Fuzzy and Recurrent Neural Network Motion Control among Dynamic Obstacles for Robot Manipulators

An integration of fuzzy controller and modified Elman neural networks (NN) approximation-based computed-torque controller is proposed for motion control of autonomous manipulators in dynamic and partially known environments containing moving obstacles. The fuzzy controller is based on artificial potential fields using analytic harmonic functions, a navigation technique common used in robot control. The NN controller can deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics of the robot arm. The NN weights are tuned on-line, with no off-line learning phase required. The stability of the closed-loop system is guaranteed by the Lyapunov theory. The purpose of the controller, which is designed as a neuro-fuzzy controller, is to generate the commands for the servo-systems of the robot so it may choose its way to its goal autonomously, while reacting in real-time to unexpected events. The proposed scheme has been successfully tested. The controller also demonstrates remarkable performance in adaptation to changes in manipulator dynamics. Sensor-based motion control is an essential feature for dealing with model uncertainties and unexpected obstacles in real-time world systems.     

基于模糊自适应控制的全向移动机器人的运动控制研究

由于四轮驱动全向移动机器人轮系分布的特点,四轮之间存在耦合关系,在运行过程中,机器人整体运动的稳定性及控制精度都不佳。针对此问题,本文设计一种基于模糊自适应控制器的误差修正方法,结合模糊控制和PD控制,在线对机器人体进行误差修正,并将整体误差按轮系结构分布合理分配到单个轮子上,从而将整体的误差修正转化为单个轮子的误差修正。通过在Matlab-Simulink环境下仿真实验表明,在使用模糊自适应控制器进行误差修正后,机器人对线速度及角速度的跟随性明显提高,改善了机器人运动控制的精度。     

RoboCup中型组足球机器人的改进模糊HSIC运动控制

针对RoboCup中型组三轮全向足球机器人路径规划和轨迹跟踪的特点,结合仿人智能控制(human-simulated intelligent control,HSIC)算法,提出了改进模糊仿人智能运动控制算法,并将其运用于该系统的运动控制.实验研究证明,该算法具有控制精度高、稳定性好、能实时逼近期望路径的特点,完全满足RoboCup足球机器人运动控制的要求.另外,模糊控制的控制规则表可以由改进HSIC的逻辑控制规则所确定,使模糊控制器的设计更易于实现.