A Hybrid Controller of Adaptive and Learning Control for Geometrically Constrained Robot Manipulators

When an endpoint of a manipulator is moving in touch with a rigid smooth surface, the manipulator should be controlled to achieve both a desired position trajectory of the endpoint and a desired contact force at the contact point. In this paper, we deal with a hybrid controller of repetitive learning control and model-based adaptive control for geometrically constrained robot manipulator. The convergence of joint angular errors and contact force errors is proved under an appropriate initial condition and the smoothness of the constraint surface. Furthermore, by the simulation experiment, we show that the proposed hybrid controller makes 2-link constrained manipulator track a desired position and a desired constraint force trajectory