Segmented Binary Control of Shape Memory Alloy Actuators — Feedforward Servo Control

2004 ◽  
Author(s):  
Brian Selden ◽  
Kyu-Jin Cho ◽  
H. Harry Asada
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory ◽  
Transient Response ◽  
Feedback Loops ◽  
Servo Control ◽  
Dramatic Improvement ◽  
New Approach ◽  
Local Feedback ◽  
Binary Control ◽  
And Control

A new approach to the design and control of shape memory alloy (SMA) actuators, called Segmented Binary Control (SBC), is extended from previous work. The transient response of SBC is examined and is discovered to be inadequate in real time servo control because of significant latency times. A dramatic improvement is shown using a feedforward method in which a predetermined path is known and appropriate actions are calculated beforehand. In addition, this feedforward servo control of SMA is accomplished with only internal local feedback loops and no global feedback of position.

Modeling of Shape Memory Alloy Actuators Using Matlab and dSpace Platform

2010 ◽  
Vol 166-167 ◽  
pp. 149-154
Author(s):  
Ioan Adrian Cosma ◽  
Vistrian Măties ◽  
Ciprian Lapusan ◽  
Rares Ciprian Mîndru
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory ◽  
Dynamic Model ◽  
Dynamic Equations ◽  
Control Technique ◽  
Positioning System ◽  
Unknown Parameters ◽  
New Approach ◽  
Model Based Control ◽  
Input And Output

The aim of the paper is to describe an approach for modeling the dynamic behavior of a positioning system actuated by two shape memory alloy springs, placed in opposition. The mathematical analysis of the system in order to develop the dynamic model is difficult in this case because of the unknown parameters within the dynamic equations (thermodynamics, change in austenite fraction) and therefore a new approach is presented. Thus, a positioning system is considered, and its behavior is determined using Matlab Software, D-space platform and an optical sensor, which analyses the position/velocity of the moving cart. The dynamic model of the system is determined in order to develop a further model based control technique. The model is generated using system identification toolbox within Matlab and input and output (response) of the considered system.

scholarly journals Seismic Assessment of RC Bridge Columns Retrofitted with Near-Surface Mounted Shape Memory Alloy Technique

Materials ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1701 ◽  
Author(s):  
Ammar Abbass ◽  
Reza Attarnejad ◽  
Mehdi Ghassemieh
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory ◽  
Plastic Hinge ◽  
Bridge Columns ◽  
Near Surface ◽  
New Approach ◽  
Seismic Rehabilitation ◽  
Bridge Column ◽  
Near Surface Mounted ◽  
Fiber Element Modeling

From past earthquakes, it has been found that the large residual displacement of bridges after seismic events could be one of the major causes of instability and serviceability disruption of the bridge. The shape memory alloy bars have the ability to reduce permanent deformations of concrete structures. This paper represents a new approach for retrofitting and seismic rehabilitation of previously designed bridge columns. In this concept, the RC bridge column was divided into three zones. The first zone in the critical region of the column where the plastic hinge is possible to occur was retrofitted with near-surface mounted shape memory alloy technique and wrapped with FRP sheets. The second zone, being above the plastic hinge, was confined with Fiber-Reinforced Polymer (FRP) jacket only, and the rest of the column left without any retrofitting. For this purpose, five types of shape memory alloy bars were used. One rectangular and one circular RC bridge column was selected and retrofitted with this proposed technique. The retrofitted columns were numerically investigated under nonlinear static and lateral cyclic loading using 2D fiber element modeling in OpenSees software. The results were normalized and compared with the as-built column. The results indicated that the relative self-centering capacity of RC bridge piers retrofitted with this new approach was highly greater than that of the as-built column. In addition, enhancements in strength and ductility were observed.

Design and control of a novel compliant differential shape memory alloy actuator

2015 ◽  
Vol 225 ◽  
pp. 71-80 ◽  
Author(s):  
Zhao Guo ◽  
Yongping Pan ◽  
Liang Boon Wee ◽  
Haoyong Yu
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory ◽  
Shape Memory Alloy Actuator ◽  
And Control

Analysis and control of large thermal deflection of composite plates using shape memory alloy

2000 ◽  
Author(s):  
Bin Duan ◽  
Mohammad Tawfik ◽  
Sylvain N. Goek ◽  
Jeng-Jong Ro ◽  
Chuh Mei
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory ◽  
Composite Plates ◽  
And Control ◽  
Thermal Deflection

Experiment Verification and Stability Analysis of Iterative Learning Control for Shape Memory Alloy Wire

2019 ◽  
Vol 31 (4) ◽  
pp. 583-593
Author(s):  
Hitoshi Kino ◽  
Naofumi Mori ◽  
Shota Moribe ◽  
Kazuyuki Tsuda ◽  
Kenji Tahara ◽  
...  
Keyword(s):  
Stability Analysis ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Iterative Learning Control ◽  
Robot Manipulator ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Methods ◽  
Experiment Verification ◽  
And Control

To achieve the control of a small-sized robot manipulator, we focus on an actuator using a shape memory alloy (SMA). By providing an adjusted voltage, an SMA wire can itself generate heat, contract, and control its length. However, a strong hysteresis is generally known to be present in a given heat and deformation volume. Most of the control methods developed thus far have applied detailed modeling and model-based control. However, there are many cases in which it is difficult to determine the parameter settings required for modeling. By contrast, iterative learning control is a method that does not require detailed information on the dynamics and realizes the desired motion through iterative trials. Despite pioneering studies on the iterative learning control of SMA, convergence has yet to be proven in detail. This paper therefore describes a stability analysis of an iterative learning control to mathematically prove convergence at the desired length. This paper also details an experimental verification of the effect of convergence depending on the variation in gain.

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