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

Abstract

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 ·

An Intelligent Multi-multivariable Dynamic Matrix Control Scheme for a 160 MW Drum-type Boiler-Turbine System

“…Regarding the proposed IBDM, the best multivariable linear model of the system and the corresponding multivariable DMC scheme are identified through this intelligence-based decision maker at each instant of time, as outlined by the following [6][7][8][9][10][11][12][13][14][15][16].…”

Applying an intelligence-based adaptive multi-predictive control strategy to a two-area interconnected power system

“…In fact, the present complex system operates in the MOP, which may change from one to another, suddenly Clarke, 1988;Flores et al, 2005;Franco et al, 2004;Gadkar et al, 2003;Huang et al, 2002;Mazinan, 2012a,b;Mazinan and Hosseini, 2011;Mazinan and Kazemi, 2010a,b, 2011a; Figure 2 The strategy of the two-area interconnected power system. Mazinan and Sadati, 2010a,b,c;Mazinan and Sheikhan, 2012;Qi-Gang et al, 2003;Rueda et al, 2005;Saha et al, 2004;Sousa and Kaymak, 2001;Zam arreno and Vega, 1999). It should be noted that the present system behavior is either a nonlinear or linear time variant, where a linear fixed model may not really lead to the expected performance.…”

High efficiency fault-detection and fault-tolerant control approach in Tennessee Eastman process via fuzzy-based neural network representation

“…Finally, a fault-tolerant active control approach is proposed to combine output results. The importance of this research is from actual data of the TE process and complete simulation in MATLAB to detect four faults (which there is concurrency possibility for two of them) and five different working modes in the system as real-time by the fuzzy-neural fusion classifier and compare classification results with the presented sample in other reliable materials [18][19][20][21][22][23][24][25][26][27][28][29][30].…”