Robust Control of Shape Memory Alloys for Assistive Robotics Applications

Architected material analogs for shape memory alloys

Precise position control of shape memory alloy actuator using inverse hysteresis model and model reference adaptive control system

Adaptive Robust Constraint-Following Control for Satellite Formation Flying with System Uncertainty

Nickel titanium (NiTi) shape memory alloys (SMAs) are well known for their significant shape recovery under thermomechanical loading [1]. Austenitic SMA can recover its original shape upon unloading (pseudoelasticity) while martensitic SMA can do so after subsequent heating (shape memory effect). Understanding the corrosion behavior of NiTi SMAs is important for extending their successful applications. Studying the corrosion of SMAs is challenging due to the inherent complexity of their constitutive response together with the presence of different phases (austenite or martensite) and microstructural states (twinned or detwinned) [2]. In this study, the corrosion behavior of equiatomic NiTi under quasi-static loading in the plastic deformation regime is studied in artificial physiological solution at room temperature. Elelctrochemcial Frequencies Modulation (EFM) technique is used the parameters of which has been found in a previous study by the authors [3]. The corrosion parameters of NiTi are measured at different load levels during plastic deformation where the passive breakdown occurs. Strain distribution on the surface of the plastically deforming NiTi sample corresponding to these load levels is captured by means of Digital Image Correlation (DIC) technique [4]. Metal loss is measured using Faraday’s law and Open Circuit Potential (OCP) measurements show that the repassivation of oxide layer is not taken place. The findings are considered a step towards better understanding of the corrosion behavior of SMAs under thermo-mechanical loading.

This study presents a new approach to control the nonlinear dynamics of an adaptive absorber using shape memory alloy (SMA) element. Shape memory alloys are classified as smart materials that can remember their original shape after deformation. Stress and temperature-induced phase transformations are two typical behaviors of shape memory alloys. Changing the stiffness associated with phase transformations causes these properties of SMA. A thermo-mechanical model (based on the transformation strain which is a measure of strain indicating the phase transformation) is used to constrain the general thermo-mechanical features of the SMA. Here, the one-dimensional SMA model is adopted to calculate both the pseudo-elastic response and the shape memory effects. The dynamic behavior of shape memory alloys is then investigated, and a Newmark method is adopted to analyze the nonlinear dynamic equations. Results demonstrate that the vibration of an initial system can be tuned using the SMA absorber in a wide range of frequencies. Therefore, SMAs as adaptive tuned vibration absorbers provide an excellent performance to control vibrations.

We introduce a new design of enhanced adaptive controllers. A PID adaptive controller have been derived using the mathematical formulas of model reference adaptive control (MRAC) combined with the self tuning adaptive control (STC). The controller tested in the position control of shape memory alloy (SMA) wire and enhanced by experiment to an enhanced adaptive controller which is more robust and consistent than other classical adaptive controllers derived based on mathematical means. This controller is used to tune the parameters of a PI controller that could control the position of an SMA wire actuator. The performance of both the enhanced adaptive controller and the PI controller are compared under different sets of excitation frequencies of input current to the SMA wire showing robust control and low percentages of error rates.KeywordsShape Memory AlloyAdaptive ControllerController ParameterShape Memory Alloy WireModel Reference Adaptive ControlThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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 coarse 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 NPID position (strain) controller. The control signals are modulated with PWM to generate a series of current pulses that heat the SMA. 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 with exceptional disturbance rejection.

Abstract: The specific behaviour of shape memory alloys (SMA) is due to a martensitic transformation [Shape Memory Materials. Cambridge University press, Cambridge]. This transformation consists mainly in a shear without volume change and is activated either by stress or temperature. The superelastic behaviour and the one‐way shape memory effect are both due to the partition between austenite and martensite. The superelastic effect is obtained for fully austenitic SMA: loaded up to 5% strain, a sample recovers its initial shape after unloading with a hysteretic loop. The one‐way shape memory effect is obtained when a martensitic SMA, plastically deformed, recovers its initial shape by simple heating. Superelasticity and one‐way shape memory effect are useful for several three‐dimensional applications. Despite all these phenomena are well known and modelled in 1D, the 3D behaviour, and especially the one‐way shape memory effect, remains quite unexplored [Mater. Sci. Res. Int., 1 (1995) 260]. Actually, the development of complex 3D applications requires time‐consuming iterations and expensive prototypes. Predictive phenomenological models are consequently crucial objectives for the design and dimensioning of SMA structures. Therefore, a series of 2D proportional and non‐proportional, isothermal and non‐isothermal tests have been performed. This database will be used to build a phenomenological model within the framework of irreversible processes.

The mechanical behaviors of super-elastic shape memory alloy are formulated based on Auricchios constitutive model. The finite element simulation method for the mechanical behaviors of super-elastic shape memory alloy using the software of ANSYS is introduced. The mechanical behaviors near crack-tip in a shape memory alloy plate with one inclining crack are numerically simulated by finite element method. Results show that the software of ANSYS can simulate the mechanical behaviors of shape memory alloy with crack effectively.

Robust tracking control of shape memory alloy (SMA) systems is a challenge because of their highly non-linear response, non-local memory and lack of differentiability. In this work, a linear parameter-varying (LPV) control method is proposed to compensate for the hysteretic behaviour of an SMA actuator. To provide information on the scheduling variable, the Preisach model of hysteresis is utilised. Parameter-dependent controller is updated based on the real-time computation of the instantaneous scheduling variable from the model. An H ∞ controller is also synthesised by representing the hysteresis as a parametric uncertainty. The controllers are implemented on an SMA actuated experimental system. Experimental validation is conducted for various types of reference inputs which can dramatically change the system's response. These reference signals are designed to excite major, minor and frequency dependent hysteresis loops. Trajectory tracking and disturbance rejection results show that the LPV controller has an improved response and the hysteresis is compensated for the full range of the scheduling variable. The tracking performances of both controllers are also compared when the system is under partial and persistent plant disturbances. For these cases, the LPV controller results in a more robust tracking response because of its less conservative structure.

In this study, a robust adaptive control method is employed for an active engine mount in a six-degree-of-freedom model of the engine on the mounts to improve vibration behavior of the engine. The vibration isolation performance and robustness of the employed robust adaptive controller are compared with a robust and an adaptive control technique. In addition, effectiveness of the robust adaptive control is evaluated in transient conditions (accelerating and gear change conditions). In this regard, a dynamic model for the engine supported by rubber and active mounts and its governing equations are presented. Then, a robust adaptive control, namely the robust Model Reference Adaptive Control (robust MRAC) technique, based on the gradient method with σ-modification, is designed by selecting a proper reference model. Moreover, a robust control, namely the H∞ control scheme and an adaptive control, namely MRAC, are employed for the active mount. Simulation results show that the robust MRAC has a better control performance (in reducing the transmitted force to the chassis) as compared with the H∞ scheme. In addition, in the face of large uncertainties, MRAC may diverge and become unstable. However, the robust MRAC is robust in the presence of large uncertainties. Also, robust MRAC is effective not only in constant engine speeds, but also in transient conditions.

Shape memory alloys (SMA) are widely and frequently applied in cases when it is useful to employ their advantages through specific behavior (pseudoelasticity or shape memory effect) in various conditions. Effects of shape memory and pseudoelasticity can be employed in innovative ways as actuating or sensing elements in many nowadays applications. There are various alloying elements which can form a SMA such as Ni, Ti, Cr, Cu, etc., but the most frequently used and known alloy is NiTi. By addition of other alloying elements the properties of the SMA can be changed to fit demands of the consumers. The investigation of such materials is very important for successful application, so the researchers investigate procedures and algorithms for comparison of experimental and numerical results to provide the best performance of SMA devices. Strong thermomechanical coupling is observed during the SMA loading, so SMA are known as highly thermosensitive materials what can be used as advantage, but also it can be a problem during the alloy production process. The strong thermomechanical coupling and the related high thermosensitivity increase the need for simulation of complex thermomechanical response in realistic problems. The complex stress states and deformation range impose the requirements for accurate analysis of large strain problems. Application of SMA started several decades ago with an engineering application in pipe couplings, while today one of the most commonly known are biomedical applications (i.e. cardiovascular stents and orthodontic braces). The main reasons for wide range of biomedical applications of NiTi alloys are the specific behavior, good biocompatibility and good fatigue performance what is important factor under the high cyclic external loading.

Robust and adaptive control: fidelity or an open relationship?

In recent years, shape memory alloys have been used widely as actuators in microelectromechanical systems. Shape memory alloys have non-linear hysteresis behaviours and parameter uncertainties that include electrical properties such as resistance. These behavioural uncertainties limit the control accuracy of shape memory alloy actuators using mathematical models. In this article, a new method is proposed for force control of shape memory alloy spring actuators. An artificial neural network is used to identify and control the shape memory alloy actuator. The shape memory alloy spring under test is a product of the Toki Corporation; its coil diameter is 0.62 mm, the diameter of the wire is 0.15 mm, and this type of shape memory alloy actuator can produce 40 gf. The results obtained are verified by an experimental set-up, which is also used to train the artificial neural network as an identifier and a controller of the system.

Active shape/stress control of shape memory alloy laminated beams