Efficient PSO-based algorithm for parameter estimation of McKibben PAM model

This study considers the parameter estimation problem for an elaborate nonlinear hybrid model of a McKibben pneumatic artificial muscle (PAM) actuated by a proportional-directional control valve and proposes an efficient particle-swarm-optimization-based algorithm to find adequate model parameters in terms of model accuracy and computation time. A novel approach to making an algorithm more efficient is to focus on the parameter space of the PAM model and to use a support vector machine (SVM) to specify a subset in the parameter space. The inertia of the PSO algorithm is erased to the extent that the particles are allowed to search intensively in the subset region. Furthermore, this study validates the efficiency of the proposed algorithm using three different practical PAM products.

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