Desired compensation neural network adaptive robust control of an industrial linear motor motion system with comparative experimental investigation

Abstract

To meet the challenge of excellent tracking performance even under complicated nonlinear disturbance, a desired compensation neural network adaptive robust control (DCN-NARC) strategy is proposed for an industrial linear motor motion system. The proposed control scheme seriously concentrates on attenuating the effects of various model uncertainties and unknown nonlinearities, such as inertia load variation, force ripple, measurement noise, and external disturbances. Particularly, a three-layer radial basis function neural network (RBFNN) is employed to compensate the unknown nonlinearities. Online parameter adaptation is then utilized to reduce the effect of parametric uncertainties and to update the neural network weights. In addition, to facilitate the controller gain tuning process and attenuate the effect of measurement noise, the desired trajectory states are used in the adaptive model compensation following the desired compensation concept. The residual modeling error and unknown uncertainty are handled through certain robust control term. Theoretically, the proposed DCNNARC can achieve not only prescribed transient performance but also excellent tracking accuracy in general. Comparative experiments are conducted on an industrial linear motor system, and the results consistently demonstrate that the proposed scheme can achieve excellent tracking performance with remarkable disturbance rejection ability even under complicated disturbances. The proposed strategy also could be extended to other industrial control applications.