Adaptive Tracking Control for Output-Constrained Switched MIMO Pure-Feedback Nonlinear Systems with Input Saturation

In this paper, an adaptive neural tracking control scheme for a class of uncertain switched multi-input multi-output (MIMO) pure-feedback nonlinear systems is proposed. The considered MIMO pure-feedback nonlinear system contains input and output constraints, completely unknown nonlinear functions and time-varying external disturbances. The unknown nonlinear functions representing system uncertainties are identified via radial basis function neural networks (RBFNNs). Then, the Nussbaum function is utilized to deal with the nonlinearity issue caused by the input saturation. To prevent system outputs from violating prescribed constraints, the barrier Lyapunov functions (BLFs) are introduced. Also, a switched disturbance observer is designed to make the time-varying external disturbances estimable. Based on the backstepping recursive design technique and the Lyapunov stability theory, the developed control method is verified applicable to ensure the boundedness of all signals in the closed-loop system and make the system output track given reference signals well. Finally, a numerical simulation is given to demonstrate the effectiveness of the proposed control method.

Automated summary

In this paper, an adaptive neural tracking control scheme is proposed for a class of uncertain switched multi-input multi-output pure-feedback nonlinear systems. The control method addresses the challenges caused by input and output constraints, unknown nonlinear functions, and time-varying external disturbances. The control design utilizes radial basis function neural networks (RBFNNs) to identify the unknown nonlinear functions representing system uncertainties, and the Nussbaum function to handle the nonlinearity issue caused by input saturation. The effectiveness of the proposed control method is demonstrated through numerical simulation.