Adaptive output feedback dynamic surface control of multi-input single-output systems

In this paper, an adaptive dynamic surface control scheme is proposed for a class of multi-input single-output (MISO) nonlinear systems with unmodeled dynamics. Under the condition that the states of the systems are unmeasured, a dynamic signal is introduced to dominate the unmodeled dynamics, and K-filters are used to estimate the unavailable states, and the neural networks are used to approximate the unknown continuous functions. Compared with the existing literatures, the proposed approach reduces the number of adjustable parameters effectively, relaxes the condition that both the upper and lower bounds of unknown gains are known. By theoretical analysis, the closed-loop control system is shown to be semi-globally uniformly ultimately bounded, with the tracking error converging to a small neighborhood of the origin.