Study On Several Fuzzy Control Strategies Of Uncertain Robot

The robotic kinetic intelligent control methods mainly include fuzzy control, neural network control variable structure control and their coalescent control. These intelligent control methods are very effective for the multiple- degrees-of-freedom rigid robotic manipulators trajectory tracking, and make the robot have quite excellent practical performance in the application fields. In practice, unfortunately, it is impossible to obtain a perfect or even reasonably accurate dynamics model of a robotic manipulator. Therefore, when we establish robotic model, we need make reasonably approximate treatment, and ignore some uncertain factors, for example, parameter errors, unmodeled dynamics, observed noises and uncertainly external disturbances and so on. The uncertainties probably result in deterioration of control system quality, even turn in instability factors of robotic system. In this dissertation, the intelligent control schemes are introduced in the control system to eliminate the influence of the uncertainties.This dissertation first introduce the Cerebella Model Articulation Controller, which is satisfies real-time control requirement for uncertain robotic manipulator. The CMAC network is used to approximate robotic uncertain function. Because the contradiction between the generalization ability of CMAC and the memory capacity is not be neglected, a fuzzy CMAC strategy is presented to solve the contradiction, and the system stability is guaranteed as well. Second, variable structure control method need not accuracy mathematic model of controlled device and is insensitivity to the parametic variation and noise disturbance, so it is specially suit to the robotic control. But its robustness and control chatting are associated. Weaken chatting and guarantee system stability is necessary. A fuzzy variable structure control strategy based on system state is proposed to lower chatting effectively and guarantee the trajectory tracking accuracy of robot. Last, the robotic parameter uncertainties and external disturbances are separated to be compensated. The neural network intelligent controller is utilized to compensate for the former. The external disturbances and approximation errors will delay the system states reaching stability condition. This paper presents two control strategies instead of conventional variable structure control. The presented controllers can cancel approximation errors and restrain disturbances.The performances of controller such as stability and robustness are analyzed. The simulation results are presented for the same 2-DOF serial robotic manipulator, which validate the effectiveness and feasibility of the proposed schemes.