Parameter extraction for identifying product type of mckibben pneumatic artificial muscles

This study demonstrates that various unknown parameters used in nonlinear models of McKibben pneumatic artificial muscles (PAMs) can characterize the features of McKibben PAM products. By focusing on a parameter space in the PAM model, this study employs a support vector machine (SVM) to determine which unknown parameters characterize each PAM product. For validation, three different PAM products are analyzed to observe whether the resulting minimal combination of parameters will help to identify the product. The observation is expected to provide prior PAM knowledge that can be used to develop efficient parameter estimation and capture aging degradation, which are important for robust estimation and control in PAM systems.

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