Design of an adaptive nonlinear PID controller for nonholonomic mobile robot based on posture identifier

Abstract

This paper proposes an adaptive nonlinear controller to guide a nonholonomic mobile robot during continuous and non-continuous trajectory tracking. The structure of the controller consists of two models that describe the kinematics and dynamics of the mobile robot system and the feedforward neural controller. The models are modified Elman neural network and feedforward multi-layer perceptron respectively. The trained Elman neural model acts as the position and orientation identifier 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 PID neural controller is based on the posture neural identifier and quadratic performance index optimization algorithm in order to tune automatically the PID controller parameters on-line for generating an optimal torque action in the transient state for N-step-ahead prediction. The general back propagation algorithm is used to learn the feedforward neural controller and the posture neural identifier. The simulation results show the effectiveness of the proposed adaptive nonlinear control algorithm; this is demonstrated by the minimised tracking error and the smoothness of the torque control signal obtained, especially with regards to the external disturbance attenuation problem.