Modeling and Hysteresis Compensation in a Thin SMA Wire Using ANFIS Methods

Kilicarslan, Atilla; Song, Gangbing; Grigoriadis, Karolos

doi:10.1177/1045389x10392610

Cited by 9 publications

(6 citation statements)

References 25 publications

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“…For example, a Preisach model was employed for current-strain hysteresis of an SMA actuator [11], but under a constant tension force. An adaptive neuro-fuzzy inference system model was realized for an SMA actuator at various frequencies, but only the voltage -strain hysteresis was captured [12]. Similarly, a generalized Prandtl-Ishlinskii model was adopted for position control, only the temperature-deflection hysteresis was compensated [13].…”

Section: Related Workmentioning

confidence: 99%

“…It was found that the control accuracy of the proposed compensation algorithm was not higher than existing methods. This can be explained as follows: Firstly, a predominant class of the existing studies focused on two-dimensional hysteresis while holding the third-dimensional property as a constant [11][12][13]18]. Although accurate characterization and compensation were achieved, these models could not work for many practical cases where the third-dimensional property also changes.…”

Section: Inverse Compensationmentioning

confidence: 99%

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Three-dimensional hysteresis compensation enhances accuracy of robotic artificial muscles

Zhang

Simeonov

Yip

2018

Smart Mater. Struct.

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Robotic artificial muscles are compliant and can generate straight contractions. They are increasingly popular as driving mechanisms for robotic systems. However, their strain and tension force often vary simultaneously under varying loads and inputs, resulting in three-dimensional hysteretic relationships. The three-dimensional hysteresis in robotic artificial muscles poses difficulties in estimating how they work and how to make them perform designed motions. This study proposes an approach to driving robotic artificial muscles to generate designed motions and forces by modeling and compensating for their three-dimensional hysteresis. The proposed scheme captures the nonlinearity by embedding two hysteresis models. The effectiveness of the model is confirmed by testing three popular robotic artificial muscles. Inverting the proposed model allows us to compensate for the hysteresis among temperature surrogate, contraction length, and tension force of a shape memory alloy (SMA) actuator. Feedforward control of an SMA-actuated robotic bicep is demonstrated. This study can be generalized to other robotic artificial muscles, thus enabling muscle-powered machines to generate desired motions.

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Section: Related Workmentioning

confidence: 99%

Section: Inverse Compensationmentioning

confidence: 99%

Three-dimensional hysteresis compensation enhances accuracy of robotic artificial muscles

Zhang

Simeonov

Yip

2018

Smart Mater. Struct.

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“…Recently, intelligent methods such as neural networks and fuzzy logic system provide a new way to model hysteresis (Li et al, 2013; Ma et al, 2015; Mai et al, 2016; Wang et al, 2018; Zhang and Tan, 2010). Kilicarslan et al (2011) developed an adaptive neuro-fuzzy inference system (ANFIS) for hysteresis modeling. Xu and Zhou (2017) proposed the identification method of KP hysteresis model based on Elman neural networks.…”

Section: Introductionmentioning

confidence: 99%

Elman neural network–based identification of rate-dependent hysteresis in piezoelectric actuators

Zhao

Shen

et al. 2020

Journal of Intelligent Material Systems and Structures

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Rate-dependent hysteresis nonlinearity in piezoelectric actuators severely limits micro- and nanoscale system performance. It is necessary to establish a dynamic model to describe the full behavior of rate-dependent hysteresis. In this article, the Elman neural network–based hysteresis model is developed for piezoelectric actuators. An improved dynamic hysteretic operator is proposed to transform the multi-valued mapping of hysteresis into one-to-one mapping on a newly constructed expanded input space. Then, Elman neural network incorporated with the improved dynamic hysteretic operator is utilized to approximate the behavior of rate-dependent hysteresis. The combination of Elman neural network and the improved dynamic hysteretic operator can dually embody the dynamic property and is capable of fully extracting the characteristics of rate-dependent hysteresis. The experimental results are presented to illustrate the potential of the proposed modeling technique.

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“…Having the capability of combining the fuzzy decision making and ANN learning, ANFIS architectures are useful tools for representing non-linear systems for control applications. In [15,16], the authors use an ANFIS architecture to model the hysteretic response and train an inverse ANFIS controller which utilises the desired output as the controller input and provides the appropriate control action. Having the feed-forward inverse controller combined with a PI controller, the control approach is capable of compensating the hysteretic behaviour of the system and can successfully track a reference trajectory.…”

Section: Introductionmentioning

confidence: 99%

Compensation of hysteresis in a shape memory alloy wire system using linear parameter‐varying gain scheduling control

Kilicarslan¹,

Grigoriadis²,

Song³

2014

IET Control Theory & Applications

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Robust tracking control of shape memory alloy (SMA) systems is a challenge because of their highly non-linear response, non-local memory and lack of differentiability. In this work, a linear parameter-varying (LPV) control method is proposed to compensate for the hysteretic behaviour of an SMA actuator. To provide information on the scheduling variable, the Preisach model of hysteresis is utilised. Parameter-dependent controller is updated based on the real-time computation of the instantaneous scheduling variable from the model. An H ∞ controller is also synthesised by representing the hysteresis as a parametric uncertainty. The controllers are implemented on an SMA actuated experimental system. Experimental validation is conducted for various types of reference inputs which can dramatically change the system's response. These reference signals are designed to excite major, minor and frequency dependent hysteresis loops. Trajectory tracking and disturbance rejection results show that the LPV controller has an improved response and the hysteresis is compensated for the full range of the scheduling variable. The tracking performances of both controllers are also compared when the system is under partial and persistent plant disturbances. For these cases, the LPV controller results in a more robust tracking response because of its less conservative structure.

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Modeling and Hysteresis Compensation in a Thin SMA Wire Using ANFIS Methods

Cited by 9 publications

References 25 publications

Three-dimensional hysteresis compensation enhances accuracy of robotic artificial muscles

Three-dimensional hysteresis compensation enhances accuracy of robotic artificial muscles

Elman neural network–based identification of rate-dependent hysteresis in piezoelectric actuators

Compensation of hysteresis in a shape memory alloy wire system using linear parameter‐varying gain scheduling control

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