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Efficient Algorithm for Constructing a Load-Dependent McKibben Pneumatic Artificial Muscle Model

This study proposes a systematic and efficient algorithm for extending the original hybrid PAM model to obtain a load-dependent model of the McKibben pneumatic artificial muscle (PAM). This algorithm consists of the following two phases: a particle-swarm-optimization (PSO)-based algorithm to find adequate model parameters and a curve fitting algorithm to develop the continuous function of a mass of loaded weight for each load-dependent parameter. Parameter estimation and curve fitting are performed to demonstrate that the proposed algorithm is appropriate and effective.