System Modeling Identification and Control of the Two-Link Pneumatic Artificial Muscle 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 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 will be validated by comparing with the simple genetic algorithm (GA) and LMS (least mean-squares) method as well. A proposed self-tuning control algorithm minimum variance control (MVC) is taken for tracking the joint angle position of this PAM manipulator. Simulation 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, identify and control other highly nonlinear systems as well.