Controlled Shape Memory Alloy Research Articles

Magnetically controlled artificial urinary sphincter: An overview from existing devices to future developments.

Urinary incontinence is a common clinical problem in the world today. Artificial urinary sphincter is a good treatment approach for severe urinary incontinence, which is designed to mimic the action of the human urinary sphincter and assist patients to regain urinary function. There are many control methods based on artificial urinary sphincter, such as hydraulic control, electromechanical control, magnetic control, and shape memory alloy control. In this paper, the literature was first searched and documented based on PRISMA search strategy for selected specific subject terms. Then, a comparison of artificial urethral sphincters based on different control methods was conducted, and the research progress of magnetically controlled artificial urethral sphincters was reviewed, and their advantages and disadvantages were summarized. Finally, the design factors for the clinical application of magnetically controlled artificial urinary sphincter are discussed. As magnetic control allows for non-contact force transfer and does not generate heat, it is proposed that magnetic control may be one of the more promising control methods. The design of future magnetically controlled artificial urinary sphincters will need a variety of considerations, including the structural design of the device, manufacturing materials, manufacturing costs, and convenience. In addition, validation of the safety and effectiveness of the device and device management are equally important. The design of an ideal magnetically controlled artificial urinary sphincter is of great importance to enhance patient treatment outcomes. However, there are still great challenges to be faced for the clinical application of such devices.

Performance-based data-driven optimal tracking control of shape memory alloy actuated manipulator through reinforcement learning

This article focuses on the continuous-time optimal tracking control problem of a shape memory alloy (SMA) actuated manipulator subject to prescribed error constraints and completely unknown nonlinear dynamics. Firstly, prespecified error constraints imposed by the prescribed performance function (PPF) are transformed into an equivalent unconstrained task by adopting a transformation function, where the newly designed PPF is irrelated to the initial condition. An unconstrained augmented system and a long-term discounted performance considering tracking errors and input cost are constructed for the convenience of designing the optimal controller. Then, a model-based optimal control algorithm is derived to solve the Hamilton–Jacobi–Bellman equation (HJBE). Next, data-driven reinforcement learning (RL) is employed to approximate the solution of the HJBE iteratively to obviate requiring an SMA manipulator model. Moreover, critic neural networks (NNs) and actor NNs are introduced to the RL-based optimal controller, and the least-squares method is used to find the parameters for the NNs-based RL algorithm. Rigorous theoretical analyses demonstrate that the proposed controller can stable the SMA manipulator system, and the tracking errors are always constrained within the prescribed region. Finally, experiments are conducted on the established SMA actuated manipulator platform, and the results illustrate that the proposed controller is feasible and effective.

Stochastic recruitment control of shape memory alloy cellular actuators based on multi-state Markov chain model

A new broadcast stochastic recruitment approach to the control of shape memory alloy (SMA) cellular actuators is proposed. The control design is based on a Markov chain model of multi-state cells, which is able to better characterize the inherent hysteresis of SMA in phase transition. The closed-loop and open-loop control laws are derived from random Lyapunov stability analysis and the stability conditions are analyzed. Simulation experiments demonstrate the effectiveness of the proposed method.

Stochastic recruitment limited control of shape memory alloy based on Markov chain model

A new control method of Shape Memory Alloy (SMA) actuators based on stochastic recruitment is proposed. From the perspective of bionic control, the physiological basis of stochastic recruitment limited feedback control of artificial muscle is explained. The limited feedback control system of two-state cells and multi-state cells are established, and the limited feedback control law is deduced for these two kinds of systems. In the Simulink environment, two kinds of limited feedback control algorithms are simulated and compared, which verifies the feasibility of the algorithm. Aiming at the key problem of scaling coefficient in the system, the longitudinal and transverse comparative experiments are carried out to analyze the influence of the scaling coefficient on the system.

A numerically controlled shape memory alloy wire bending process using vat photopolymerization

Abstract The fabrication of shape memory alloy (SMA) based products requires the precise bending and high-temperature training of SMA wires to obtain desired two-dimensional (2D) and three-dimensional (3D) patterns. In the typical fabrication process, SMA wire is bent and fixed in a pre-built mold and is trained using a furnace. However, in the small batch production, the fabrication of a mold that can be put in a high-temperature oven is costly and requires a long time to fabricate. A novel SMA wire bending process using vat photopolymerization (VPP) is presented in this paper. A rotary wire extruding system was designed to control the feeding speed of the fed wire and the bending of the wire into the desired shape. The wire bending process was enabled by utilizing a VPP tool that was immersed in liquid resin to selectively cure the resin to firmly fix the extruded SMA wire on a substrate. An air bubble assisted separation process was developed to address the separation of the cured resin from the immersed VPP tool. After the bending of the SMA wire into the desired shape, the SMA wire was connected to an electric current to heat the wire to a high temperature required by the training process so the SMA wire can memorize the fabricated pattern. Finally, the photocured fixtures deposited by the VPP tool were dissolved by a chemical solution and the bent SMA wire was detached from the fixture that was ready to use. The developed numerically controlled wire bending process is flexible for designed patterns and can fabricate SMA wires into 2D/3D shapes without specific molds or tedious manual work. Several test cases were performed to demonstrate the effectiveness of the newly developed SMA wire bending process.

Design of a High-Concentration Microtracking Photovoltaic System

Abstract High-concentration photovoltaic systems can provide power conversion efficiency that is nearly double that of conventional solar panels. Concentrating photovoltaics (CPV) cannot compete with fixed silicon panels for rooftop installations due to the complexity and cost of CPV two-axis pedestal tracking systems. Fixed optic designs have recently been proposed to have a transparent middle sheet with small, widely spaced, and highly efficient solar cells sandwiched between a fixed lenslet array on the top and a fixed reflector array on the bottom. Precision actuators position the middle sheet at the focal points of the lenslet/reflector array to microtrack the sun throughout the day. This paper discusses the kinematic design and control of shape memory alloy (SMA) actuators used for the first time in this solar microtracking application. SMA actuators have the potential to be less expensive, easier to integrate, and lower power than electric motors. The kinematic design maintains upper and lower bounds on wire tension to prevent failure and ensure reversible actuation, respectively. The SMA actuators under quasi-linearized proportional integral directive (PID) control can position the middle sheet with ± 7 mm of range in the vertical and horizontal directions while ensuring less than 1.9 µm of steady-state error in SMA actuator stroke. The middle sheet position and orientation errors, however, exceed 1 mm and 0.5 deg, respectively. These relatively large errors are due to flexibility in the suspension system, friction at wire supports, and large kinematic gains at extreme positions and indicate the need for middle sheet error measurement and feedback control.

Precise position control of shape memory alloy–actuated continuum modules through fuzzy algorithm

Development of a fuzzy precise controller for the continuum modules utilizing shape memory alloy actuators is the main focus of this study. To this end, two continuum and flexible shape memory alloy–actuated modules, containing shape memory alloy wires or shape memory alloy springs, are considered as the testbed of the control problem to be tackled. The fuzzy controllers in this application are developed using two strategies in this research. In the first technique, the position errors of the two motion variables of the system are considered in the controller design process. The resulted controller is referred as “error-based fuzzy controller.” In the second technique, which is called as “improved fuzzy controller,” the parameters of the desired configuration of the system, in addition to their errors, are considered in controller design process. This procedure makes it possible to overcome the error-based fuzzy controller drawbacks. In order to validate the simulation results, experimental tests are conducted. Both simulation and experimental results reveal the performance of the developed novel two-stage improved fuzzy controller against error-based fuzzy controller and traditional proportional–integral–derivative controller.

Structure and Martensitic Transformation in Rapidly Solidified CoNiAlFe Alloy

Housler based magnetic controlled shape memory alloys are characterized by a large magnetic field induced strain. The strain was dependent on the twin martensite structure rearrangement, and the rapid solidification technology had a significant influence on the microstructure, physical, and chemical properties of the alloy. Thus, the structure and the martensitic transformation changes of Co33Ni31Al27Fe9 during the rapidly solidified process were studied. The microstructure of Co33Ni31Al27Fe9 with furnace cooled and rapid solidification (RS) constitutes a dual-phase structure, β phase and γ phase in a low cooling rate and martensite and γ phase in a high cooling rate. The γ phase at the grain boundaries reduced and became more fragile by raising the RC value. The one-step austenite-martensite phase transformation occurred during the process of heating and cooling. The phase transition temperature presents an increasing trend by rising the cooling rate, even to over the room temperature. Moreover, the martensite structure in Co33Ni31Al27Fe9 constitutes a typical L10-type twinning structure.

Model-based precision position and force control of SMA actuators with a clamping application

This paper presents a novel control strategy for precision control of Shape Memory Alloy (SMA) actuators by using a combination of large and small-signal controllers. The large-signal controller, responsible for coarse positioning, is a nonlinear PID (NPID) temperature controller that gets its desired temperature value online from a computationally efficient stress-strain and phase kinetics SMA model. The small-signal controller, responsible for fine positioning, is variable structure controller that uses an asymmetrical boundary layer to prevent over-heating of the wire. Further, the control signals adapt to the dynamic load on the wire to prevent overshoots. The control results show precise tracking control with exceptional disturbance rejection for various loads. The control strategy is then used for force control in an SMA-based clamping vice to clamp a thin-walled glass test tube. While the large-signal controller remains unchanged, the small-signal controller is used as a force controller rather than a position controller. The control unit of the vice is an embedded processor, on which the control algorithms run with a sample time of 3 ms. The vice is used to clamp the test tube at various desired step and sinusoidal forces and shows accurate force tracking and good disturbance rejection.

Aeroelastic flutter enhancement by exploiting the combined use of shape memory alloys and nonlinear piezoelectric circuits

In this paper, the combined effects of semi-passive control using shunted piezoelectric material and passive pseudoelastic hysteresis of shape memory springs on the aerolastic behavior of a typical section is investigated. An aeroelastic model that accounts for the presence of both smart materials employed as mechanical energy dissipation devices is presented. The Brinson model is used to simulate the shape memory material. New expressions for the modeling of the synchronized switch damping on inductor technique (developed for enhanced piezoelectric damping) are presented, resulting in better agreement with experimental data. The individual effects of each nonlinear mechanism on the aeroelastic behavior of the typical section are first verified. Later, the combined effects of semi-passive piezoelectric control and passive shape memory alloy springs on the post-critical behavior of the system are discussed in details. The range of post-flutter airflow speeds with stable limit cycle oscillations is significantly increased due to the combined effects of both sources of energy dissipation, providing an effective and autonomous way to modify the behavior of aeroelastic systems using smart materials.

Model reference adaptive control based on kp model for magnetically controlled shape memory alloy actuators.

Magnetically controlled shape memory alloy (MSMA) actuators take advantages of their large deformation and high controllability. However, the intricate hysteresis nonlinearity often results in low positioning accuracy and slow actuator response. In this paper, a modified Krasnosel'skii-Pokrovskii model was adopted to describe the complicated hysteresis phenomenon in the MSMA actuators. Adaptive recursive algorithm was employed to identify the density parameters of the adopted model. Subsequently, to further eliminate the hysteresis nonlinearity and improve the positioning accuracy, the model reference adaptive control method was proposed to optimize the model and inverse model compensation. The simulation experiments show that the model reference adaptive control adopted in the paper significantly improves the control precision of the actuators, with a maximum tracking error of 0.0072 mm. The results prove that the model reference adaptive control method is efficient to eliminate hysteresis nonlinearity and achieves a higher positioning accuracy of the MSMA actuators.

Study on the Model Parameter Algorithm of the MSMA Sensor

Magnetically controlled shape memory alloy (MSMA) has a mechanical–electromagnetic inverse characteristic. Based on MSMA inverse characteristics, an identification algorithm on MSMA sensor model parameter is proposed in this paper. When removing the pre-pressure system, the improved sensor structure is proved to be more effectiveness through theoretical and experimental research. In the absence of pre-pressure, chaotic particle swarm optimization algorithm is used to identification each parameter of MSMA sensor model, the model parameter of the sensor is obtained under different experimental conditions. Comparison the experimental and calculated values of the induced voltage, the correctness of model parameter algorithm is verified by this method. The research work lays a foundation for theoretical research of MSMA sensor.

Hysteresis Curve Fitting Optimization of Magnetic Controlled Shape Memory Alloy Actuator

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.

Methods to Create and Control Shape Memory Alloy Wire Actuators as Reference Design for Makers

Methods to Create and Control Shape Memory Alloy Wire Actuators as Reference Design for Makers

Hybrid Control Method of Magnetically Controlled Shape Memory Alloy Actuator Based on Inverse Prandtl-Ishlinskii Model

The hysteresis nonlinearity of magnetically controlled shape memory alloy actuator is an obstacle in the achievement of high positioning accuracy. To eliminate the influence of hysteresis nonlinearity, a PID hybrid control method which uses the inverse Prandtl-Ishlinskii model as a feedforward controller is proposed in this paper. The PID parameters are searched by particle swarm optimization with cross and heredity function. The simulation results show that when the influence of external disturbance is not considered, the positioning accuracy of open loop control method is 0.67%, and the positioning accuracy of PID hybrid control based on particle swarm optimization is 0.38%; when the influence of external disturbance is considered, the positioning accuracy of open loop control method is 3.29%, and the positioning accuracy of PID hybrid control based on particle swarm optimization is 1.65%. This indicates that the proposed control method can effectively eliminate the impact of hysteresis nonlinearity and external disturbances on the positioning accuracy.

Characterization, modeling, and control of Ni-Ti shape memory alloy based on electrical resistance feedback

The use of shape memory alloy actuators has steadily increased within the fields of aerospace, robotics, and biomedical engineering due to their superior properties compared to other actuation systems. Position control of shape memory alloy actuators is difficult due to the highly non-linear behavior but has been well studied using numerous approaches. Electrical resistance can be used to estimate strain in shape memory alloy actuator wire due to a correlation between the two parameters. Previous models of this correlation are subject to one or more drawbacks such as being limited to a single applied load, not accounting for hysteresis effects, or applying only to a specific actuator size. This article presents a stress–strain–resistance model that accounts for varying applied load, major and minor hysteresis effects and is normalized in terms of actuator geometry. Results of simulation and a simple position control experiment are demonstrated, validating the performance of the model. Furthermore, a correlation between the model and an augmented version of the Liang and Rogers model is also presented.

Reversible and Nonvolatile Modulations of Magnetization Switching Characteristic and Domain Configuration in L10-FePt Films via Nonelectrically Controlled Strain Engineering.

Reversible and nonvolatile modulation of magnetization switching characteristic in ferromagnetic materials is crucial in developing spintronic devices with low power consumption. It is recently discovered that strain engineering can be an active and effective approach in tuning the magnetic/transport properties of thin films. The primary method in strain modulation is via the converse piezoelectric effect of ferroelectrics, which is usually volatile due to the reliance of the required electric field. Also the maximum amount of deformation in ferroelectrics is usually limited to be less than 1%, and the corresponding magnetoelastic strain energy introduced to ferromagnetic films is on the order of 10(4) J/m(3), not enough to overcome magnetocrystalline anisotropy energy (Ku) in many materials. Different from using conventional strain inducing substrates, this paper reports on the significantly large, reversible, and nonvolatile lattice strain in the L10-FePt films (up to 2.18%) using nonelectrically controlled shape memory alloy substrates. Introduced lattice strain can be large enough to effectively affect domain structure and magnetic reversal in FePt. A noticeable decrease of coercivity field by 80% is observed. Moreover, the coercivity field tunability using such substrates is nonvolatile at room temperature and is also reversible due to the characteristics of the shape memory effect. This finding provides an efficient avenue for developing strain assisted spintronic devices such as logic memory device, magnetoresistive random-access memory, and memristor.

Precision Control of SMA Actuators with a Real Time Model-Based Controller and Extended VSC

Abstract: This paper presents a novel control strategy for precision position control of Shape Memory Alloy (SMA) actuators by using a combination of large and small signal controllers. The large signal controller, responsible for course positioning, is an NPID temperature controller that gets its desired temperature value online from a computationally efficient stress-strain and phase kinetics SMA model that accounts for time varying stresses. The small signal controller, responsible for fine positioning, is an extended variable structure controller that uses an asymmetrical boundary layer to prevent over-heating of the wire and whose control signals adapt to the dynamic load on the wire. The control strategy is tested on an SMA wire, first, with various constant loads and then with varying loads. The results show robust and precise tracking control within 0.03% strain with exceptional disturbance rejection.

Hysteresis Model of Magnetically Controlled Shape Memory Alloy Based on a PID Neural Network

Magnetically controlled shape memory alloys(MSMA) are a new kind of smart material that can be used in micro-displacement and micro-positioning applications. However, the hysteresis nonlinearity of this material is an obstacle to achieve high precision accuracy [1]. The hysteresis nonlinearity of MSMA can be described by the relationship between the input and output signal which is called hysteresis loop [2]. As shown in figure 1(a), hysteresis loops can be divided into major and minor hysteresis loops. The input and output signals have the same phase at the extrema and the phase of the output signal lags behind the input signal at all other times. A major hysteresis loop takes shape when the input signal rises from an initial value to a maximum value before decreasing to a minimum value (which is same as the initial value). Minor hysteresis loops occur when the input signal rises from a local minimum value (which is different from the overall minimum value) to a local maximum value (which is different from the overall maximum value) before decreasing to a local minimum value.

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

The hysteresis nonlinearity of the magnetically controlled shape memory alloy (MSMA) actuator exerts a great influence on the positioning accuracy. To effectively alleviate the hysteresis nonlinearity of the MSMA actuator, an inverse Krasnosel’skii-Pokrovskii model is established in the paper. Subs equently, a hybrid control scheme which is composed of a hysteresis inverse model feedforward controller and a PID feedback controller is proposed to further improve the positioning accuracy of the MSMA actuator. A differential evolution algorithm is adopted to tune the parameters of PID controller. The experimental results show that the proposed control scheme is effective for improving the positioning accuracy of the MSMA actuator, and the maximum positioning error rate is 0.55%.