Adaptive dynamic programming-based event-triggered optimal tracking control

In this article, an event-triggered constrained optimal tracking control algorithm using integral reinforcement learning (IRL) is developed. First, the constrained optimal tracking control problem is transformed into an optimal regulation problem by employing an augmented system with a discounted value function. Then, IRL is introduced to solve the Hamilton-Jacobi-Bellman equation, where the drift dynamics and reference dynamics are not required. The learning of neural network weights is event-triggered and there is no restriction on the initial control to be admissible. The involvement of event-triggering mechanism alleviates the pressure of data transmission on the network to some extent, which is suitable for control systems with limited computational and communication resources. Moreover, the nonexistence of Zeno behavior and the stability of the impulsive system are proved, respectively. Finally, the application of the algorithm on a mass-spring-damper system verifies its effectiveness.