Applications utilizing smart materials are rapidly increasing and include high speed milling and hybrid motor design. Such application utilize 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. To this end, the models and control algorithms must utilize known physics to the highest degree possible, be low order, and be sufficiently robust to operate under realistic conditions. In this paper we consider the robust control of a smart structure with disturbances due to inherent hysteresis and sensor noise. We dmeonstrate the techniques on a magnetostrictive transducers but they are sufficiently general to be utilized on several commonly used smart materials. The performance of the control strategies are illustrated through numerical examples.
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.
Proc. SPIE. 4326, Smart Structures and Materials 2001: Modeling, Signal Processing, and Control in Smart Structures
KEYWORDS: Transducers, Actuators, Control systems, Complex systems, Systems modeling, Differential equations, Magnetostrictive materials, Control systems design, Nonlinear filtering, Scientific research
This paper focuses on the development of partial inverse compensation techniques for linear control design in systems employing magnetostrictive transducers operating in nonlinear and hysteretic regimes. At low drive levels, linear models can be used to characterize strains and forces generated by magnetostrictive transducers with reasonable accuracy. However, at the moderate to high drive levels where transducer performance is optimal, inherent constitutive nonlinearities and hysteresis must be accommodated to achieve the accuracy and speed requirements for high performance applications. Appropriate nonlinear and hysteretic modeling techniques are reviewed and an inverse compensator based on the nonlinear kernel of the model is developed. The performance of the technique is illustrated through numerical examples.