Abstract
As a new type of smart material, magnetic shape memory alloy has the advantages of a fast response frequency and outstanding strain capability in the field of microdrive and microposition actuators. The hysteresis nonlinearity in magnetic shape memory alloy actuators, however, limits system performance and further application. Here we propose a feedforward-feedback hybrid control method to improve control precision and mitigate the effects of the hysteresis nonlinearity of magnetic shape memory alloy actuators. First, hysteresis nonlinearity compensation for the magnetic shape memory alloy actuator is implemented by establishing a feedforward controller which is an inverse hysteresis model based on Krasnosel'skii-Pokrovskii operator. Secondly, the paper employs the classical Proportion Integration Differentiation feedback control with feedforward control to comprise the hybrid control system, and for further enhancing the adaptive performance of the system and improving the control accuracy, the Radial Basis Function neural network self-tuning Proportion Integration Differentiation feedback control replaces the classical Proportion Integration Differentiation feedback control. Utilizing self-learning ability of the Radial Basis Function neural network obtains Jacobian information of magnetic shape memory alloy actuator for the on-line adjustment of parameters in Proportion Integration Differentiation controller. Finally, simulation results show that the hybrid control method proposed in this paper can greatly improve the control precision of magnetic shape memory alloy actuator and the maximum tracking error is reduced from 1.1% in the open-loop system to 0.43% in the hybrid control system.
Highlights
Development of new smart materials like piezoelectric, magnetostrictive and shape memory alloy (SMA) made it possible to produce micro motion and force, and magnetic shape memory alloy (MSMA) is a new type of material which possesses advantages of smart materials such as fast response frequency, outstanding strain and stress capability [1,2,3,4,5,6]
This paper presents a hybrid control strategy for an MSMA actuator that utilizes the inverse KP model as a feedforward controller with Proportion Integration Differentiation (PID) feedback control
The proposed hybrid control strategy is applicable for all the type of MSMA actuator whose output displacement is continuous
Summary
Development of new smart materials like piezoelectric, magnetostrictive and shape memory alloy (SMA) made it possible to produce micro motion and force, and magnetic shape memory alloy (MSMA) is a new type of material which possesses advantages of smart materials such as fast response frequency, outstanding strain and stress capability [1,2,3,4,5,6]. Ge P et al proposed a hysteresis control approach for the piezoceramic actuator that incorporated a feedforward loop using the classical Preisach model with a Proportion Integration Differentiation (PID) feedback controller, which improved control accuracy by 50% compared to a regular PID controller [18]. Sui XM et al proposed a complex control strategy by combining a Cerebellar Model Articulation Controller neural network feedforward control, which was used to establish a real-time hysteresis model for Giant Magnetostrictive Material and a sliding mode variable structure control that was used to eliminate the modeling error and the external disturbance; the simulation results demonstrated that the tracking error was reduced to 1% [19]. The test results showed that the Preisach model worked smoothly within the control framework and performed more robust for the given task, and the implementation of the JCA model shows a certain sensitivity to the input increment [25]
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