The Research On Uncertain Robot With Several Kinds Of Intelligent Control Strategies

The control problems of robotic manipulators have received great attention in theoretical and engineering for many years. When the robot model is exactly known, the technique of feedback linearization in nonlinear systems can solve the problem very well. However, the parameters of dynamic model of robotic manipulators may also be subject to change when the manipulator goes about its task. Meanwhile, the system can be influenced by uncertainties such as external disturbance and payload change. Therefore it is necessary to improve these existing control methods In this dissertation, the system of robotic manipulators with entire dynamic model, namely, the robotic system with uncertainties is regarded as controlled plant. The various robust control schemes based intelligent algorithms are developed using the references available.The first chapter of dissertation gives a brief description about the developing situation and control theory of robot. The second chapter introduces necessary mathematic knowledge of controller design and the dynamic model of robot. The third chapter put forward a fuzzy adaptive control method combining sliding supervisory control term. The fuzzy controller associated with simple rule base and membership function effectively compensate the system uncertainties. Afterward utilizing a sliding supervisory control term which produces a low chattering eliminate the approaching error of fuzzy control system. The fourth chapter bring forward the fuzzy neural network control of uncertain robot using backstepping method. Here fuzzy neural network is used to learn the ideal feedback linearization control law, and through adopting a robust term compensate learning error via fuzzy neural network. The overall designing process of controller is based on backstepping method and the whole stability of system is effectively guaranteed.The fifth chapter introduces a sliding observer using RBF neural network to observe the speed signal aiming at only having position measurement in industrial robot. The observer combining sliding method improve the ability of resisting disturbance. RBF neural network is used to compensate various uncertainties of robot. In addition, the method avoid the computation of regressor matrix and the demand of the priori knowledge of inertia matrix. Taking into account the dynamic interactions between the observer and controller dynamics, it can be ensured that the fictitious speed signal can substitute the real speed signal and is employed in the feedback loop. Simulation results prove the effectiveness of proposed methods.