Analog compound orthogonal neural network control of robotic manipulators

摘要

An analog compound orthogonal neural network is presented which is based on digital compound orthogonal neural networks. The compound neural network's control performance was investigated as applied to a robot control problem. The analog neural network is a Chebyshev neural network with a high speed-learning rate in an on-line manner. Its control algorithm does not relate to controlled plant models. The analog neural network is used as the feedforward controller, and PD is used as the feedback controller in the control system of robots. The excellent performance in system response, tracking accuracy, and robustness was verified through a simulation experiment applied to a robotic manipulator with friction and nonlinear disturbances. The trajectory tracking control showed results in satisfactory effectiveness. This analog neural controller provides a novel approach for the control of uncertain or unknown systems.