Adaptive NN-based finite-time tracking control for wheeled mobile robots with time-varying full state constraints

Abstract

An adaptive neural network-based finite-time tracking control method is proposed for wheeled mobile robots (WMR) in the presence of the slipping with full state time-varying constraints. The practical WMR model with coupled inputs as two torques for each wheel is difficult for the designed controllers to achieve finite-time convergence. A novel adaptive finite-time tracking control strategy is proposed by satisfying the time-varying constraints and utilizing barrier Lyapunov functions. It is proved that the controllers we designed are finite-time stable, and each tracking error converges to a small neighborhood of the original states in a finite time without violation of the full state constraints. Finally, a simulation based on a practical WMR model is conducted to verify the effectiveness of the proposed tracking control method. By using the exponential-decayed time-varying constraints, the simulation results show that the tracking control effect is very optimistic, which means that the control method proposed in this paper has great significance in engineering.