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Sliding Mode Controller with Sliding Perturbation Observer Based on Gain Optimization using Genetic Algorithm
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  • Sliding Mode Controller with Sliding Perturbation Observer Based on Gain Optimization using Genetic Algorithm
  • Sliding Mode Controller with Sliding Perturbation Observer Based on Gain Optimization using Genetic Algorithm
저자명
You. Ki-Sung,Lee. Min-Cheol,Yoo. Wan-Suk
간행물명
KSME international journal
권/호정보
2004년|18권 4호|pp.630-639 (10 pages)
발행정보
대한기계학회
파일정보
정기간행물|ENG|
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기타
이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
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기타언어초록

The Stewart platform manipulator is a closed-kinematics chain robot manipulator that is capable of providing high structural rigidity and positional accuracy. However, this is a complex and nonlinear system, so the control performance of the system is not so good. In this paper, a new robust motion control algorithm is proposed. The algorithm uses partial state feedback for a class of nonlinear systems with modeling uncertainties and external disturbances. The major contribution is the design of a robust observer for the state and the perturbation of the Stewart platform, which is combined with a variable structure controller (VSC). The combination of controller and observer provides the robust routine called sliding mode control with sliding perturbation observe. (SMCSPO). The optimal gains of SMCSPO, which is determined by nominal eigenvalues, are easily obtained by genetic algorithm. The proposed fitness function that evaluates the gain optimization is to put sliding function. The control performance of the proposed algorithm is evaluated by the simulation and experiment to apply to the Stewart platform. The results showed high accuracy and good performance.