Static/dynamic event-triggered learning control for constrained nonlinear systems

This paper designs two novel event-triggered control (ETC) schemes based on the critic learning technique for constrained discrete-time nonlinear systems. First, starting from the stability of the constrained system, a static ETC method is developed to reduce the computational burden. Then, a nonnegative dynamic variable is introduced into the static event-triggered mechanism, so as to establish the dynamic ETC method, which further improves the resource utilization rate and possesses the anti-interference ability. Moreover, a speedy value iteration architecture is designed to obtain an initially admissible optimal control policy, which can ensure the normal execution of the designed ETC methods. Finally, two experimental examples are provided to illustrate the effectiveness and superiority of the developed schemes.