Identification and control of a heat flow system based on the Takagi-Sugeno fuzzy model using the grey wolf optimization algorithm

Jovanović, Radiša; Zarić, Vladimir

doi:10.2298/tsci210825324j

Cited by 3 publications

(4 citation statements)

References 30 publications

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“…In addition, as car ownership continues to increase, the gap between them is increasingly worrying [3]. Hence, for these two which are environment pollution and the needy resources, many countries come to deal with the problems which can generally be divided into three categories: different technical routes [4] (large): pure electric vehicle (BEV), hybrid electric vehicle (HEV), and fuel cell electric vehicle (FCEV) [5]. Table 1 shows the characteristics of the three types of electric vehicles.…”

Section: Introductionmentioning

confidence: 99%

Collaborative Control and Optimization Mechanism of Thermal Management of New Energy Vehicles

Huang

2022

Advances in Multimedia

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Global issues such as global warming, rising sea level, melting Arctic and Antarctic glaciers, deterioration of the natural environment, and depletion of fossil energy have attracted increasing attention. As the concept of environmental protection has become increasingly popular, the concept of new energy automobiles has been proposed to keep the heat up. Especially in recent years, with the advantages of energy conservation, environmental protection, and low noise, new energy automobiles have changed from concept to industry and have been generally accepted and recognized by consumers in the consumer market. The increasingly stringent energy crisis and emission regulations have put forward more stringent requirements for modern automobiles. The thermal management of the new generation of intelligent automobiles is not only limited to simply solving the problem of engine heat dissipation but also involves reliability, power, economy, emissions, and comfort. It is an important vehicle development technology with many performances such as performance. The integrated thermal management of new energy vehicles includes engine cooling, oil cooling, air conditioning refrigeration, HVAC heating, supercharged intercooling, low cycle thermal fatigue, and thermal damage. For new energy vehicles such as hybrid and pure electric vehicles, it also includes motor cooling, motor controller cooling, and power battery temperature control. For the purpose of vehicle thermal management optimization design, this paper innovatively proposes IVTM technical solutions, relying on multidimensional numerical calculation coupling and multiobjective collaborative optimization control to integrate system design, scheme evaluation, performance analysis, dynamic control, and collaborative optimization. Through the integrated thermal management collaborative control strategy based on the full operating conditions of the new energy vehicle, the comprehensive improvement of multiple evaluation indicators such as system design performance, thermal management, and control performance and economy is achieved.

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Section: Introductionmentioning

confidence: 99%

Collaborative Control and Optimization Mechanism of Thermal Management of New Energy Vehicles

Huang

2022

Advances in Multimedia

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“…When the system was created in the traditional manner and began with nonzero initial conditions, the response was significantly poorer than when the system was designed in the novel manner and began with nonzero initial conditions as well. The results show that the improvement achieved, compared to individual controllers that take into account nonzero initial conditions applied to one local linear plant model [1,2], was transferred to the case discussed in this article, which is the application of a PDC control system to a nonlinear TS fuzzy plant model [25].…”

Section: Discussionmentioning

confidence: 84%

“…Review paper [22] gives an idea of past, present and future trends in the application of this type of system. Multiple articles on the TS discrete-time fuzzy models can be found in the literature, including [23][24][25]. The fuzzy model proposed by Takagi and Sugeno [26] is described by fuzzy IF-THEN rules, which depict local linear input-output relations of a nonlinear system.…”

Section: Introductionmentioning

confidence: 99%

Discrete-Time System Conditional Optimization Based on Takagi–Sugeno Fuzzy Model Using the Full Transfer Function

et al. 2022

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The study proposes a novel method for synthesis of a discrete-time parallel distributed compensation (PDC) controller for the nonlinear discrete-time Takagi–Sugeno (TS) fuzzy plant model. For each of the fuzzy plant model linear subsystems, a local linear first order proportional-sum (PS) controller is designed. The algebraic technique is used in two-dimensional parameter space, utilizing the characteristic polynomial of the row nondegenerate full transfer function matrix. Each system’s relative stability is accomplished in relation to the selected damping coefficient. The supplementary criterion is the minimal value of the performance index in the form of the sum of squared errors (SSE). However, unlike the traditional technique, output error is impacted by all simultaneous actions on the system: nonzero inputs and nonzero initial conditions. The full transfer function matrix of the system allows for the treatment of simultaneous actions of the input vector and unknown unpredictable initial conditions. In order to show the improvement caused by the application of a new optimization method that considers nonzero initial conditions, a comparison of PDC controllers designed under zero and nonzero initial conditions is given, where the system in both cases starts from the same nonzero initial conditions, which is often the case in practice. The simulation and experimental results on a DC servo motor are shown to demonstrate the suggested method efficiency.

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“…As the identification of heat transfer in a room or chamber is essential for designing an efficient HVAC system, some papers have already investigated this subject using computer vision: [1], a pure time delay model with fractional order pole [2], or the Takagi-Sugeno fuzzy model [3].…”

Section: Introductionmentioning

confidence: 99%

Heat Flow Process Identification Using ANFIS-GA Model

Vesović¹,

Jovanović²

2023

Proceedings of the International Scientific Conference - Sinteza 2023

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This paper provides a nonlinear technique that uses a fuzzy inference system and neural networks for the identification purposes of heat flow transfer in the chamber. Firstly, linear models are obtained by transfer functions with delay using Matlab identification tools for heat exchange. Three different transfer functions are provided (for three sensors in different positions along the chamber), and after it has been concluded that the second model has the smallest error, it is tested using different input. In this case, the linear model failed to represent the behaviour of the system precisely, making the error more than 1.5 C in the steady state. This was expected because linear models are trustworthy only around certain operating ranges. In order to make the new model, which will be unique and valid in the whole state space, another identification method using an adaptive neuro-fuzzy inference system (ANFIS) was presented. Furthermore, for the best performance, the ANFIS architecture was found using one of the most famous population-based optimizations: the genetic evolutionary algorithm. With two inputs and 70 parameters found by optimization (40 premises and 30 consequent) ANFIS greatly outperforms standard identification technique in terms of the mean square error. This nonlinear model was also tested on the different input, which was not used in the training process, and it was concluded that the nonlinear model identifies the real object with a neglectable error, which is 45 times smaller than the linear one.

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Identification and control of a heat flow system based on the Takagi-Sugeno fuzzy model using the grey wolf optimization algorithm

Cited by 3 publications

References 30 publications

Collaborative Control and Optimization Mechanism of Thermal Management of New Energy Vehicles

Collaborative Control and Optimization Mechanism of Thermal Management of New Energy Vehicles

Discrete-Time System Conditional Optimization Based on Takagi–Sugeno Fuzzy Model Using the Full Transfer Function

Heat Flow Process Identification Using ANFIS-GA Model

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