Trajectory Tracking Control Of Industrial Robots

A robotic manipulator is not only an MIMO, high coupling and complicated nonlinearsystem, but also subjected to various kinds of uncertainties, such as varying load, randomdisturbances, measurement errors, frictions, and unmodeled dynamics. So we could not get theaccurate robot system model. With the rapid development of modern industry, we need higherquality robot to serve for us. It is significance in theory and practicability to research theuncertain robot control problem.The design and analysis of controller for robot trajectory tracking and force tracking isproposed in this thesis. The procedure is based on a series of theories, which include Lyapunovstability theory, adaptive control, sliding mode variable structure control, iterative learningcontrol, position/force hybrid control theory. The main works of this paper are as follows:1. A fast convergence terminal sliding mode controller and a adaptive fast terminalsliding mode controller is derived for robot nominal system and uncertain robot system withunknown upper bound. We make full use of the fast convergence characteristics of theterminal sliding mode control. The results show that the system has strong robustness andfast convergence speed.2. An adaptive iterative learning control algorithm based on boundary layer isproposed for trajectory tracking of uncertain robot system. Based on the boundary layer wedesign adaptive iterative learning controller, avoid the traditional method to design thecontroller of discontinuity and chattering phenomena, and improving the robustness ofsystem.3. Based on the Lyapunov stability theory, a position/force hybrid adaptive iterativelearning controller is designed for the constrained robot manipulator while existing structureuncertain. The adaptive law updates the unknown parameters on the line and the projectionoperator is employed to prevent the drifts of the estimated parameters. The proposed controllaws can guarantee the convergence and all the signals in the closed-loop are bounded.4. By using the Lyapunov stability theory, adaptive iterative learning control schemeis presented for the constrained robot manipulator while existing structure uncertainties andnon-structure uncertainties. Through the repeatedly iterative the external disturbance can be inhibited. This control strategy can guarantee the tracking accuracy of the position and makethe end-effector move along the constraint surface with a desired contact force.