Abstract
Magnetic shape memory alloy (MSMA) actuator has potential application value in the aerospace, robotics and precision positioning due to the advantages such as small size, high precision, long stroke length and large energy density. However, the asymmetrical rate-dependent hysteresis between input and output of the MSMA actuator makes it difficult to build precise model of the MSMA actuator-based micropositioning system, so that the application of the MSMA actuator is seriously hindered. In this paper, a Bouc-Wen (BW) model is adopted to describe the hysteresis of the MSMA actuator. The parameters of BW model are identified online by Hopfield neural network (HNN). Then, the effectiveness of HNN-based BW model is fully certified using the experiments. The experimental results show that the BW model identified in this paper can accurately describe the hysteresis of the MSMA actuator at different input excitation.
Highlights
As frontier of modern science, most of the equipment in micronano positioning technology employs micro-actuator as actuation.1 Smart materials, such as giant magnetostrictive alloy, piezoceramic material and magnetic shape memory alloy (MSMA),2,3 are widely used in the development of micro-actuators devoted to micro-nano positioning applications
The same as other smart material actuators, there are some difficulties in application of the Magnetic shape memory alloy (MSMA) actuator, namely, 1) asymmetric loop between ascending and descending branches and 2) the output displacement depends on the input frequency,4,5 which causes the deterioration of positioning accuracy
A BW model is established to describe hysteresis of the MSMA actuator, and the Hopfield neural network (HNN) is first adopted to adjust the parameters of BW model online
Summary
As frontier of modern science, most of the equipment in micronano positioning technology employs micro-actuator as actuation. Smart materials, such as giant magnetostrictive alloy, piezoceramic material and magnetic shape memory alloy (MSMA), are widely used in the development of micro-actuators devoted to micro-nano positioning applications. The same as other smart material actuators, there are some difficulties in application of the MSMA actuator, namely, 1) asymmetric loop between ascending and descending branches and 2) the output displacement depends on the input frequency, which causes the deterioration of positioning accuracy This phenomenon is called asymmetrical rate-dependent hysteresis. The experimental results show compared with the rate-independent PI model, the ratedependent PI model, which considered the effect of input frequency by defining a time-dependent operator, can describe dynamic nonlinearty of MSMA actuator. HNN has the features of the nonlinear mapping property to be applied to adjust the model parameters online It has more computing power and better global searching capability compared with feedforward neural network. Experimental results demonstrate that the proposed model in this paper has capable of capturing the hysteresis of the MSMA actuator at different input signals
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