Proceedings Article10.1109/FUZZY.1998.687520
Fuzzy model based predictive control
Jin-Hwan Kim,Uk-Youl Huh +1 more
- 04 May 1998
- Vol. 1, pp 405-409
32
TL;DR: The fuzzy model based predictive control is demonstrated by the nonlinear model with the sensitive dynamics and it is compared with the predictive control with the linear based model.
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Abstract: In this paper, the predictive control with the fuzzy model is developed. Predictive control is an effective technique, and the use of a fuzzy model enhances its usefulness for nonlinear processes. The proper fuzzy model is obtained by fuzzy c-means method. Finally, the fuzzy model based predictive control is demonstrated by the nonlinear model with the sensitive dynamics and it is compared with the predictive control with the linear based model.
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Citations
A TSK-type recurrent fuzzy network for dynamic systems processing by neural network and genetic algorithms
TL;DR: The proposal calls for the design of TRFN by either neural network or genetic algorithms depending on the learning environment, which develops from a series of recurrent fuzzy if-then rules with TSK-type consequent parts.
Prediction and identification using wavelet-based recurrent fuzzy neural networks
Cheng-Jian Lin,Cheng-Chung Chin +1 more
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TL;DR: The proposed WRFNN model combines the traditional Takagi-Sugeno-Kang fuzzy model and the wavelet neural networks to create a wavelet-based recurrent fuzzy neural network for prediction and identification of nonlinear dynamic systems.
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Hybrid fuzzy predictive control based on genetic algorithms for the temperature control of a batch reactor
TL;DR: The GA-hybrid predictive control strategy proved to be a suitable method for the control of hybrid systems, giving similar performance to that of typical hybrid predictive control strategies and a significant saving with respect to the computation time.
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Design and Stability Analysis of Fuzzy Model-based Predictive Control—A Case Study
Sašo Blažič,Igor Škrjanc +1 more
TL;DR: The proposed fuzzy model based predictive control algorithm is developed in the state space and is given in analytical form, which is an advantage in comparison with optimisation based control schemes.
Integrated control, diagnosis and reconfiguration of a heat exchanger
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References
Fuzzy identification of systems and its applications to modeling and control
T. Takagi,Michio Sugeno +1 more
- 01 Jan 1985
TL;DR: A mathematical tool to build a fuzzy model of a system where fuzzy implications and reasoning are used is presented and two applications of the method to industrial processes are discussed: a water cleaning process and a converter in a steel-making process.
20.1K
Generalized predictive control—Part I. The basic algorithm
TL;DR: A novel method—generalized predictive control or GPC—is developed which is shown by simulation studies to be superior to accepted techniques such as generalized minimum-variance and pole-placement and to be a contender for general self-tuning applications.
3.8K
A fuzzy-logic-based approach to qualitative modeling
Michio Sugeno,T. Yasukawa +1 more
TL;DR: A general approach to quali- tative modeling based on fuzzy logic is discussed, which proposes to use a fuzzy clustering method (fuzzy c-means method) to identify the structure of a fuzzy model.
2.5K
Building Sugeno-type models using fuzzy discretization and orthogonal parameter estimation techniques
Liang Wang,Reza Langari +1 more
TL;DR: This paper develops a new approach to building Sugeno-type models by separating the premise identification from the consequence identification, while these are mutually related in the previous methods.
200
Building Sugeno-type models using fuzzy discretization and orthogonal parameter estimation techniques
Liang Wang,Reza Langari +1 more
- 18 Dec 1994
TL;DR: This paper develops a new approach to building Sugeno-type models to separate premise identification from consequence identification, while these are mutually related in the previous methods.
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