Observer based adaptive dynamic programming for fault tolerant control of a class of nonlinear systems

This paper develops a novel fault tolerant control (FTC) scheme for a class of nonlinear systems with actuator failures based on adaptive dynamic programming (ADP). The estimated actuator failure from a fault observer is utilized to construct an improved performance index function that reflects the failure, regulation and control simultaneously. By employing a proper performance index function, the FTC problem can be transformed into an optimal control problem. By using policy iteration, the Hamilton-Jacobi-Bellman equation can be solved by constructing a critic neural network. Then, the approximated optimal controller can be derived directly. The closed-loop system is guaranteed to be uniformly ultimately bounded via the Lyapunov stability theorem. The effectiveness of the developed FTC scheme is demonstrated by two simulation examples. The significant contribution of the proposed strategy lies in that the well-known ADP method is extended to solving the FTC problem.