Abstract:
An adaptive neural sliding mode control strategy with the self-tuning of robust item coefficients is
proposed for the trajectory tracking of non-holonomic wheeled mobile robots. Firstly, a kinematic controller is
designed by means of backstepping technique. Then, the dynamic controller is proposed based on sliding mode control
method, in which the upper bound of the uncertainties is adaptively approximated by RBF neural networks and the
robust item coefficients are self-tuned. Thus, the disadvantage of the traditional sliding mode controller, which
needs to know the boundary of the system uncertainties in advance, is overcome. By using Lyapunov stability
theorem, both the stability of closed-loop system and the asymptotical convergence of tracking errors are ensured.
Simulation results further validate the effectiveness of the proposed controller.