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S. W. Chan, J. Lilly, D. W. Repperger and J. E. Berlin, “Fuzzy PD+I Learning Control for a Pneumatic Muscle,” Proceedings of 2003 IEEE International Conference on Fuzzy Systems, St. Louis, 9 July 2003, pp. 278-283.

has been cited by the following article:

  • TITLE: Modeling and Adaptive Self-Tuning MVC Control of PAM Manipulator Using Online Observer Optimized with Modified Genetic Algorithm

    AUTHORS: Ho Pham Huy Anh, Nguyen Thanh Nam

    KEYWORDS: Modified Genetic Algorithm (MGA), Online System Identification, ARX Model, Pneumatic Artificial Muscle (PAM), PAM Manipulator, Minimum Variance Controller (MVC)

    JOURNAL NAME: Engineering, Vol.3 No.2, March 18, 2011

    ABSTRACT: In this paper, the application of modified genetic algorithms (MGA) in the optimization of the ARX Model-based observer of the Pneumatic Artificial Muscle (PAM) manipulator is investigated. The new MGA algorithm is proposed from the genetic algorithm with important additional strategies, and consequently yields a faster convergence and a more accurate search. Firstly, MGA-based identification method is used to identify the parameters of the nonlinear PAM manipulator described by an ARX model in the presence of white noise and this result will be validated by MGA and compared with the simple genetic algorithm (GA) and LMS (Least mean-squares) method. Secondly, the intrinsic features of the hysteresis as well as other nonlinear disturbances existing intuitively in the PAM system are estimated online by a Modified Recursive Least Square (MRLS) method in identification experiment. Finally, a highly efficient self-tuning control algorithm Minimum Variance Control (MVC) is taken for tracking the joint angle position trajectory of this PAM manipulator. Experiment results are included to demonstrate the excellent performance of the MGA algorithm in the NARX model-based MVC control system of the PAM system. These results can be applied to model, identify and control other highly nonlinear systems as well.