Research On Manipulator Trajectory Tracking Based On Interative Prediction Control

Manipulator is not only a high coupling, MIMO and complicated nonlinear system, but also subjected to various kinds of uncertainties, such as modeling error and random disturbances. So we could not get the accurate manipulator system model. With the rapid development of modem industry, we need higher quality manipulator to serve for us. It is significance in theory and practicability to research the uncertain manipulator trajectory tracking control problem.The design and analysis of controller for manipulator trajectory tracking is proposed in this thesis. The procedure is based on a series of theories, which include iterative learning control, model predictive control. The main works of this paper are as follows:1. To the characteristics of repetitive operation and nonlinearity of manipulator trajectory tracking control process, a hybrid algorithm of iterative learning control and model predictive control is given.2. A nonlinear iterative learning model predictive control algorithm based on T-S fuzzy model was designed for overcoming the instability resulted from modeling error and perturbation of manipulator trajectory tracking control.3. In the given track experiment of the two-link manipulator, the system achieved perfect trajectory tracking effect, and the system error was converged to zero rapidly. The results show that the method proposed in this paper has high robust stability for nonlinear system. The effectiveness of the method was verified in the experiment.