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Article

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

1
Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Ave. Epigmenio González 500, Fracc. San Pablo, Querétaro 76130, Mexico
2
KTH Royal Insitute of Technology, 114 28 Stockholm, Sweden
3
Escuela Politécnica Superior, Universidad Mondragón, 20500 País Vasco, Spain
*
Authors to whom correspondence should be addressed.
Sensors 2019, 19(11), 2576; https://doi.org/10.3390/s19112576
Received: 12 April 2019 / Revised: 2 June 2019 / Accepted: 4 June 2019 / Published: 6 June 2019
(This article belongs to the Special Issue Artificial Intelligence and Sensors)
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. View Full-Text
Keywords: shape memory alloys; artificial neural networks; control; manipulators shape memory alloys; artificial neural networks; control; manipulators
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MDPI and ACS Style

Gómez-Espinosa, A.; Castro Sundin, R.; Loidi Eguren, I.; Cuan-Urquizo, E.; Treviño-Quintanilla, C.D. Neural Network Direct Control with Online Learning for Shape Memory Alloy Manipulators. Sensors 2019, 19, 2576. https://doi.org/10.3390/s19112576

AMA Style

Gómez-Espinosa A, Castro Sundin R, Loidi Eguren I, Cuan-Urquizo E, Treviño-Quintanilla CD. Neural Network Direct Control with Online Learning for Shape Memory Alloy Manipulators. Sensors. 2019; 19(11):2576. https://doi.org/10.3390/s19112576

Chicago/Turabian Style

Gómez-Espinosa, Alfonso, Roberto Castro Sundin, Ion Loidi Eguren, Enrique Cuan-Urquizo, and Cecilia D. Treviño-Quintanilla. 2019. "Neural Network Direct Control with Online Learning for Shape Memory Alloy Manipulators" Sensors 19, no. 11: 2576. https://doi.org/10.3390/s19112576

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