scholarly journals Experimental comparison of classical PID and model-free control: Position control of a shape memory alloy active spring

2011 ◽  
Vol 19 (5) ◽  
pp. 433-441 ◽  
Author(s):  
Pierre-Antoine Gédouin ◽  
Emmanuel Delaleau ◽  
Jean-Matthieu Bourgeot ◽  
Cédric Join ◽  
Shabnam Arbab Chirani ◽  
...  
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory ◽  
Position Control ◽  
Experimental Comparison ◽  
Model Free ◽  
Model Free Control

scholarly journals Correction to: Accurate position control of shape memory alloy actuation using displacement feedback and self-sensing system

2021 ◽  
Author(s):  
Ermira Junita Abdullah ◽  
Josu Soriano ◽  
Iñaki Fernández de Bastida Garrido ◽  
Dayang Laila Abdul Majid
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory ◽  
Position Control ◽  
Sensing System ◽  
Accurate Position

Position Control of a Three-link Shape Memory Alloy Actuated Robot

2006 ◽  
Vol 17 (5) ◽  
pp. 381-392 ◽  
Author(s):  
Hashem Ashrafiuon ◽  
Mojtaba Eshraghi ◽  
Mohammad H. Elahinia
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory ◽  
Position Control

Angular control of differential shape-memory alloy spring actuator for underactuated dynamic system

2021 ◽  
pp. 107754632110216
Author(s):  
M Banu Sundareswari ◽  
G Then Mozhi ◽  
K Dhanalakshmi
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory ◽  
Fractional Order ◽  
Position Control ◽  
Sliding Mode ◽  
Control Effort ◽  
Control Laws ◽  
Integer Order ◽  
Two Degree Of Freedom ◽  
Outer Primary

This article dwells on two technical aspects, the design and implementation of an upgraded version of the differential shape-memory alloy–based revolute actuator/rotary actuating mechanism for stabilization and position control of a two-degree-of-freedom centrally hinged ball on beam system. The actuator is configured with differential and inclined placement of shape-memory alloy springs to provide bidirectional angular shift. The shape-memory alloy spring actuator occupies a smaller space and provides more extensive reformation with justifiable actuation force than an equally able shape-memory alloy wire. The cross or diagonal architecture of shape-memory alloy springs provides force amplification and reduces the actuator’s control effort. The shape-memory alloy spring–embodied actuator’s function is exemplified by the highly dynamic underactuated custom-designed ball balancing system. The ball position control is experimentally demonstrated by cascade control using the control laws that have been unattempted for shape-memory alloy actuated systems; the ball is positioned with linear (integer-order and fractional-order) proportional–integral–derivative controllers optimized with genetic algorithm and particle swarm optimization at the outer/primary loop. Angular control of the shape-memory alloy actuated beam is obtained with nonlinear (integer-order and fractional-order sliding mode control) control algorithms in the inner/secondary loop.

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.

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.

Sign in / Sign up

Export Citation Format

Share Document