A case study for fuzzy adaptive multiple models predictive control strategy

Kazemi²

2010

IIM

Self Cite

This paper describes a new approach to intelligent model based predictive control scheme for deriving a complex system. In the control scheme presented, the main problem of the linear model based predictive control theory in dealing with severe nonlinear and time variant systems is thoroughly solved. In fact, this theory could appropriately be improved to a perfect approach for handling all complex systems, provided that they are firstly taken into consideration in line with the outcomes presented. This control scheme is organized based on a multi-fuzzy-based predictive control approach as well as a multi-fuzzy-based predictive model approach, while an intelligent decision mechanism system (IDMS) is used to identify the best fuzzy-based predictive model approach and the corresponding fuzzy-based predictive control approach, at each instant of time. In order to demonstrate the validity of the proposed control scheme, the single linear model based generalized predictive control scheme is used as a benchmark approach. At last, the appropriate tracking performance of the proposed control scheme is easily outperformed in comparison with previous one.Keywords: multi-fuzzy-based predictive control approach, multi-fuzzy-based predictive model approach, intelligent decision mechanism system

Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Approach to Intelligent Model Based Predictive Control Scheme

Kazemi²

2010

IIM

Self Cite

“…The proposed control scheme is implemented in association with LGPC scheme and fuzzy-based scheme [17][18][19][20][21][22][23][24][25][26][27][28][29][30]. Prior to analyzing this control strategy, both LGPC and nonlinear GPC (NLGPC) schemes have to be represented.…”

Section: The Proposed Control Schemementioning

confidence: 99%

Application of intelligence-based predictive scheme to load-frequency control in a two-area interconnected power system

Hosseini

2010

Appl Intell

Self Cite

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.Keywords Load-frequency control · Intelligence-based predictive scheme · Linear generalized predictive control scheme · Two-area interconnected power system · Nonlinear generalized predictive control scheme

“…The proposed research work is different from the previous ones. All in all, we organize a multi-GPC scheme as well as a multi-TSK fuzzy-based model approach to deal with the whole of the chosen operating environments of the system, in an accurate manner, as long as a new GPC identifier is used to decide on GPC schemes for identifying the best one [34][35][36][37][38][39][40][41].…”

Section: Introductionmentioning

confidence: 99%

On the practice of artificial intelligence based predictive control scheme: a case study

Sheikhan

2010

Appl Intell

Self Cite

This paper describes a novel artificial intelligence based predictive control scheme for the purpose of dealing with so many complicated systems. In the control scheme proposed here, the system has to be first represented through a multi-Takagi-Sugeno-Kang (TSK) fuzzy-based model approach to make an appropriate prediction of the system behavior. Subsequently, a multi-generalized predictive control (GPC) scheme, which is organized based on a number of GPC schemes, is realized in line with the investigated model outcomes, at chosen operating points of the system. In case of the proposed control strategy realization, the investigated multi-GPC scheme is instantly updated to handle the system by activating the best control scheme through a new GPC identifier, while the system output is suddenly varied with respect to time. To present the applicability of the proposed control scheme, an industrial tubular heat exchanger system and also a drum-type boiler-turbine system have been chosen to drive through the proposed strategy. In such a case, the simulations are carried out and the corresponding results are compared with those obtained using traditional GPC scheme in addition to nonlinear GPC (NLGPC) scheme, as benchmark approaches, where the acquired results of the proposed control scheme are desirably verified.Keywords Multi-TSK fuzzy-based model approach · Multi-GPC scheme · GPC scheme · NLGPC scheme · GPC identifier · Industrial tubular heat exchanger system ·