An experimental study on model reference adaptive control for robot manipulators

Abstract

Model reference adaptive control based on Popov's hyperstability theory is applied to the trajectory tracking control of a two-link SCARA-type manipulator, and its effectiveness is examined experimentally. A linearized state equation with an optimal state feedback is used as a reference model. The similarity of the structure of the model and a real system like this is useful for simplifying the design of the adaptive control system. Further, by introducing a new adaptation law which consists of a combination of linear and non-linear control, it is possible to reduce high frequency chattering in the control inputs. The results of a simulation and experiment show that by adding a rather simple adaptation mechanism to a conventional state feedback, the effects of Coulomb frictions and parameter identification errors are removed and the accuracy of trajectory tracking is greatly improved.