This paper examines an adaptive control scheme for tubular linear motors with micro-metric positioning tolerances. Uncertainties such as friction and other electromagnetic phenomena are approximated with a radial basis function network, which is trained online using a learning law based on Lyapunov design. Differently from related literature, the approximator is trained using a composite adaptation law combining the tracking error and the model prediction error. Stability analysis and bounds for both errors are established, and a report on an extensive experimental investigation is provided to illustrate the practical advantages of the proposed scheme.
|Titolo:||Adaptive control with composite learning for tubular linear motors with micro-metric tolerances|
|Data di pubblicazione:||2009|
|Nome del convegno:||ACC09, IEEE American Control Conference|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1109/ACC.2009.5159828|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|