Characteristic Extraction for Model Parameters of McKibben Pneumatic Artificial Muscles

Ishikawa, Tsuneo; Nishiyama, Yu; Kogiso, Kiminao

doi:10.9746/jcmsi.11.357

Cited by 3 publications

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, an emprical modeling approach is given by Wickramtunge and Leephakpreda which relates force and muscle legth as a nonlinear elastic relation [8]. Moreover, Ishikawa et al also performed model parameter extraction of structurally different PAMs using SVM [9]. There are more studies to resolve the modeling issue in literature, such as virtual work , empirical and phenomenological models, AI based models.…”

Section: Introductionmentioning

confidence: 99%

An ANFIS based inverse modeling for pneumatic artificial muscles

Baysal

2020

International Journal of Applied Mathematics Electronics and Computers

View full text Add to dashboard Cite

Pneumatic Artificial Muscles (PAM) are soft actuators with advantages of high force to weight ratio, flexible structure and low cost. On the other hand, their inherent nonlinear characteristics yield difficulties in modeling and control actions, which is an important factor restricting use of PAM. In literature, there are various modeling approaches such as virtual work , empirical and phenomenological models. However, they appear as either much complicated or are approximate ones as a variable stiffness spring for model with nonlinear input-output relationship. In this work, the behaviour of PAM is interpreted as an integrated response to pressure input that results in a simultaneous force and muscle length change. The integrated response behaviour of PAM is not combined effectively in terms of simultaneous resultant force and muscle contraction in many existing models. In order to implement that response, standard identification methods , for instance NNARX, are not suitable for modeling this behaviour. Moreover, an inverse modeling with grey box approach is proposed in order to utilize the model in control applications. Since Neuro-Fuzzy inference systems are universal estimators, the modeling is implemented by an ANFIS structure using the experimental data collected from PAM test bed. According to implementation results, the ANFIS based inverse model has yielded satisfactory performance deducing that it could be a simple and effective solution for PAM modeling and control issue.

show abstract

Section: Introductionmentioning

confidence: 99%

An ANFIS based inverse modeling for pneumatic artificial muscles

Baysal

2020

International Journal of Applied Mathematics Electronics and Computers

View full text Add to dashboard Cite

show abstract

Neural Network Based Inverse Modelling for Pneumatic Artificial Muscles

Baysal¹

2021

European Journal of Science and Technology

View full text Add to dashboard Cite

Pneumatic Artificial Muscles (PAM) are soft actuators with advantages such as high force to weight ratio, flexible structure and low cost. Pneumatic Artificial Muscles have inherent compliance that makes them feasible for exoskeletons and rehabilitation robots. However, their inherent nonlinear characteristics yield difficulties in modelling and control actions, which is an important factor restricting use of PAM. The compliance of PAM is associated with nonlinearity, hysteresis, and time varying characteristics, which makes it more difficult to model the dynamics and operation with model based high-performance controllers. Although there are many studies to overcome the modelling issue such as virtual work , empirical and phenomenological models, they are either much complicated or very approximate ones as a variable stiffness spring for model with nonlinear input-output relationship. Based on the analysis of well known previous modeling works in our PAM test bed, it has been observed that efficacy of the those methods are limited for representing the physical behaviour of PAM and thus there is still requirement for simple and effective models . In this work, apart from previous modeling approaches, the behaviour of PAM is foreseen as an integrated response to pressure input, which results in simultaneous force and muscle length change. Therefore, standard direct input-output identification methods are not suitable for modelling that behaviour. An inverse modeling approach is proposed in order to utilize it in control applications. The black box model is implemented by an Artificial Neural Network (ANN) structure using the experimental data collected from the PAM test bed. According to implementation results, an ANN based inverse model has yielded satisfactory performance deducing that it could be a simple and effective solution for PAM modelling and control .

show abstract

Pnömatik Yapay Kaslar için Yapay Sinir Ağı Esaslı Ters Modelleme

Baysal

2020

European Journal of Science and Technology

View full text Add to dashboard Cite

Pneumatic Artificial Muscles (PAM) are soft actuators with advantages such as high force to weight ratio, flexible structure and low cost. Pneumatic Artificial Muscles have inherent compliance that makes them feasible for exoskeletons and rehabilitation robots. However, their inherent nonlinear characteristics yield difficulties in modelling and control actions, which is an important factor restricting use of PAM. The compliance of PAM is associated with nonlinearity, hysteresis, and time varying characteristics, which makes it more difficult to model the dynamics and operation with model based high-performance controllers. Although there are many studies to overcome the modelling issue such as virtual work , empirical and phenomenological models, they are either much complicated or very approximate ones as a variable stiffness spring for model with nonlinear input-output relationship. Based on the analysis of well known previous modeling works in our PAM test bed, it has been observed that efficacy of the those methods are limited for representing the physical behaviour of PAM and thus there is still requirement for simple and effective models. In this work, apart from previous modeling approaches, the behaviour of PAM is foreseen as an integrated response to pressure input, which results in simultaneous force and muscle length change. Therefore, standard direct input-output identification methods are not suitable for modelling that behaviour. An inverse modeling approach is proposed in order to utilize it in control applications. The black box model is implemented by an Artificial Neural Network (ANN) structure using the experimental data collected from the PAM test bed. According to implementation results, an ANN based inverse model has yielded satisfactory performance deducing that it could be a simple and effective solution for PAM modelling and control .

show abstract

Characteristic Extraction for Model Parameters of McKibben Pneumatic Artificial Muscles

Cited by 3 publications

References 24 publications

An ANFIS based inverse modeling for pneumatic artificial muscles

An ANFIS based inverse modeling for pneumatic artificial muscles

Neural Network Based Inverse Modelling for Pneumatic Artificial Muscles

Pnömatik Yapay Kaslar için Yapay Sinir Ağı Esaslı Ters Modelleme

Contact Info

Product

Resources

About