Accurate position control of shape memory alloy actuation using displacement feedback and self-sensing system

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.

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Sensorless Position Control Based on Resistance and Heat Transfer Models in Shape Memory Alloy Actuators

2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM) ◽

10.1109/aim.2019.8868335 ◽

2019 ◽

Author(s):

Takuya Sakagami ◽

Kenta Seki ◽

Makoto Iwasaki

Keyword(s):

Heat Transfer ◽

Shape Memory Alloy ◽

Shape Memory ◽

Position Control ◽

Transfer Models

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Adaptive predictive control of a novel shape memory alloy rod actuator

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering ◽

10.1177/0959651820974488 ◽

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.

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Hysteresis modeling and position control of actuator with magnetic shape memory alloy

2016 17th International Carpathian Control Conference (ICCC) ◽

10.1109/carpathiancc.2016.7501150 ◽

2016 ◽

Cited By ~ 3

Author(s):

Bartosz Minorowicz ◽

Frederik Stefanski ◽

Dariusz Sedziak

Keyword(s):

Shape Memory Alloy ◽

Shape Memory ◽

Position Control ◽

Magnetic Shape Memory ◽

Magnetic Shape Memory Alloy ◽

Hysteresis Modeling

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

Sensors ◽

10.3390/s19112576 ◽

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.

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