Event-Triggered Robust Control for Uncertain Nonlinear Systems with Input Constraints

In this paper, an event-triggered robust control (ETRC) method is developed for input-constrained nonlinear systems with uncertainties in both the internal dynamics and the input matrix by using adaptive dynamic programming (ADP) technique. The ETRC problem is transformed into an event-triggered optimal regulation problem by constructing a modified value function composed of the regulation, the control input and two known upper-bounded functions. Moreover, a critic neural network (NN) is employed to approximate the value function for solving the event-triggered Hamilton-Jacobi-Bellman equation. The ETRC law is obtained by designing an event-triggered condition, which determines whether the robust control law should be updated. To relax the persistence of excitation condition, we introduce the experience replay technique to design a novel critic NN weight updating rule. In the developed ETRC method, the computational burden is reduced, the communication resource and the bandwidths are saved. Furthermore, both the approximate error of the critic NN weights and the closed-loop system states are ensured to be uniformly ultimately bounded by using the Lyapunov's direct method. Finally, a numerical example is utilized to demonstrate the effectiveness of the proposed ADP-based ETRC method.