Hybrid control of magnetically controlled shape memory alloy actuator based on Krasnosel’skii-Pokrovskii model

Zhou, Miaolei; He, Shanbo; Zhang, Qi; Ji, Kun; Hu, Bing

doi:10.3233/ifs-151570

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…MSMA has deformation in direction of the axis when excited by magnetic field H in the direction of the y axis (17). As shown in Figure 1A, variant 1 can transform to variant 2 when the magnetic field H passes through the MSMA perpendicular to the direction of the induced strain (18). However, stronger magnetic field can make the MSMA reach maximum strain.…”

Section: Modeling Of the Msma Actuators Based On The Kp Modelmentioning

confidence: 99%

Model Reference Adaptive Control Based on KP Model for Magnetically Controlled Shape Memory Alloy Actuators

Zhou

Yannan

et al. 2017

Journal of Applied Biomaterials & Functional Materials

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J Appl Biomater Funct Mater 2017; 15 (Suppl 1): S31-S37 ORIGINAL RESEARCH ARTICLEmodel to compensate complex hysteresis of the MSMA actuators. The valid proportional-integral controller was adopted in this paper. Experimental results prove that tracking error of the hybrid control was reduced by 60% compared to the feedforward control. Ping and Jouaneh (7,8) proposed a classical Preisach model as a controller for the hysteresis nonlinearity compensation of the piezoelectric actuators. A hybrid control consisting of proportional-integral-derivative (PID) feed-back control and feed-forward control was adopted to further increase the positioning precision. Experimental results demonstrate that the maximum error of the hybrid control was reduced by 50% compared to the general PID control. Wang et al (9) proposed an inverse MPI model as a controller to increase the positioning accuracy of the piezoelectric actuators. Simulation results show that the positioning accuracy was doubled when the controller was introduced to compensate the intricate nonlinearity. Song et al (10) employed the inverse classical Preisach model as a controller to eliminate complex hysteresis of piezoelectric ceramic actuators for real-time control. Simulation results show that the error decreased by 50% to 70% when the hysteresis compensation was added to the feed-forward control. Feng et al (11) designed a robust adaptive controller based on the Duhem model to describe the severe hysteresis nonlinearity. Experimental results prove that the controller could effectively eliminate the nonlinearity. Ru et al (12) proposed an effective mathematical model to compensate

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Section: Modeling Of the Msma Actuators Based On The Kp Modelmentioning

confidence: 99%

Model Reference Adaptive Control Based on KP Model for Magnetically Controlled Shape Memory Alloy Actuators

Zhou

Yannan

et al. 2017

Journal of Applied Biomaterials & Functional Materials

Self Cite

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“…In addition, the proportional-integral-derivative (PID) model and other models recently became research hotspots [16][17][18]. To date, none of the modelling methods developed is perfect.…”

Section: Introductionmentioning

confidence: 99%

Hysteresis Curve Fitting Optimization of Magnetic Controlled Shape Memory Alloy Actuator

Zhuang

et al. 2016

Actuators

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As a new actuating material, magnetic controlled shape memory alloys (MSMAs) have excellent characteristics such as a large output strain, fast response, and high energy density. These excellent characteristics are very attractive for precision positioning systems. However, the availability of MSMAs in practical precision positioning is poor, caused by weak repeatability under a certain stimulus. This problem results from the error of a large magnetic hysteresis in an external magnetic field. A suitable hysteresis modelling method can reduce the error and improve the accuracy of the MSMA actuator. After analyzing the original hysteresis modelling methods, three kinds of hysteresis modelling methods are proposed: least squares method, back propagation (BP) artificial neural network, and BP artificial neural network based on genetic algorithms. Comparing the accuracy and convergence rate of three kinds of hysteresis modelling methods, the results show that the convergence rate of least squares method is the fastest, and the convergence accuracy of BP artificial neural networks based on genetic algorithms is the highest.

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“…In addition to the Preisach model, Sadeghzadeh et al [ 24 ] used phase shift hysteresis compensation to describe the MSM actuator and combined with PID control and gain scheduling for position controller. Zhou et al [ 25 , 26 , 27 ] used an inverse Prandtl-Ishlinskii model to eliminate the influence of the hysteresis non-linearity and a PID neural network as hybrid control for the MSM actuator.…”

Section: Introductionmentioning

confidence: 99%

Tracking Control of a Magnetic Shape Memory Actuator Using an Inverse Preisach Model with Modified Fuzzy Sliding Mode Control

Lin

Chiang

2016

Sensors

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Magnetic shape memory (MSM) alloys are a new class of smart materials with extraordinary strains up to 12% and frequencies in the range of 1 to 2 kHz. The MSM actuator is a potential device which can achieve high performance electromagnetic actuation by using the properties of MSM alloys. However, significant non-linear hysteresis behavior is a significant barrier to control the MSM actuator. In this paper, the Preisach model was used, by capturing experiments from different input signals and output responses, to model the hysteresis of MSM actuator, and the inverse Preisach model, as a feedforward control, provided compensational signals to the MSM actuator to linearize the hysteresis non-linearity. The control strategy for path tracking combined the hysteresis compensator and the modified fuzzy sliding mode control (MFSMC) which served as a path controller. Based on the experimental results, it was verified that a tracking error in the order of micrometers was achieved.

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Hybrid control of magnetically controlled shape memory alloy actuator based on Krasnosel’skii-Pokrovskii model

Cited by 4 publications

References 23 publications

Model Reference Adaptive Control Based on KP Model for Magnetically Controlled Shape Memory Alloy Actuators

Model Reference Adaptive Control Based on KP Model for Magnetically Controlled Shape Memory Alloy Actuators

Hysteresis Curve Fitting Optimization of Magnetic Controlled Shape Memory Alloy Actuator

Tracking Control of a Magnetic Shape Memory Actuator Using an Inverse Preisach Model with Modified Fuzzy Sliding Mode Control

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