System Identification and Self-Tuning Pole Placement Control of the Two-Axes Pneumatic Artificial Muscle Manipulator Optimized by Genetic Algorithm

In this paper, self-tuning pole placement control of the 2-axes pneumatic artificial muscle (PAM) manipulator is proposed as an appropriate strategy which can automatically accommodate wide changes in operating conditions, such as payload and time varying parameters of the 2-axes PAM manipulator. This novel proposed control scheme is initially applied to the independent control of the PAM manipulator joint angle position. Proposed pole placement controller utilizes a low order linear approximation of the PAM manipulator ARX model, whose parameters are estimated online from past input and output values by RLS system identification algorithm. Furthermore, parametric values of ARX model are optimized by a modified genetic algorithm (MGA). This superb combination between MGA and self-tuning pole placement controller is developed for tracking the joint angle position of the prototype 2-axes PAM manipulator. Simulation and experiment results demonstrate the excellent performance of the proposed control scheme. These results can be applied to model, identify and control other highly nonlinear systems as well.