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

The multiple-model-based control (MMBC) technique utilizes knowledge of nominal plant dynamics and principles of Bayesian estimation to provide parameter-independent trajectory tracking accuracy. The MMBC algorithm is formed by augmenting a model-based controller with a closed-loop form of multiple-model adaptive estimation (ΔMMAE). The ΔMMAE uses perturbation models of the combined plant and feedback control system, along with measurements of tracking error, to provide an estimate of the plant parameters. When MMBC is applied to the robotic manipulator control problem the ΔMMAE provides a payload estimate. 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. MMBC provides a unique solution to the problem of maintaining trajectory tracking accuracy in uncertain payload environments