Browse > Article
http://dx.doi.org/10.6109/jkiice.2018.22.4.602

Optimization of Mobile Robot Predictive Controllers Under General Constraints  

Park, Jin-Hyun (Department of Mechatronics Eng., Kyeongnam National Univ. of Science and Technology)
Choi, Young-Kiu (Department of Electrical Engineering, Pusan National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.22, no.4, 2018 , pp. 602-610 More about this Journal
Abstract
The model predictive control is an effective method to optimize the current control input that predicts the current control state and the future error using the predictive model of the control system when the reference trajectory is known. Since the control input can not have a physically infinitely large value, a predictive controller design with constraints should be considered. In addition, the reference model $A_r$ and the weight matrices Q, R that determine the control performance of the predictive controller are not optimized as arbitrarily designated should be considered in the controller design. In this study, we construct a predictive controller of a mobile robot by transforming it into a quadratic programming problem with constraints, The control performance of the mobile robot can be improved by optimizing the control parameters of the predictive controller that determines the control performance of the mobile robot using genetic algorithm. Through the computer simulation, the superiority of the proposed method is confirmed by comparing with the existing method.
Keywords
mobile robots; predictive control; constraints; genetic algorithm; control parameters;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 S. Akiba, T. Zanma, and M. Ishida, "Optimal tracking control of two-wheeled mobile robots based on model predictive control," in Proceeding of the 11th IEEE International Workshop on Advanced Motion Control, Nagaoka, Niigata: Japan, pp. 454-459, May 2010.
2 G. Klancar and I. skrjanc, "Tracking-error model-based predictive control for mobile robots in real time," Robotics and Autonomous Systems, vol. 55, no. 6, pp. 460-469, June 2007.   DOI
3 H. S. Son, J. H. Park and Y. K. Choi, "Predictive control for mobile robots using genetic algorithms," Journal of the Korea Institute of Information and Communication Engineering, vol. 21, no. 4, pp. 698-707, Apr. 2017.   DOI
4 L. Wang, Model Predictive Control System Design and Implementation Using MATLAB, London: UK, Springer Verlag, 2009.
5 J. H. Park and Y. K. Choi, "Control gain optimization for mobile robots using neural networks and genetic algorithms," Journal of the Korea Institute of Information and Communication Engineering, vol. 20, no. 4, pp. 698-707, Apr. 2016.   DOI
6 R. Siegwart, I. R. Nourbakhsh, and D. Scaramuzza, Introduction to Autonomous Mobile Robots, 2nd ed. Cambridge, Massachusetts, London, England: The MIT Press, 2013.
7 D. Gu and H. Hu, "Receding horizon tracking control of wheeled mobile robots," IEEE Transactions on Control System Technology, vol. 14, no. 4, pp. 743-749, July 2006.   DOI
8 R. S. Ortigoza and J. R. Sanchez, "Trajectory tracking control for a differential drive wheeled mobile robot considering the dynamics related to the actuators and power stage," IEEE Latin America Transactions, vol. 14, no. 2, pp. 657-664, Feb. 2016.   DOI
9 K. M. Lynch and F. C. Park, Modern Robotics: Mechanics, Planning and Control, New York, NY: Cambridge University Press, 2017.
10 E. F. Camacho and C. Bordons, Model Predictive Control, London, UK: Springer-Verlag, 2007.