Adaptive prescribed performance control of switched MIMO uncertain nonlinear systems subject to unmodeled dynamics and input nonlinearities

In this paper, the design of an adaptive tracking control for a class of switched uncertain multiple-input-multiple-output nonlinear systems in the strict-feedback form with unmodeled dynamics in the presence of three types of input nonlinearity under arbitrary switching has been addressed. By means of an intelligent approximator like a fuzzy logic system or a neural network, the unknown dynamics are estimated. The unmodeled dynamics have been tackled with a dynamic signal. A universal framework for describing different types of input nonlinearity including saturation, backlash, and dead zone has been utilized. By applying the backstepping approach and the common Lyapunov function method, virtual and actual controllers and the adaptive law for each subsystem have been constructed. Finally, it has been shown that the closed-loop system is semiglobally uniformly ultimately bounded and the tracking errors converge to their predefined bounds. The effectiveness of the proposed control scheme has been shown through simulation study.