Adaptive control with composite learning for tubular linear motors with micro-metric tolerances

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.