Model Reference Adaptive Control Strategy for Application to Robot Manipulators

The geometric nonlinearities, strong couplings, and the dependence on the inertia payload in the system dynamics of the robot manipulators lead to the difficulty in achieving good control performance. Conventional control methods cannot compensate for the payload variation effect. On the other hand, the mathematical model of the robot systems is extremely complicated and consumes an excessive amount of time in computing the robot dynamics. Moreover, deriving an exact mathematical model of the manipulator is very difficult. To handle the above issues, the model reference adaptive controller for motion control applied to robot manipulators is presented in this paper. The control law is based on the decentralized linear joint control strategy. In this approach, the control law does not require the exact model of the joint. Experiments are conducted on the 4-DOF robot manipulator to demonstrate the practicality and feasibility of the proposed control scheme, and the results are compared to those of the Ziegler-Nichols method-based PID controller and those of the model-independent controller based on time-delay estimation technique. The comparison results show that the control performance of the proposed scheme is better than that of the other controllers.