Adaptive control of a shape memory alloy actuator using neural-network feedforward and RISE feedback

Awan, Asad Ullah; Park, Jaemann; Kim, Hyoun Jin; Ryu, Jae-Yong; Cho, Maenghyo

doi:10.1007/s12541-016-0051-7

Cited by 10 publications

(5 citation statements)

References 32 publications

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“…The thermal effect of the radiated heat on adjacent taxel actuators may cause some distortion to the displayed information. Though there has been increased research on the use of NiTi as an actuator, the focus has been placed on finding the best method of control due to its non-linear behaviour [3] [17] [18]. The thermal effects on adjacent taxel actuators when NiTi is used have not been researched much.…”

Section: Introductionmentioning

confidence: 99%

An Investigation of the Effects of Thermal Interference between Adjacent Nitinol Spring Actuators in a Tactile Display

Sekerere¹,

Ndeda²,

Karanja³

2023

WJET

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Over the years, there has been increased research interest in the application of Nitinol as an actuator, due to its shape memory behaviour, simplicity, high power-to-weight ratio, compactness, and extreme high fatigue resistance to cyclic motion, and noiseless operation. Nitinol has found application in tactile displays which reproduce tactile parameters such as texture and shape, depending on the application. This paper presents the effects of thermal interference between adjacent Nitinol spring actuators in a tactile display. The tactile display is made of a 3 by 3 pin array whose spatial resolution was varied from 4 mm to 6 mm in steps of 1 mm while a current of 1.5 A was used to actuate 8 of the springs, and the centre spring was left unactivated to observe the thermal effects on it due to the heat gradient formed. A Finite Element (FE) model was developed using COMSOL Multiphysics and the results were further verified through experimentation. In both cases, there was visible thermal interference between actuators. The increase in spatial resolution saw a decrease in thermal interference by 12.7%. Using a fan to introduce forced convection, reduced the thermal interference in the simulation by 20% and during experimentation by 11%. The results of this research indicate a spatial resolution of 6 mm reduced the thermal inference to a negligible rate. However, thermal interference could not be eliminated with these two methods.

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Section: Introductionmentioning

confidence: 99%

An Investigation of the Effects of Thermal Interference between Adjacent Nitinol Spring Actuators in a Tactile Display

Sekerere¹,

Ndeda²,

Karanja³

2023

WJET

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“…This change in Electrical resistance property leads to development of shape memory coil (SMC) as a sensor during actuation. Later on, many researchers proved that SMA is useful in sensing the thermal and mechanical properties of its own and other objects (Awan et al 2016 ; Gurang and Banerjee 2016 ). The electrical resistance is changing nonlinearly with contraction of length of SMA wire.…”

Section: Introductionmentioning

confidence: 99%

“…The electrical resistance is changing nonlinearly with contraction of length of SMA wire. So, it is suggested that Neural Network handles nonlinearity better and is used to model the relation between resistance and contracted length of SMA wire (Awan et al 2016 ). The Unscented Kalman Filter (UKF) estimates the contracted length as a state with help of measured electrical resistance of SMA wire actuator.…”

Section: Introductionmentioning

confidence: 99%

Machine learning-based self-sensing of the stiffness of shape memory coil actuator

Sul

Dhanalakshami

2022

Soft Comput

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Self-sensing actuation (SSA) assists in sensing the vital property of the shape memory coil which can be used to monitor and control the actuation in smart equipment as well as robotics. The stiffness characteristic of the shape memory coil (SMC) is sensed during actuation which plays a significant role in development of intelligent robotics in defense systems. The electrical property of SMC such as electrical resistance changes due to martensitic phase transformation which is further used to sense the mechanical properties such as strain, stress, temperature, length, and force of the equipment. Nowadays electrical properties are used to sense the stiffness of the shape memory coil. As of now, there is no well-established analytical model to predict the stiffness of SMC during actuation accurately. Therefore, a soft model based on machine learning is proposed in this paper for autosensing of the stiffness in SMC. The experimental facility has been developed for the collection of data with respect to diverse Joule heating currents. To determine the experimental data values of stiffness and electrical resistance of SMC is a cumbersome task. Hence, we have proposed an automated method to predict the stiffness of the SMC using soft computing-based methods. The Classical Polynomial and Bayesian optimization-based Feedforward Neural Network (FFNN) models are developed for analyzing the stiffness of the SMC. It is found that the hybrid FFNN model outperforms the other ML-based model by attaining 95.2650% accuracy. The FFNN model is also able to explain almost all the predicted stiffness values which are experimentally recorded.

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“…Adaptive control algorithms, such as output feedback direct adaptive control [30], model reference active control [31], and robust indirect adaptive control [32], etc., were proposed to control SMA actuators. Moreover, neural network model prodictive control [33], neural network feedforward control with RISE feedback [34] and neural network direct control with online learning [35] were also used to control the modeling error of SMA actuators. However, how to reject the modeling error effectively in a wide working condition of SMA actuators is still an open problem.…”

Section: Introductionmentioning

confidence: 99%

Active Modeling and Control for Shape Memory Alloy Actuators

et al. 2019

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In this paper, an active modeling and control scheme is developed for Shape Memory Alloy (SMA) actuators to eliminate the negative influences caused by the uncertainties in its dynamics. First, a nonlinear SMA dynamic model based on Liang model and the empirical models is built and linearized, and all the uncertainties due to time-varying parameters, external disturbances, as well as the linearization, are considered as model error of the linearized model. Secondly, an active modeling based on Kalman filter is constructed to estimate the model error in real time, which intends to improve the model accuracy actively. Finally, an active modeling based control method is proposed to compensate the model error in order to improve control performance of SMA actuators. Experiments are conducted on a one degree-of-freedom (DOF) testbed actuated by a SMA wire. The experimental results of the active model error estimation, and the control performance with and without the active model based compensation are presented and compared to demonstrate the improvements of the proposed scheme. INDEX TERMS Shape memory alloy (SMA), model error, active modeling, active compensation control.

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Adaptive control of a shape memory alloy actuator using neural-network feedforward and RISE feedback

Cited by 10 publications

References 32 publications

An Investigation of the Effects of Thermal Interference between Adjacent Nitinol Spring Actuators in a Tactile Display

An Investigation of the Effects of Thermal Interference between Adjacent Nitinol Spring Actuators in a Tactile Display

Machine learning-based self-sensing of the stiffness of shape memory coil actuator

Active Modeling and Control for Shape Memory Alloy Actuators

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