Magnetic Shape Memory Alloy Actuator Research Articles

Neural Network Based Iterative Learning Control for Dynamic Hysteresis and Uncertainties in Magnetic Shape Memory Alloy Actuator

Neural Network Based Iterative Learning Control for Dynamic Hysteresis and Uncertainties in Magnetic Shape Memory Alloy Actuator

Neural Network Adaptive Control of Magnetic Shape Memory Alloy Actuator With Time Delay Based on Composite NARMAX Model

Magnetic shape memory alloy (MSMA) based actuator plays an important role in the field of micro and nano-fabrication due to its large stroke. However, the inherent hysteresis of MSMA seriously affects the application prospects of MSMA-based actuator in the field of precision positioning. In this study, a composite model is proposed for hysteresis in the MSMA-based actuator by coupling an improved fractional-order Bouc-Wen model to a nonlinear auto-regressive moving average with exogenous inputs (NARMAX) model. Based on the proposed model, a neural network adaptive control method is then used to control the MSMA-based actuator, and the controller design takes into account the effect of time delay on the system performance to improve the positioning accuracy of the MSMA-based actuator. First, we fuse the composite NARMAX model with the canonical form of the nonlinear system to describe the MSMA-based actuator. Then, a control strategy is developed to address the impact of the unknown time delay for controller design and achieve the satisfactory positioning accuracy of the MSMA-based actuator. By using Lyapunov theory, the tracking error is proven to be asymptotic convergence. Experimental studies are provided to validate the effectiveness of the proposed modeling and control schemes.

Neural network based iterative learning control for magnetic shape memory alloy actuator with iteration-dependent uncertainties

The magnetic shape memory alloy based actuator (MSMA-BA) is an indispensable component mechanism for high-precision positioning systems as it possesses the advantages of high precision, low energy consumption, and large stroke. However, hysteresis is an intrinsic property of MSMA material, which seriously affects the positioning accuracy of MSMA-BA. In this study, we propose a multi meta-model approach incorporating the nonlinear auto-regressive moving average with exogenous inputs (NARMAX) and Bouc–Wen (BW) models to describe the complex dynamic hysteresis of MSMA-BA. In particular, the BW model is introduced into the NARMAX model as an exogenous variable function, and a wavelet neural network (WNN) is adopted to construct the nonlinear function of the multi meta-model. In addition, iterative learning control is combined with a WNN to improve its convergence speed. A two-valued function is employed in the controller design process, so as to make use of history iteration information in updating control input. The main contribution of this study is the convergence analysis of the proposed iteration learning controller with iteration-dependent uncertainties (non-strict repetition of the initial state and varying iteration length). The experiments conducted on the MSMA-BA illustrate the validity of the proposed control scheme.

Hysteresis Modeling of Magnetic Shape Memory Alloy Actuator Based on Volterra Series

Magnetic shape memory alloy-based actuator (MSMA-BA) has the capability to generate the micro–nano-scale precision positioning, which has unparalleled virtue in contrast with the traditional motion driving mechanism. Nevertheless, the output displacement of the MSMA-BA exhibits complex hysteresis nonlinearity, which hinders its utility in the high-precision positioning field. In this article, an online Volterra series model based on wavelet neural network (VSM-WNN) is proposed to describe the rate-dependent hysteresis of the MSMA-BA. The kernel function of the VSM is extracted with a WNN, in which the feasibility of this extraction is proved in theory via Taylor expansion of the wavelet function. Finally, the validity of the proposed model is confirmed by means of experiments. Experimental results indicate that the VSM-WNN has a remarkable performance in describing the traits of hysteresis behavior of the MSMA-BA.

Design and performance analysis of hydraulic switching valve driven by magnetic shape memory alloy

In this paper, the hydraulic switching valve is designed and its dynamic performance is investigated through proposing a fast response actuator with magnetic shape memory alloy (MSMA) to drive the valve. MSMA actuator with spring return is designed and a double-layered coil is constructed to achieve compactness of electromagnetic case. The dynamic characteristics of the MSMA actuator are analyzed and the step response characteristics is tested. Hydraulic switching valve with MSMA actuator is designed with poppet type. Pressure and velocity field in the flow channel under different valve opening and different inlet and outlet pressure differences are analyzed in COMSOL Multiphysics software. The dynamics of the valve poppet during opening and closing process is modeled mathematically, and simulation analysis are conducted in AMESim software to analyze the response of valve under step and square wave signals. The step response of output flow rate and pressure-flow characteristic under different operating conditions are obtained through experiment. The results show that the MSMA based valve can achieve fast response with opening time of 5 ms at the pressure difference of 1 MPa, providing a theoretical support for the development of hydraulic switching valve with high performance actuator driven by MSMA.

Neural-Network-Based Iterative Learning Control for Hysteresis in a Magnetic Shape Memory Alloy Actuator

The typical characteristic of a magnetic shape memory alloy (MSMA)-based actuator is rate-dependent and stress-dependent hysteresis. In addition, the hysteresis in an MSMA-based actuator includes memory, multivalued mapping, and asymmetry properties, which seriously degrade the positioning accuracy. In this article, a feedforward neural network (FNN)-based nonlinear autoregressive moving average with exogenous inputs (NARMAX) model is employed to describe the hysteresis in an MSMA-based actuator. Through a Taylor expansion, it is revealed how the FNN constructs the nonlinear function of the NARMAX model. For the controller implementation, an FNN-based iterative learning control (ILC) strategy is proposed to achieve the desired tracking performance. The contributions of this study include exploring the update rule of the FNN-based ILC and analyzing the convergence properties of the proposed controller when the initial state of the system varies. To verify the effectiveness of the proposed method, experiments on an MSMA-based actuator were conducted. The results illustrate that the proposed modeling and control methods achieve an excellent performance and that the theoretical results are correct.

Takagi–Sugeno Fuzzy Neural Network Hysteresis Modeling for Magnetic Shape Memory Alloy Actuator Based on Modified Bacteria Foraging Algorithm

The magnetic shape memory alloy (MSMA)-based actuator, as a new type of actuator, has a great application prospect in the micro-precision positioning field. However, the input-to-output hysteresis nonlinearity largely hinders its wide application. In this paper, a Takagi–Sugeno fuzzy neural network (TSFNN) model based on the modified bacteria foraging algorithm (MBFA) is innovatively utilized to describe the complex hysteresis nonlinearity of the MSMA-based actuator, and the parameters of TSFNN are optimized by the MBFA. The TSFNN is a combination of the fuzzy-logic system and neural network; thus, it has the capability of approximating the nonlinear mapping function and self-adjustment and is suitable for hysteresis modeling. The MBFA, which can obtain better optimization values, is employed for the parameter identification procedure. To demonstrate the effectiveness of the proposed model, a TSFNN based on the gradient descent algorithm (GDA) is used for comparison. Experimental results clearly show that the proposed modeling method can accurately describe the hysteresis nonlinearity of the MSMA-based actuator and has significance for its future application.

A hopfield neural network-based Bouc-Wen model for magnetic shape memory alloy actuator

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.

Nonlinear dynamic characteristics and optimal control of MSMA actuator subjected to harmonic magnetic field and stochastic excitation

A novel magnetic shape memory alloy (MSMA) actuator is proposed in this paper, which has fast response speed and large deformation, and its nonlinear dynamic response and control are studied. A new differential term is developed to explain the MSMA’s stress-strain curves. The dynamic model of the MSMA actuator subjected to harmonic magnetic field and random disturbance is established, and its nonlinear dynamic characteristics are obtained. Finally, the stochastic dynamic programming control is introduced to enhance the drive accuracy. The results show that the perturbation plays an important role in the system’s dynamic response; dynamic programming control can improve the actuator’s accuracy in strong stochastic perturbation. These results will be helpful for the optimal design and development of MSMA actuators.

Experimental characterization and control of a magnetic shape memory alloy actuator using the modified generalized rate-dependent Prandtl–Ishlinskii hysteresis model

In this article, two models are used, namely rate-independent and rate-dependent generalized Prandtl–Ishlinskii, to characterize a magnetic shape memory alloy actuator. The results show that the rate-independent model cannot consider the effect of input excitation frequency, while the rate-dependent model omits this drawback by defining a time-dependent operator. For the first time, the effects of excitation frequency on the hysteretic behavior of magnetic shape memory alloy actuator are investigated. In this study, five excitation voltages with different frequencies in the range of 0.05–0.4 Hz are utilized as inputs to the magnetic shape memory alloy actuator and the displacement outputs are measured. Experimental results indicate that, with increasing the excitation frequency, the size of the hysteresis loops changes. Since the generalized rate-dependent Prandtl–Ishlinskii model cannot consider the asymmetric hysteresis loops, in the developed model, a tangent hyperbolic function is applied as an envelope function in order to improve the capability of the model in characterizing the asymmetric behavior of magnetic shape memory alloy actuator. The parameters of both rate-dependent and rate-independent models are identified by genetic algorithm optimization. The results reveal that the rate-independent form is not capable of accurately describing the hysteretic behavior of magnetic shape memory alloy actuator for different input frequencies. Simulation and experimental results also demonstrate the proficiency of the developed model for precise characterization of the saturated rate-dependent hysteresis loops of magnetic shape memory alloy actuator. In addition, the proposed model is utilized for determining a proper range for controller coefficients during controller design.

NONLINEAR DYNAMIC CHARACTERISTICS OF MAGNETIC SHAPE MEMORY ALLOY ACTUATOR

A kind of hysteretic nonlinear model of magnetic shape memory alloy (MSMA) is developed in this paper, and the nonlinear dynamic characteristics of a MSMA actuator are studied. Nonlinear differential items are introduced to explain the hysteretic phenomena of MSMA, and the magneto-mechanical coupled model of MSMA actuator in harmonic magnetic fields is developed. The relationship between input magnetic signal and output displacement is obtained, and the phenomena of the MSMA actuator’s accuracy aggravation in high-frequency magnetic field are explained. The theoretical and experimental results show that there are multiple frequencies in the response of the system, and the system’s motions change from periodic orbits to chaos with the increase in the level of the input magnetic signals; the multi-frequency vibration is induced by the hysteretic nonlinear characteristics of magnetic shape memory alloy.

Nonlinear Dynamic Characteristics of Magnetic Shape Memory Alloy Actuator

A kind of hysteretic nonlinear model of magnetic shape memory alloy (MSMA) was developed in this paper, and the nonlinear dynamic characteristics of MSMA actuator were investigated. Van der Pol nonlinear item were introduced to interpret the hysteretic phenomena of strain-magnetic field intensity (MFI) curves of MSMA, and the constitutive relationship among strain, stress and MFI was obtained in partial least-square regression method to describe the variation of strain-MFI curves with stress. The result of significance test shows that all of the items in the constitutive model are significant, and the result of forecast test shows that the model can describe the hysteretic nonlinear characteristics of strain-MFI curves of MSMA in different stress well. Based on the MSMA model, the magneto-mechanical coupled model of MSMA actuator was developed, and the relationship between input magnetic signal and output displacement was obtained. The nonlinear dynamic characteristics of MSMA actuator were discussed, and phenomena of accuracy aggravation of MSMA actuator in high-frequency magnetic field were explained. Finally the theoretical results were proved by experiment. The new MSMA model has simple form and is easy to be analyzed in theory, which is helpful for the application of MSMA actuator in engineering fields.

Modified KP Model for Hysteresis of Magnetic Shape Memory Alloy Actuator

ABSTRACTThe hysteresis characteristic can significantly decrease the control accuracy of magnetic shape memory alloy actuator. To describe the hysteresis between input signal and the output signal of the magnetic shape memory alloy actuator, a modelling method based on the modified Krasnosel'skii–Pokrovskii kernel is developed in this paper. The density parameters of the adopted hysteresis model are identified by the recursive least square method. The simulation results reveal that the proposed method has a good performance in describing the hysteresis modelling of magnetic shape memory alloy actuator and the modelling accuracy can be further improved by adjusting the number of Krasnosel'skii–Pokrovskii kernel. The result showed that the adopted modelling method can accurately describe the hysteresis characteristic of the magnetic shape memory alloy actuator, and the maximum modelling error can be reduced to 0.0022 mm when the number of discretization lines is selected as L = 15.

Reinforcement Learning Fuzzy Neural Network Control for Magnetic Shape Memory Alloy Actuator

Magnetic shape memory alloy actuator is a new type of actuator that can offer big travel and high resolution of output displacement, which makes it suitable for driving task. However, its output displacement represents the hysteresis applied to input magnetic field. Hysteresis restricts its application in the high precision positioning. In order to eliminate the hysteresis of magnetic shape memory alloy actuator, a reinforcement learning fuzzy neural network controller is proposed. Network structure and special learning algorithm of reinforcement learning fuzzy neural network controller are introduced in detail. The proposed control system adopts the generalized approximate reasoning-based intelligent control architecture, which is mainly consisted of three parts: Action Selection Network, Action Evaluation Network and Stochastic Action Modifier. Finally, in order to verify the effectiveness of the proposed control method, the simulation experiment is researched. The experimental results show that the proposed control method can obtain the smaller tracking error, and the controller’s maximum tracking error is less than 0.95%, hysteresis loop is less than 2.66%.

Comparative Study on MSMA Actuation under Electromagnetic and biased-Magnetic Fields

A comparative study on the effect of magnetic shape memory alloy (MSMA) under electromagnet and biased field has been carried out numerically and experimentally. The influencing parameters, magneto-strain, power efficiency and time response have been considered. The intensity and uniformity of the magnetic field are predicted numerically incorporated in the experimentation. The pre-stressed specimen is subjected to electromagnet field and bias field actuations. The measured data demonstrate an improvement in the performance of initial magneto-strain with an average difference of 75% and 27.2% reduction in time response of the MSMA actuator at lower input power.

Feedforward-feedback hybrid control for magnetic shape memory alloy actuators based on the Krasnosel'skii-Pokrovskii model.

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.

Hybrid control based on inverse Prandtl-Ishlinskii model for magnetic shape memory alloy actuator

The hysteresis characteristic is the major deficiency in the positioning control of magnetic shape memory alloy actuator. A Prandtl-Ishlinskii model was developed to characterize the hysteresis of magnetic shape memory alloy actuator. Based on the proposed Prandtl-Ishlinskii model, the inverse Prandtl-Ishlinskii model was established as a feedforward controller to compensate the hysteresis of the magnetic shape memory alloy actuator. For further improving of the positioning precision of the magnetic shape memory alloy actuator, a hybrid control method with hysteresis nonlinear model in feedforward loop was proposed. The control method is separated into two parts: a feedforward loop with inverse Prandtl-Ishlinskii model and a feedback loop with neural network controller. To validate the validity of the proposed control method, a series of simulations and experiments were researched. The simulation and experimental results demonstrate that the maximum error rate of open loop controller based on inverse PI model is 1.72%, the maximum error rate of the hybrid controller based on inverse PI model is 1.37%.

Modeling of Magnetic Shape Memory Alloy Actuator Based on Hysteretic Nonlinear Characteristics

Modeling of Magnetic Shape Memory Alloy Actuator Based on Hysteretic Nonlinear Characteristics