Event-Triggered Prescribed Performance Intermittent Control for Constrained Nonlinear Systems via Adaptive Dynamic Programming

This paper investigates the event-triggered prescribed performance intermittent (ETPPI) control for constrained nonlinear systems via adaptive dynamic programming (ADP). By integrating the ADP algorithm and prescribed performance control, an optimal control policy is designed to ensure both system states and control inputs are constrained in prescribed boundaries while guaranteeing that system states converge within a prescribed time. Subsequently, an event-triggered intermittent control mechanism is established to reduce the consumption of computing and communication resources. Additionally, a critic neural network is built to approximate the solution of the Hamilton-Jacobi-Bellman equation, which enables the derivation of an approximate optimal control policy. Theoretical analysis demonstrates that under the developed ETPPI control approach, the closed-loop system achieves asymptotically stable and the Zeno behavior is precluded. Finally, the effectiveness of the developed approach is validated through two simulation cases.