JACIII Vol.11 No.8 pp. 931-936
doi: 10.20965/jaciii.2007.p0931


Soft-Target-Based Predictive Fuzzy Control for a Cart-Pendulum System with Dynamic Constraints

Yougen Chen and Seiji Yasunobu

Department of Intelligent Interaction Technologies, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan

March 14, 2007
August 10, 2007
October 20, 2007
soft target, predictive fuzzy control (PFC), dynamic constraint, cart-pendulum system
Human decisions to act are based on broad targets and respond flexibly in different situations. Such, self-adaptation to dynamic constraints is difficult but important for autonomous control. Conventional control usually uses a single target that results in inflexibility in responding to dynamic environments such as changes in constraints. We propose a predictive fuzzy intelligent controller based on soft targets defined as a series of target sets that include many target elements with different satisfaction grades and are converted to target setting knowledge by fuzzy logic. This controller was applied to upswing and stabilization control of a cart-pendulum system with dynamic changing limit positions as constraints to realize situational self-adaptation and target self-regulation. Simulation and experiments demonstrated the feasibility of our soft-target-based intelligent controller.
Cite this article as:
Y. Chen and S. Yasunobu, “Soft-Target-Based Predictive Fuzzy Control for a Cart-Pendulum System with Dynamic Constraints,” J. Adv. Comput. Intell. Intell. Inform., Vol.11 No.8, pp. 931-936, 2007.
Data files:
  1. [1] L. A. Zadeh, “From Computing with Numbers to Computing with Words – From Manipulation of Measurements to Manipulation of Perception,” IEEE Transactions on Circuits and Systems, Vol.45, No.1, pp. 105-119, 1999.
  2. [2] S. Yasunobu and H. Yamasaki, “Evolutionary Control Method and Swing up & Stabilization Control of Inverted Pendulum,” IFSA2001, pp. 2078-2083, 2001.
  3. [3] R. Ordonez and J. Zumberge, “Adaptive Fuzzy Control: Experiment and Comparative Analyses,” IEEE Transactions on Fuzzy Systems, Vol.5, No.2, pp. 167-188, 1997.
  4. [4] R. E. Bellman and L. A. Zadeh, “Decision-making in Fuzzy Environment,” Management Science, Vol.17, No.4, pp. B-141-164, 1970.
  5. [5] Y. G. Chen and S. Yasunobu, “Soft Target Based Intelligent Controller for a System with Dynamic Restriction,” 3rd Int. Conf. on Soft Computing and Intelligent Systems and 7th Int. Symposium on advanced Intelligent Systems, pp. 1949-1954, 2006.
  6. [6] S. Yasunobu and T. Matsubara, “An Intelligent Controller Based on Fuzzy Target and Its Application to Car Like Vehicle,” Proc. of SICE Annual Conf. 2005 in Okayama, pp. 2489-2494, 2005.
  7. [7] S. Yasunobu and S. Miyamoto, “Automatic train operation by predictive fuzzy control,” Industrial Application of Fuzzy Control, M. Sugeno (Ed.), North Holland, pp. 1-18, 1985.

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