Research on neural network PID control with application to heavy-duty wheeled vehicle steering system

According to the plant characteristics and the operating method of the heavy-duty wheeled vehicle, a neural network controller is proposed for improving the control performance of the traditional PID controller. The configuration of the control system is based on RBF neural networks, one is RBF neural network identifier (RBFNNI) used to identify the Jacobian matrix of control plant, and the other is neural network controller (NNC) used to provide nonlinear PID control parameters, which is used to achieve the better performance than conventional single PID controller. Then, neural network architecture is designed according to improved control algorithm. At last, simulation result is given and discussed. The result shows that the control system is robust and adaptive in dealing with nonlinear systems, so it is feasible for control steering and manipulating system of the wheeled vehicle.