Hysteresis modeling and position control of actuator with magnetic shape memory alloy

Minorowicz, Bartosz; Stefański, Frederik; Sędziak, Dariusz

doi:10.1109/carpathiancc.2016.7501150

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…The RBFNN is used to establish the functional relationship model (20)(21)(22). But the input-output of MSMA actuators is multi-mapping (23).…”

Section: Rbfnn Modelmentioning

confidence: 99%

“…where y is the output for output layer; n represents the number of hidden layer nodes, which automatically increases with different design requirements; w i represents the weight from hidden layer's ith neuron to output neuron. The basic idea of RBFNN is used in this paper (22): input singles are transferred to hidden layer by using the two neurons. The RBFNN is nonlinear transformation, which is an activation function between input layer and hidden layer.…”

Section: Rbfnn Modelmentioning

confidence: 99%

See 1 more Smart Citation

Feed-Forward Control for Magnetic Shape Memory Alloy Actuators Based on the Radial Basis Function Neural Network Model

Zhou

Wang

et al. 2017

Journal of Applied Biomaterials & Functional Materials

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Gain control and hysteresis compensation phase shifter were used to improve the proportion integration differentiation (PID) feed-back control in the experiment. Results showed that control precision, settling time and overshoot were improved and the control accuracy was 25 nm. Ruderman and Bertram (17) proposed the system-oriented dynamic model for MSMA actuators, and combined the dynamic model of second-order linear actuators with Preisach hysteresis nonlinearity model. The discrete model parameters were identified by using experimental data and effectiveness of this dynamic model was validated. Adaptive inverse hysteresis control method based on observer was implemented to improve the robustness of system (17). The effectiveness of the control method was proved by using experiment. Mao Chiang et al predigested the control rules by using sliding mode controller and fuzzy sliding surfaces. Experimental results showed that this method was more effective and the control precision was 0.25 nm (18,19).With the advantages of adaptive learning, associative memory, strong robustness and fault tolerance, radial basis function neural network (RBFNN) has the capacity to identify any nonlinear functions. The hidden layers' output is used to obtain a set of basic functions. The linear approach is achieved by linear combination of output layers of RBFNN. In this paper, the RBFNN hysteresis model of the MSMA actuators is proposed. First, RBFNN is used as activation function to establish an inverse model. Then, a feed-forward controller is proposed

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“…The RBFNN is used to establish the functional relationship model (20)(21)(22). But the input-output of MSMA actuators is multi-mapping (23).…”

Section: Rbfnn Modelmentioning

confidence: 99%

Section: Rbfnn Modelmentioning

confidence: 99%

Feed-Forward Control for Magnetic Shape Memory Alloy Actuators Based on the Radial Basis Function Neural Network Model

Zhou

Wang

et al. 2017

Journal of Applied Biomaterials & Functional Materials

View full text Add to dashboard Cite

show abstract

“…The comparison of different model-free and model-based techniques is described in [30]. Finally, it should be mentioned that other techniques for hysteresis modelling include Generalized PIM (GPIM) [31], hyperbolic tangent [32], Hamiltonian and port Hamiltonian modeling [12,33], and Phaser [34].…”

Section: Current State In Msma Actuator Design Modeling and Controlmentioning

confidence: 99%

Design, modelling and control of a micro-positioning actuator based on magnetic shape memory alloys

Minorowicz¹,

Leonetti²,

Stefański³

et al. 2016

Smart Mater. Struct.

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This paper presents an actuator based on magnetic shape memory alloys (MSMAs) suitable for precise positioning in a wide range (up to 1 mm). The actuator is based on the spring returned operating mode and uses a Smalley wave spring to maintain the same operating parameters of a classical coil spring, while being characterized by a smaller dimension. The MSMA element inside the actuator provides a deformation when excited by an external magnetic field, but its behavior is characterized by an asymmetric and saturated hysteresis. Thus, two models are exploited in this work to represent such a non-linear behavior, i.e., the modified and generalized Prandtl-Ishlinskii models. These models are particularly suitable for control purposes due to the existence of their analytical inversion that can be easily exploited in real time control systems. To this aim, this paper investigates three closed-loop control strategies, namely a classical PID regulator, a PID regulator with direct hysteresis compensation, and a combined PID and feedforward compensation strategy. The effectiveness of both modelling and control strategies applied to the designed MSMA-based actuator is illustrated by means of experimental results.

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Intelligent Materials Application in Mechatronic Devices

Milecki

2017

Lecture Notes in Mechanical Engineering

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Hysteresis modeling and position control of actuator with magnetic shape memory alloy

Cited by 6 publications

References 13 publications

Feed-Forward Control for Magnetic Shape Memory Alloy Actuators Based on the Radial Basis Function Neural Network Model

Feed-Forward Control for Magnetic Shape Memory Alloy Actuators Based on the Radial Basis Function Neural Network Model

Design, modelling and control of a micro-positioning actuator based on magnetic shape memory alloys

Intelligent Materials Application in Mechatronic Devices

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