Multiple model-based control of robotic manipulators: an overview

The multiple model-based control (MMBC) technique utilizes knowledge of nominal robot dynamics and principles of Bayesian estimation to provide payload-independent trajectory tracking accuracy. The MMBC algorithm is formed by augmenting a model-based controller with a form of multiple model adaptive estimation (MMAE). The MMAE uses perturbation models of the robot dynamics and joint angle measurements to provide an estimate of the payload parameters required to minimize trajectory tracking errors. The model-based controller combines a priori knowledge of robot structure with the payload estimate to produce the multiple models of the manipulator dynamics required to maintain controller accuracy. Extensive simulation studies on the first three links of a PUMA-560 have been validated by experimental evaluation. It is concluded that MMBC provides a unique solution to the problem of maintaining trajectory tracking accuracy in uncertain payload environments