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: Increased control demands in applications including high speed milling and hybrid motor design have led to the utilization of magnetostrictive transducers operating in hysteretic and nonlinear regimes. To achieve the high performance capabilities of these transducers, models and control laws must accommodate the nonlinear dynamics in a manner which is robust with regard to system inputs and facilitates real-time implementation. This necessitates the development of models and control algorithms which utilize known physics to the degree possible, are low-order, and are easily updated to accommodate changing operating conditions such as temperature. We consider here the development of nonlinear adaptive identification techniques for low-order, energy-based hysteresis models having nonlinear parameterizations. We illustrate the techniques in the context of magnetostrictive transducers but they are sufficiently general to be employed for a number of commonly used smart materials including piezoceramics, magnetostrictives and shape memory alloys. The performance of the resulting nonlinear identification algorithms are illustrated through numerical examples.

Nonlinear Adaptive Parameter Estimation Techniques for Magnetic Transducers Operating in Hysteretic Regimes