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유전알고리즘을 이용한 이동로봇의 예측제어

Predictive Control for Mobile Robots Using Genetic Algorithms

  • Son, Hyun-sik (Department of Electrical and Computer Engineering, Pusan National University) ;
  • Park, Jin-hyun (Dep. of Mechatronics Engineering, Kyeongnam National University of Science and Technology) ;
  • Choi, Young-kiu (Department of Electrical Engineering, Pusan National University)
  • 투고 : 2016.11.29
  • 심사 : 2016.12.19
  • 발행 : 2017.04.30

초록

본 논문에서 이동로봇의 기준궤적추적제어를 위한 예측제어방법을 다룬다. 예측제어는 예측모델을 사용하여 기준궤적과 시스템 상태 간의 미래오차들을 최소화시키는 효과적인 제어방법으로 알려져 있으나, 실시간 계산량이 너무 많아 화공정 플랜트와 같이 매우 느린 시스템에 한정되어 적용되었다. 근래에는 컴퓨터 기술 발달로 고속계산이 가능하여 이동로봇과 같은 빠른 시스템에도 예측제어방법이 도입되고 있다. 그런데 예측제어기에서 제어성능과 관계된 제어 파라미터들이 있는데 임의로 지정되어 최적화되지 못하였다. 본 논문에서 이동로봇 예측제어기 성능 개선을 위해 관련 제어 파라미터들을 유전알고리즘으로 최적화시켰고 모의실험을 통해 제어성능이 개선됨을 확인하였다.

This paper deals with predictive control methods of mobile robots for reference trajectory tracking control. Predictive control methods using predictive model are known as effective schemes that minimize the future errors between the reference trajectories and system states; however, the amount of real-time computation for the predictive control are huge so that their applications were limited to slow dynamic systems such as chemical processing plants. Lately with high computing power due to advanced computer technologies, the predictive control methods have been applied to fast systems such as mobile robots. These predictive controllers have some control parameters related to control performance. But these parameters have not been optimized. In this paper we employed the genetic algorithm to optimize the control parameters of the predictive controller for mobile robots. The improved performances of the proposed control method are demonstrated by the computer simulation studies.

키워드

참고문헌

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피인용 문헌

  1. The Design of Trajectory Controller using Neural Networks Simulating Predictive Control Method for Mobile Robot vol.19, pp.4, 2017, https://doi.org/10.17958/ksmt.19.4.201708.538
  2. Design of Model Predictive Controllers with Velocity and Acceleration Constraints vol.20, pp.6, 2017, https://doi.org/10.17958/ksmt.20.6.201812.809