Browse > Article
http://dx.doi.org/10.6109/jkiice.2018.22.1.9

Predictive Control based on Genetic Algorithm for Mobile Robots with Constraints  

Choi, Young-Kiu (Department of Electrical Engineering, Pusan National University)
Park, Jin-Hyun (Dept. of Mechatronics Engineering, Kyeognam National Univ. of Science and Technology)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.22, no.1, 2018 , pp. 9-16 More about this Journal
Abstract
Predictive control is a very practical method that obtain the current input that minimizes the future errors of the reference command and state by use of the predictive model of the controlled object, and can also consider the constraints of the state and input. Although there have been studies in which predictive control is applied to mobile robots, performance has not been optimized as various control parameters for determining control performance have been arbitrarily specified. In this paper, we apply the genetic algorithm to the trajectory tracking control of a mobile robot with input constraints in order to minimize the trajectory tracking errors through control parameter tuning, and apply the quadratic programming Hildreth method to reflect the input constraints. 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 K. M. Lynch and F. C. Park, Modern Robotics: Mechanics, Planning and Control, New York, NY: Cambridge University Pressr, 2017.
2 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 Trans, vol. 14, no. 2, pp. 657-664, Feb 2016.   DOI
3 K. Shojaei and A. M. Shahri, "Output feedback tracking control of uncertain non-holonomic wheeled mobile robots: a dynamic surface control approach," IET Control Theory and Applications, vol. 6, no. 2, pp. 216-228, Jan 2012.   DOI
4 Y. Wang, S. Wang, R. Tan, and Y. Jiang, "Motion control of a wheeled mobile robot using digital acceleration control method," International Journal of Innovative Computing, Information and Control, vol. 9, no. 1, pp. 387-396, Jan 2013.
5 E. F. Camacho and C. Bordons, Model Predictive Control, London, UK: Springer-Verlag, 2007.
6 D. Gu and H. Hu, "Receding horizon tracking control of wheeled mobile robots," IEEE Trans on Control System Technology, vol. 14, no. 4, pp. 743-749, July 2006.   DOI
7 C. T. Lin and C. S. G. Lee, Neural Fuzzy Systems, Upper Saddle River, NJ: Prentice Hall, 1996.
8 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.
9 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
10 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
11 L. Wang, Model Predictive Control System Design and Implementation Using MATLAB, London: UK, Springer Verlag, 2009.
12 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