Adaptive learning control design for robotic manipulators driven by permanent magnet synchronous motors

摘要

On the basis of the ideas recently presented in Tomei and Verrelli (Tomei, P., and Verrelli, C.M. (2010 Tomei, P and Verrelli, CM. 2010. Learning Control for Induction Motor Servo Drives with Uncertain Rotor Resistance. International Journal of Control, 83: 1515–1528. [Taylor & Francis Online], [Web of Science ®] , [Google Scholar]), ‘Learning Control for Induction Motor Servo Drives with Uncertain Rotor Resistance’, International Journal of Control, 83, 1515–1528) and Marino et al. (Marino, R., Tomei, P., and Verrelli, C.M. (2011 Marino, R, Tomei, P and Verrelli, CM. 2011. Robust Adaptive Learning Control for Nonlinear Systems with Extended Matching Unstructured Uncertainties. International Journal of Robust and Nonlinear Control, Early View, doi: 10.1002/rnc.1720 [Google Scholar]), ‘Robust Adaptive Learning Control for Nonlinear Systems with Extended Matching Unstructured Uncertainties’, International Journal of Robust and Nonlinear Control, Early View, doi: 10.1002/rnc.1720), we briefly show how the adaptive learning control design proposed in Liuzzo and Tomei (Liuzzo, S., and Tomei, P. (2009 Liuzzo, S and Tomei, P. 2009. Global Adaptive Learning Control of Robotic Manipulators by Output Error Feedback. International Journal of Adaptive Control and Signal Processing, 23: 97–109. [Crossref], [Web of Science ®] , [Google Scholar]), Global Adaptive Learning Control of Robotic Manipulators by Output Error Feedback, International Journal of Adaptive Control and Signal Processing, 23, 97–109) can be extended to robotic manipulators driven by nonsalient-pole (surface) permanent magnet synchronous motors. Unstructured uncertain dynamics (that is no parameterisation is available for the uncertainties) of the rigid robot with rotational joints are considered as well as uncertainties in stator resistances of the synchronous motors are taken into account. Two solutions with clear stability proofs are presented: a global decentralised control via state feedback and a semi-global control via output feedback. Output tracking of known periodic reference signals and learning of corresponding uncertain input reference signals are achieved. Available results in the literature are thus improved since no simplification concerning negligible electrical motor dynamics is used.