Multiple model-based control of robotic manipulators: theory and experimentation

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 the a priori knowledge of robot structure with the payload estimate to produce the multiple models of the manipulator dynamics required to maintain controller accuracy. The development of the PUMA-specific version of the MMBC is presented first three links of PUMA-560, along with experimental validation of extensive simulation studies