Adaptive robust control strategy for rhombus-type lunar exploration wheeled mobile robot using wavelet transform and probabilistic neural network

In this paper, we propose a stable tracking control rule for rhombus-type lunar exploration wheeled mobile robot (RLEWMR) with completely unknown dynamics and unmodeled disturbance. The control system adopts a wavelet transform and probabilistic neural network (WTPNN) with accurate approximation capability to represent the unknown dynamics of the RLEWMR, and it also uses an adaptive robust compensator to confront the inevitable approximation errors due to the finite number of wavelet bases functions and to disturbances. Adaptive learning algorithms are proposed to learn the parameters of WTPNN weight and robust compensator on line. Based on the Lyapunov stability theorem, the tracking stability of the closed-loop system, the convergence of the WTPNN weight-updating process, and boundedness of WTPNN weight estimation errors are all guaranteed. The effectiveness and efficiency of the proposed controller is demonstrated by simulation and experiment studies.