Electric Drive Rigid Manipulator Neural Network Control Method

This paper concerns to the tracking control problem of robots with uncertainties. It analyses the dynamical characteristics of the robots and presents the dynamical differential equations of the system. The controller design of the robot system which is made up of mechanical subsystem and electrical subsystem results from the techniques of robust control and neural network control, in addition to the method of anti-dynamics.(1) The design of robust controller with NN. To care about the ability of suppressing disturbance, a robust controller which is underlined to the H control theory is designed to deal with the model errors. A two-layer feed forward NN which involves a novel weights algorithm is used to identified the first-order differentials of the expected current.(2) The former model errors can also be regarded as nonlinear functions, which makes it that they can be identified by NN because of its nonlinear function ability. The overall controller of system is made up of two two-layer feed forward NNs.Stability analysis is made for the above two control algorithms, which results in the uniformly ultimately bounded stability of the tracking errors and weights. What's more, we present the relationship between the errors upper bound and the controller parameters, which is valuable to the design of the controller.To compare former controllers, the ones designed in this paper are characterized by simple structure, good real time property, fine control precision, and guaranteed robustness, which makes it constructive in the real-time control of robots.