基于反演技术和神经网络的移动机器人控制

提出了一种基于反演设计和RBF神经网络自适应的非完整移动机器人轨迹跟踪方法.首先,设计一个虚拟的速度控制律使得输出跟踪误差尽可能小;然后,利用反演技术设计一个基于RBF神经网络的动力学控制器,以确保在机器人系统中存在不确定性和外界扰动的情况下,机器人仍具有良好的跟踪能力.RBF神经网络连接权值在线自适应律由Lyapunov理论导出,保证了控制系统的稳定性.本文提出方法的主要优点是不需要移动机器人动力学的先验知识,而且对外界扰动具有良好的鲁棒性.最后,在两轮非完整移动机器人上的仿真结果证明了本文所提出方法的有效性.

Control for mobile robots using back stepping and neural Networks

An adaptive controller was proposed for tracking control of nonholonomic mobile robots, that based on the utilization of back stepping and RBF neural networks. Firstly, an auxiliary velocity controller wss designed to make the output tracking error as small as possible. Then, an adaptive neural network controller was presented to ensure the velocity tracking ability under dynamic uncertainties and disturbances. The online adaptive laws of the RBF network were derived in the Lyapunov sense so that the stability of the system can be guaranteed. Finally, simulation results on a wheeled mobile robot are provided to show the effectiveness of the proposed approach.