Adaptive Multiple-Model Switching Control of Robotic Manipulators

The control problem of robotic manipulators has drawn increasing attention during the past twenty years. Adaptive control, with its great potential for dealing with systems in uncertain environments, becomes a powerful tool in this area. This research 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.