WE-A-108-08: Development of Shape Memory Alloy Actuated Flexible Needle Control System for Prostate Brachytherapy

2013 ◽  
Vol 40 (6Part28) ◽  
pp. 467-467
Author(s):  
B Ruiz ◽  
P Hutapea ◽  
K Darvish ◽  
A Dicker ◽  
Y Yu ◽  
...  
Keyword(s):  
Control System ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Prostate Brachytherapy ◽  
Needle Control

Adaptive predictive control of a novel shape memory alloy rod actuator

2020 ◽  
pp. 095965182097448
Author(s):  
S Farzaneh Hoseini ◽  
S Ali MirMohammadSadeghi ◽  
Alireza Fathi ◽  
Hamidreza Mohammadi Daniali
Keyword(s):  
Control System ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Predictive Control ◽  
Smart Materials ◽  
Position Control ◽  
Least Square ◽  
Recursive Least Square ◽  
Shape Memory Alloy Actuator ◽  
Hysteresis Nonlinearity

Shape memory alloys are among the highly applicable smart materials that have recently appealed to scientists from various fields of study. In this article, a novel shape memory alloy actuator, in the form of a rod, is introduced, and an adaptive model predictive control system is designed for position control of the developed actuator. The need for such an advanced control system emanates from the fact that modeling and controlling of shape memory alloy actuators are thwarted by their hysteresis nonlinearity, dilatory response, and high dependence on environmental conditions. Real-time identification and dynamic parameter estimation of the model are addressed according to orthogonal Laguerre functions and recursive least square algorithm. In the end, the designed control system is implemented on the experimental setup of the fabricated shape memory alloy actuator. It is observed that the designed control system successfully tracks the variable step and sinusoidal control references with startling accuracy of ±1 μm.

Modeling and Tracking Control System of Shape Memory Alloy Actuator

2007 ◽  
Author(s):  
B. Y. Ren ◽  
B. Q. Chen
Keyword(s):  
Control System ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Tracking Control ◽  
Fuzzy Controller ◽  
Smart Structures ◽  
Constitutive Law ◽  
Hysteresis Model ◽  
Sma Actuators ◽  
Sma Actuator

The different Shape Memory Alloy (SMA) actuators have been widely used in the fields of smart structures. However, the accurate prediction of thermomechanical behavior of SMA actuators is very difficult due to the nonlinearity of inherence hysteresis of SMA. Therefore, the tracking control accuracy of SMA actuator is very important for the practical application of the SMA actuator. A dynamic hysteresis model of bias-type SMA actuator based on constitutive law developed by Brinson et al. and hysteresis model developed by Ikuta et al. is presented. The control systems composed of the Proportional Integral Derivative (PID) controller as well as a fuzzy controller or a fuzzy-PID composite controller for compensating the hysteresis is proposed. The effort of tracking control system is analyzed according to the simulation on the displacement of SMA actuator with the three kinds of controllers. The result can provide a reference for the application of SMA actuator in the fields of smart structures.

scholarly journals Neural Network Direct Control with Online Learning for Shape Memory Alloy Manipulators

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2576
Author(s):  
Alfonso Gómez-Espinosa ◽  
Roberto Castro Sundin ◽  
Ion Loidi Eguren ◽  
Enrique Cuan-Urquizo ◽  
Cecilia D. Treviño-Quintanilla
Keyword(s):  
Neural Network ◽  
Online Learning ◽  
Control System ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Position Control ◽  
Reference Signal ◽  
Angular Position ◽  
Hysteresis Model ◽  
Direct Control

New actuators and materials are constantly incorporated into industrial processes, and additional challenges are posed by their complex behavior. Nonlinear hysteresis is commonly found in shape memory alloys, and the inclusion of a suitable hysteresis model in the control system allows the controller to achieve a better performance, although a major drawback is that each system responds in a unique way. In this work, a neural network direct control, with online learning, is developed for position control of shape memory alloy manipulators. Neural network weight coefficients are updated online by using the actuator position data while the controller is applied to the system, without previous training of the neural network weights, nor the inclusion of a hysteresis model. A real-time, low computational cost control system was implemented; experimental evaluation was performed on a 1-DOF manipulator system actuated by a shape memory alloy wire. Test results verified the effectiveness of the proposed control scheme to control the system angular position, compensating for the hysteretic behavior of the shape memory alloy actuator. Using a learning algorithm with a sine wave as reference signal, a maximum static error of 0.83° was achieved when validated against several set-points within the possible range.

Thermo-Magnetic Control System for Nano-Ferromagnetic Particle Doped Shape Memory Alloy for Orthopedic Devices and Rehabilitation Techniques

2017 ◽  
Vol 13 (4) ◽  
pp. 678-686 ◽  
Author(s):  
Arvind Gautam ◽  
Anuradha Balouria ◽  
Divya Andem ◽  
Kare Mounika ◽  
A. Bhargavi Rani ◽  
...  
Keyword(s):  
Control System ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Ferromagnetic Particle ◽  
Magnetic Control
Sign in / Sign up

Export Citation Format

Share Document