Design, DSP implementation, and performance of artificial-intelligence-based speed estimators for electromechanical drives

The design and performance of artificial-intelligence-based (AIB) speed estimators for electromechanical drives based on feedforward and recursive artificial neural networks, associative memory networks and neuro-fuzzy networks are compared and discussed. Emphasis is placed on the development of minimal configuration estimators with a view to reducing DSP requirements. It is shown that it is an advantage of the AIB approach to estimator design that neither a conventional drive model nor a knowledge of any drive parameters are required and that an estimate of rotor speed can be obtained using only measurements of supply voltages and/or currents. The DSP system used is based on the Texas Instruments TMS320C31 mounted in a host PC. Results are presented for the real-time application to the speed control of a small DC drive and the estimators are shown to provide a sufficiently accurate speed estimate resulting in stable, robust, speed control. The DSP requirements and performances of each of the estimator forms are presented