On-line Control of Manipulator Joint Based on RBFNN
The on-line control strategy of Manipulator joint is proposed by using RBF (Radial basis function) neural network of characteristics with high accuracy, high-speed learning. Firstly,(RBF) neural network is applied to on-line control as a identifier, here the gradient descent method is used and improved. The center ，width and weight of the network are regulated in order to reach the smallest error function.. Then Jacobi matrix is obtained according to the calculated value. At last three parameters of controller are obtained by Jacobi matrix, thus obtain the purpose of on-line control. The simulation results of dynamics model have verified the proposed control scheme possesses characteristics of high-speed adjustment, and high-precision steady-state error and strong self-adaptive capability. This paper also proves mathematically the stability of the control system under the existence of disturbances and modeling errors.
radial basis function neural network on-line self-adaptive capability manipulator joint
LIU Haiyun HU Shaoxing
Laboratory of 3D Laser Scan & Industry Computer Tomography Beihang University Department of Mechanical Engineering & Automation, Beihang University, Beijing 100083
国际会议
北京
英文
2007-08-05（万方平台首次上网日期，不代表论文的发表时间）