Sliding-mode surface-based decentralized event-triggered control of partially unknown interconnected nonlinear systems via reinforcement learning

This paper studies a sliding-mode surface (SMS)-based decentralized event-triggered control problem for a class of partially unknown interconnected nonlinear systems within an identifier-critic neural networks framework. By assigning an especial cost function associated with SMS for each nominal subsystem, the original control issue is equivalently converted into designing a number of optimal controllers updating under an event-triggered manner, which considerably saves the communication resources. To derive these optimal event-based control policies, the corresponding Hamilton-Jacobi-Bellman equations are solved via the reinforcement learning algorithm. Different from traditional reinforcement learning algorithms, the identifier-critic network framework can obviate the requirement for the knowledge of system internal dynamics, and remove the errors generated by approximating actor networks. The weight vector in the critic network is updated through the gradient descent method and the experience replay technology, such that the persistence of excitation condition is relaxed. Under the proposed SMS-based decentralized control scheme, the considered system has a faster control response while minimizing the cost function. After that, according to the Lyapunov stability theory, all signals of the interconnected nonlinear systems are strictly proved to be bounded. Finally, the validity of the proposed control scheme is demonstrated via a simulation example.