Abstract:

A control structure is proposed for trajectory tracking control of nonholonomic mobile robots. It integrates the backstepping kinematic controller and a sliding mode controller with Adaptive Dynamic Recurrent Fuzzy Neural Network (ADRFNN). The genetic algorithm is used to optimize the parameters of kinematic controller that effectively suppresses the excessive initial speed and output torque caused by large initial error of posture. The ADRFNN is developed to achieve on-line estimation of the part of dynamic nonlinear uncertain, which greatly reduces estimation errors of uncertainties. By combing ADRFNN with the adaptive robust controller, this method can not only solve the problem of parameters and non-parameter uncertainties of mobile robots, but also eliminate input chattering of the sliding mode control. The stability and convergence of the control system are proved by Lyapunov theory. Simulation results demonstrate the effectiveness of the proposed method.

Key words: automatic control technology, nonholonomic mobile robots, trajectory tracking, adaptive dynamic recurrent fuzzy-neural network(ADRFNN), sliding mode control, genetic algorithm (GA)