Self-Adaptive Path Tracking Control for Mobile Robots under Slippage Conditions Based on an RBF Neural Network

Wheeled mobile robots are widely implemented in the field environment where slipping and skidding may often occur. This paper presents a self-adaptive path tracking control framework based on radial basis function (RBF) neural network to overcome slippage disturbances. Both kinematic dynamic models of wheeled robot with skid-steer characteristics established position, orientation, equivalent error definitions. A dual-loop is proposed, integrated inner outer loops, respectively. An RBF neutral employed for yaw rate realize adaptability longitudinal slippage. Simulations employing proposed performed track snaking DLC reference slip ratio variations. The results suggest that yields much lower position orientation errors compared those PID single neuron (SNN) controller. It also exhibits prior anti-disturbance performance could thus be autonomous working complex terrain.