condition. Furthermore, the stability analysis of the controlled system is given by utilizing the Lyapunov principle. Finally, the simulation results are presented to verify the feasibility of the designed approach. [ABSTRACT FROM AUTHOR]

This article presents a novel event-driven guaranteed cost control method for nonlinear systems subject to actuator faults. For the purpose of handling the problem of actuator faults and obtaining the event-driven approximate optimal guaranteed cost control approach for general nonlinear dynamics, the reinforcement learning (RL) algorithm is utilized to develop a sliding-mode control (SMC) strategy. To begin with, the unknown faults can be estimated by designing a fault observer. Meanwhile, an SMC technique is presented aiming at countering the effect of abrupt faults. In addition, the optimal performance of the equivalent sliding mode dynamics is considered, then an event-driven guaranteed cost control mechanism is implemented by using RL principle. In the control process, a general cost function, which has a simpler structure, is given to reduce the computation complexity. At the same time, a modified cost function is approximated to obtain optimal guaranteed cost control by using a single critic neural network (NN). In addition, a modified weight update law for critic NN is presented to relax the persistence of excitation (PE) condition. Moreover, a newly triggering condition, which is easy to be implemented, is designed, and the critic NN update law makes sure that the system states are stable. Furthermore, in light of the Lyapunov analysis, it is demonstrated that the developed event-driven control method guarantees the uniformly ultimately bounded (UUB) property of all the signals. Finally, three simulation results are given to validate the designed control method. [ABSTRACT FROM AUTHOR]

• Firstly, a novel FTC strategy involved with event-triggered mechanism and ISMC method is proposed for nonlinear systems subject to actuator failures. The proposed control method can make the nonlinear systems stable, as well as lower the frequency of network communication of the controlled systems, and therefore avoid the waste of system resources. • Secondly, inspired by Vrabie and Lewis (2009)[35] and Liu et al. (2020)[36], the proposed control method is presented to drive an event-based H ∞ control policy by applying RL algorithm without requiring a stable initial control law, which is necessary in the implemen-tation of event-triggered RL-based control approaches developed in Adib and Braun (2019)[25], Liu et al. (2019)[29], Wang et al. (2019)[33] and Vrabie et al. (2009)[34]. • Thirdly, since the phenomenon of input constraints often exists in nonlinear practical control systems, different from Liu et al. (2019)[29], Liu et al. (2021)[37], Han et al. (2020)[38] and Liu et al. (2020)[39], the event-triggered H ∞ control strategies is developed for nonlinear system dynamics subject to actuator failures. This paper investigates guaranteed cost control (GCC) problem for nonlinear systems subject to input constraints and disturbances by utilizing the reinforcement-learning (RL) algorithm. Firstly, by establishing a modified Hamilton–Jacobi–Bellman (HJI) equation, which is difficult to be solved, a model-based policy iteration (PI) GCC algorithm is designed for input-constrained nonlinear systems with disturbances. Moreover, without requiring any knowledge of system dynamics, by designing an auxiliary system with a control law and an auxiliary disturbance policy, an online model-free GCC approach is developed by utilizing integral reinforcement learning (IRL) algorithm. To implement the proposed control algorithm, the actor and disturbance NNs are constructed to approximate the optimal control input and worst-case disturbance policy, while the critic NN is utilized to approximate optimal value function. Further, a synchronization weight update law is developed to minimize the NN approximation residual errors. The asymptotic stability of controlled systems is analyzed by applying the Lyapunov's method. Finally, the effectiveness and feasibility of the proposed control method are verified by two nonlinear simulation examples. [ABSTRACT FROM AUTHOR]