System Modeling and Identification the Two-Link Pneumatic Artificial Muscle (PAM) Manipulator Optimized with Genetic Algorithms

In this paper, the application of modified genetic algorithms (MGA) in the parameterization of the 2-link pneumatic artificial muscle (PAM) manipulator is investigated. The new algorithm is proposed from the conventional genetic algorithm (SGA) with some additional strategies, and consequently yields a faster convergence and a more accurate search. Firstly, this near-optimum search technique, MGA-based ID method, is used to identify the parameters of the prototype 2-link pneumatic artificial muscle (PAM) manipulator described by an ARX model in the presence of white noise and this result is validated by comparing with the simple genetic algorithm (SGA) and LMS (least mean-squares) method as well. The parameters of the hysteresis as well as other nonlinear disturbances existing intuitively in the 2-link pneumatic artificial muscle (PAM) manipulator are estimated in a single identification experiment. Experiment results are included to demonstrate the excellent performance of the MGA algorithm in the system modeling and identification of the PAM manipulator. These results can be applied to model and identify other nonlinear systems as well