Robust adaptive decentralized control of robot manipulators

The authors proposes a robust adaptive decentralized control algorithm for trajectory tracking of robot manipulators. The controller is designed based on a Lyapunov method, which consists of a PD (proportional plus derivative) feedback part and a dynamic compensation part. It is shown that, without any prior knowledge of manipulator or payload parameters and possibly under deterioration of parameter variation with time or state-independent input disturbances, the tracking error is bound to converge to zero asymptotically. In particular, the algorithm does not require explicit system parameter estimation and therefore makes the controller structurally simple and computationally easy. Moreover, the controller is implemented in a decentralized manner, i.e. a subcontroller is independently and locally equipped at each joint servoloop. To illustrate the performance of the controller, a numerical simulation example is provided