Command filter and dynamic surface control technology based adaptive optimal control of uncertain nonlinear systems in strict‐feedback form

Abstract In this article, the problem of command filter and event‐triggered mechanism based adaptive neural optimal control is discussed for a class of nonlinear systems in the presence of unmodeled dynamics in strict‐feedback form. The overall controller design is composed of feedforward controller and feedback controller as well as event‐triggered controller design. In the first part, the feedforward controller is constructed by exploiting command filter and introducing the compensation error, and the first‐order filter in the classical dynamic surface control technology is replaced by utilizing the second‐order filter. In the second part, adaptive dynamic programming algorithm is used to estimate the unknown optimal index function and optimal control signal by the aid of the capability of neural networks. In the third part, based on the feedforward and feedback controllers, an adaptive event‐triggered control is developed to avoid the occurrence of Zeno behavior. All the signals in the controlled system are proved to be semi‐globally uniformly ultimately bounded through theoretical analysis. Two numerical examples are employed to verify the availability of the proposed method.