Adaptive regulation of robots using approximate models

Many control schemes have been proposed in the literature of robot control, while most of them assume either partial or full models of the kinematics, dynamics and actuator to be known exactly. Hence system stability cannot be guaranteed if all models of kinematics, dynamics and actuator are uncertain. We proposed a new task-space adaptive control scheme for regulation of robots with approximate kinematics, dynamics and actuator models. The stability problem of the robot in presence of these uncertainties is formulated and solved. The sufficient conditions for choosing the feedback gains and approximate models are given to guarantee the convergence of the task-space position error. Experimental results are presented to illustrate the performance of the proposed scheme.