Applying Posture Identifier and Backstepping Method in the Design of an Adaptive Nonlinear Predictive Controller for Nonholonomic Mobile Robot

Ahmed S. Al-Araji, Maysam F. Abbod, and Hamed S. Al-Raweshidy


Mobile Robots, Adaptive Neural Predictive Controlle, back-stepping Method, Trajectory Tracking


This paper presents a trajectory tracking control algorithm of a nonholonomic mobile robot using back-stepping method based predictive feedback control with an optimization algorithm and an adaptive posture identifier model while following a continuous and a non continuous path. The posture identifier model is a modified Elman neural network that describes the kinematics and dynamics of the mobile robot system. The feedforward neural controller is trained off-line and adaptive weights are adapted on-line to find the reference torques, which controls the steady-state outputs of the mobile robot system. The feedback controller is nonlinear inverse dynamic neural controller to find the optimal torque action in the transient state for N-step-ahead prediction. The stability and convergence of control system are proved by using the Lyapunov criterion. Simulation results show the effectiveness of the proposed algorithm; this is demonstrated by the minimised tracking error and the smoothness of the torque control signal obtained with bounded external disturbances.

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