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Concurrent Learning-Based Global Exponential Tracking Control of Uncertain Switched Systems With Mode-Dependent Average Dwell Time

This paper investigates the problem of global exponential tracking control for switched nonlinear systems with linear uncertain parameters and without persistency of excitation. A switched model reference adaptive control technique, using the mode-dependent average dwell time method and a concurrent learning approach, is proposed for the first time. A sufficient condition is provided to ensure that the dynamics of the state tracking error and the adaptive weight error converge to zero exponentially rapidly. Consequently, the relationship among the average dwell time of each subsystem, the concurrent learning algorithm, and the performance of uncertain switched systems is established. Furthermore, the transient performance bounds of the state tracking error and the adaptive weight error are studied. Finally, an illustrative numerical example is provided to demonstrate the effectiveness of the proposed method.