Adaptive prescribed performance optimal control for strict-feedback nonlinear systems with input delay and input quantization

Abstract

In this paper, an adaptive optimal control strategy is proposed based on command filter technique for a class of strict-feedback nonlinear systems with input quantization and input delay, as well as prescribed performance. Utilizing dynamic surface control (DSC) and an error compensation mechanism, the impact of filtering errors on system performance is eliminated, and the difficulties of optimal control caused by input delay and input quantization are overcome. Based on identifier-critic-actor optimal architecture and by introducing positive definite functions with the application of gradient descent approach, the persistence excitation condition is relaxed. Through the cost function with prescribe performance and the compensation method in virtual control and adaptive law design, the system prescribed performance is achieved. The control scheme not only ensures that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB), but also minimizes the cost function and saves network resources. Finally, the simulation example is given to verify the effectiveness of the algorithm.