Global Adaptive Learning Control of Robotic Manipulators by Output Error Feedback

• Published in: IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics pp. 3874 - 3879
• Main Authors: Liuzzo, S., Tomei, P.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-11-2006
• Subjects: Adaptive control; Asymptotic stability; Error correction; Fourier series; Manipulator dynamics; Output feedback; Programmable control; Robot control; Signal design; Signal processing
• ISSN: 1553-572X
• DOI: 10.1109/IECON.2006.347556

This paper addresses the problem of designing a global, output error feedback based, adaptive learning control for robotic manipulators with revolute joints and uncertain dynamics. The reference signals to be tracked are assumed to be smooth and periodic with known period. By developing in Fourier series expansion the input reference signals of every joint, an adaptive, output error feedback, learning control is designed which 'learns' the input reference signals by identifying their Fourier coefficients: global asymptotic and local exponential stability of the tracking error dynamics are obtained when the Fourier series expansion of each input reference signal is finite, while arbitrary small tracking errors are achieved otherwise. The resulting control is not model based and depends only on the period of the reference signals and on some constant bounds on the robot dynamics