Neural-network-based model reference adaptive systems for high-performance motor drives and motion controls

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance, or torque constant. For example, the conventional linear estimators are not adaptive to variations of the operating point. The model reference adaptive systems (MRASs) have been shown to give better solutions with online adaptation, but the adapting mechanism is mostly linear. Neural networks (NNs) have shown better results when estimating or controlling nonlinear systems. This paper combines the online adaptation of MRASs with the ability of NNs for better modeling of nonlinear systems. It presents an MRAS using a NN in the adaptation mechanism. The technique is applied to a permanent-magnet synchronous motor drive. The effects of torque constant and stator resistance variations on the position and/or speed estimations over a wide speed range have been studied. The NN estimators are able to track the varying parameters at different speeds with consistent performance. Simulation and experimental implementations and results are presented.