A dynamic prediction error based adaptive multiple-model control scheme for robotic manipulators

Abstract

This paper first proposes a new dynamic prediction error based adaptive controller for robotic manipulators with uncertain parameters. Unlike most prediction errors used in the robotics literature, a dynamic prediction error is generated from an adaptive predictor of a parametrized and dynamic manipulator model. A multiple-model adaptive control scheme is then developed using multiple prediction errors and multiple controllers, incorporated with multiple parameter estimators and a control switching mechanism. The use of an adaptive dynamic predictor for parameter estimation leads to a new, effective and simple control structure. Multiple controllers are constructed with different parameter estimators, and a most appropriate control signal is selected by the control switching mechanism which is designed to find the model that best approximates the manipulator dynamics. Closed-loop system stability and output tracking are proved and the detailed analysis is given. Simulation results demonstrate the desired control system performance.