In this paper, a simple fuzzy logic based intelligent mechanism is developed for predicting and controlling a chaotic system to a desired target, using only input–output data obtained from the unknown (or uncertain) underlying chaotic system. In the chaos prediction phase, a fuzzy system approach incorporating with Gaussian type of fuzzy membership functions is used. Only system input–output data are needed for prediction, and a recursive least-squares computational algorithm is employed for the calculation. In the controller design phase, the Lyapunov stability criterion is used, which forms the basis of the main design principle. Some simulation results on the chaotic Sin map and Henon map are given, for both prediction and control, to illustrate the effectiveness and control performance of the proposed method.
[1]
Ying-Cheng Lai,et al.
Controlling chaos
,
1994
.
[2]
Ying Chen,et al.
Identifying chaotic systems via a Wiener-type cascade model
,
1997
.
[3]
T. W. Frison.
Controlling chaos with a neural network
,
1992,
[Proceedings 1992] IJCNN International Joint Conference on Neural Networks.
[4]
Celso Grebogi,et al.
Using small perturbations to control chaos
,
1993,
Nature.
[5]
Hong Wang,et al.
Controlling chaos via model-based fuzzy control system design
,
1997,
Proceedings of the 36th IEEE Conference on Decision and Control.