Innovations in generalized predictive control using TSK fuzzy-based approach
ABSTRACT The main idea of the paper presented here is to reorganize the linear model based generalized predictive control (LGPC) approach in dealing with a severe nonlinear system. The proposed control strategy is investigated using a TSK fuzzy-based LGPC approach as well as a TSK fuzzy-based model approach. The TSK fuzzy-based model approach is accurately identified as the best representation of the nonlinear system, at each instant of time. And subsequently the TSK fuzzy-based LGPC approach is realized in line with the system modeling outcomes. In order to present the validity of the proposed control strategy, the simulations are carried out in deriving an industrial tubular heat exchanger system as a highly nonlinear system. As is obvious from its acquired results, the proposed control strategy is appropriate in comparison with the traditional LGPC approach.
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ABSTRACT: The purpose of this paper is to deal with a novel intelligent predictive control scheme using the multiple models strategy with its application to an industrial tubular heat exchanger system. The main idea of the strategy proposed here is to represent the operating environments of the system, which have a wide range of variation in the span of time by several local explicit linear models. In line with this strategy, the well-known linear generalized predictive control (LGPC) schemes are initially designed corresponding to each one of the linear models of the system. After that, the best model of the system and the LGPC control action are precisely identified, at each instant of time, by an intelligent decision maker scheme (IDMS), which is playing the so important role in realizing the finalized control action for the system. In such a case, as soon as each model could be identified as the best model, the adaptive algorithm is implemented on the both chosen model and the corresponding predictive control schemes. In conclusion, for having a good tracking performance, the predictive control action is instantly updated and is also applied to the system, at each instant of time. In order to demonstrate the effectiveness of the proposed approach, simulations are carried out and the results are compared with those obtained using a nonlinear GPC (NLGPC) scheme as a benchmark approach realized based on the Wiener model of the system. In agreement with these results, the validity of the proposed control scheme can tangibly be verified. KeywordsFuzzy adaptive predictive control scheme–Nonlinear generalized predictive control scheme–Multiple models strategy–Intelligent decision maker scheme–Tubular heat exchanger systemApplied Intelligence 04/2012; 34(1):127-140. · 1.85 Impact Factor
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ABSTRACT: This paper describes an application of intelligence-based predictive scheme to load-frequency control (LFC) in a two-area interconnected power system. In this investigation, at first, a dynamic model of the present system has to be considered and subsequently an efficient control scheme which is organized based on Takagi-Sugeno-Kang (TSK) fuzzy-based scheme and linear generalized predictive control (LGPC) scheme needs to be developed. In the control scheme proposed, frequency deviation versus load electrical power variation could efficiently be dealt with, at each instant of time. In conclusion, in order to validate the effectiveness of the proposed control scheme, the whole of outcomes are simulated and compared with those obtained using a nonlinear GPC (NLGPC), as a benchmark approach, which is implemented based on the Wiener model of this power system. The validity of the proposed control scheme is tangibly verified in comparison with the previous one. KeywordsLoad-frequency control–Intelligence-based predictive scheme–Linear generalized predictive control scheme–Two-area interconnected power system–Nonlinear generalized predictive control schemeApplied Intelligence 05/2012; 35(3):457-468. · 1.85 Impact Factor
Conference Proceeding: A case study for fuzzy adaptive multiple models predictive control strategy[show abstract] [hide abstract]
ABSTRACT: The purpose of the paper presented here is to deal with the well-known linear generalized predictive control (LGPC) scheme based on multiple models strategy for a tubular heat exchanger system. In this control strategy, the operating environments of the system are first represented by multiple explicit linear models. Then the best model of the system is precisely identified by a novel intelligent decision mechanism (IDM), where is organized in association with the fuzzy adaptive Kalman filter and recursive weight generator approaches. As soon as the best model of the system is identified, the corresponding predictive control action is instantly implemented on the system. In order to demonstrate the effectiveness of the proposed strategy, simulations are carried out and the outcomes are compared with those obtained using the nonlinear GPC (NLGPC) approach. The results can verify the validity of the proposed control scheme.Industrial Electronics, 2009. ISIE 2009. IEEE International Symposium on; 08/2009