Nonlinear Adaptive Parameter Estimation Algorithms for Hysteresis Models of Magnetostrictive Actuators

摘要

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 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 for low order, energy-based models. 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. The performance of the identification algorithm is illustrated through numerical examples.