Event‐driven adaptive near‐optimal tracking control of the robot in aircraft skin inspection

Abstract

Abstract In this article, we discuss a near‐optimal tracking control problem (NOTCP) of robots used for inspecting aircraft skin with partially unknown systems, unmeasurable states, unknown disturbances, and unknown output delay. A novel observer based on an augmented neural network is designed to overcome the unknown disturbances, unknown output delay, and unknown internal states. An augmented system state, composed of the tracking error and reference system state, is proposed to introduce a new nonquadratic discounted performance function for the NOTCP. Due to the complexity in solving the Hamilton–Jacobi–Bellman equation, an online policy iteration is presented under the adaptive dynamic programming (ADP) framework. Unlike the traditional ADP, the event‐driven algorithm updates the control input only when the event is triggered, which reduces the computational cost and transmission load. Both the control policy and the observer are updated according to the developed triggering condition. Convergence to a near‐optimal control solution and the stability analysis of the proposed algorithm are shown through the Lyapunov candidate function for both the continuous and jump dynamics. The performance of the proposed algorithm is demonstrated by simulation.