Brief paper: Neural network compensation control for mechanical systems with disturbances

Authors:
Xuemei Ren;Frank L. Lewis;Jingliang Zhang
Affiliations:
Department of Automatic Control, Beijing Institute of Technology, Beijing 100081, China;Automation and Robotics Research Institute, The University of Texas at Arlington, Fort Worth, TX76118, USA;A* star, Data Storage Institute, Engineering Drive 1, 117608, Singapore
Venue:
Automatica (Journal of IFAC)
Year:
2009

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Abstract

Two novel compensation schemes based on accelerometer measurements to attenuate the effect of external vibrations on mechanical systems are proposed in this paper. The first compensation algorithm exploits the neural network as the feedback-feedforward compensator whereas the second is the neural network feedforward compensator. Each compensation strategy includes a feedback controller and a neural network compensator with the help of a sensor to detect external vibrations. The feedback controller is employed to guarantee the stability of the mechanical systems, while the neural network is used to provide the required compensation input for trajectory tracking. Dynamics knowledge of the plant, disturbances and the sensor is not required. The stability of the proposed schemes is analyzed by the Lyapunov criterion. Simulation results show that the proposed controllers perform well for a hard disk drive system and a two-link manipulator.