Adaptive Output Feedback Dynamic Surface Control of Nonlinear Systems with Actuator Failures and Unmodeled Dynamics

In this paper, an adaptive dynamic surface control scheme is investigated for a class of output feedback nonlinear systems with actuator failures and unmodeled dynamics. Under the conditions that the failure time instants are unknown and the states of the system are unmeasured, an available dynamic signal is introduced to dominate the unmodeled dynamics, K-filters are designed to estimate the unavailable states and the neural networks are used to approximate the unknown continuous functions. Compared with the existing literature, the number of adjustable parameters is effectively reduced, and the assumption that the upper and lower bounds of unknown gains are known is relaxed. By theoretical analysis, the closed-loop system is shown to be semi-globally uniformly ultimately bounded, with the tracking error converging to a small neighborhood of the origin. A numerical example is provided to demonstrate the effectiveness of the proposed approach.