Tracking control of a non-holonomic wheeled mobile robot using improved compound cosine function neural networks

Abstract

The purpose of this paper is to propose an improved compound cosine function neural network (NN) controller to improve the tracking control performance of the compound cosine function NN controller for a non-holonomic mobile robot with nonlinear disturbances. Since the hidden layer neuron functions chosen in the three-layer network structure can affect the learning and control capability of the NN controller, the hidden layer neuron functions in the improved NN are composed of the compound functions of the cosine function and the arcsine function combined with a unipolar sigmoid function. The main advantages of this improved NN controller are able to effectively improve the rapid real-time control capability and control accuracy in the non-holonomic mobile robot control system with nonlinear disturbances and to have the simple learning algorithm of the NN controller. The effectiveness of the improved NN controller is demonstrated by simulation experiments.

Keywords:

• improved compound cosine function neural network
• continuous learning algorithm
• non-holonomic mobile robot
• neural network tracking control