A Takagi-Sugeno Fuzzy Model for Greenhouse Climate

Hamad, Imen Haj; Chouchaine, Amine; Bouzaouache, Hajer

doi:10.48084/etasr.4291

Cited by 6 publications

(2 citation statements)

References 20 publications

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“…The T-S model is one of the most well-liked modeling frameworks among the different fuzzy modeling topics [16][17]. Since it can approximate any smooth nonlinear control system, the T-S fuzzy model is considered a universal approximator.…”

Section: Modified T-s-fuzzy Controllermentioning

confidence: 99%

Pantograph Catenary Contact Force Regulation Based on Modified Takagi-Sugeno Fuzzy Models

Hai

Tiem²,

Lan³

et al. 2023

Eng. Technol. Appl. Sci. Res.

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In this paper, a new contact force control technique, based on the modified Takagi-Sugeno model and the parallel distributed compensation concept is developed to suppress vibrations between the pantograph and the catenary by regulating the contact force to a reference value, thereby achieving stable current collection. The proposed method uses simple and standard PID and modified Takagi-Sugeno fuzzy controllers. The two controllers guarantee the designed system's robust stability. Furthermore, based on a simplified pantograph–catenary system model, the comparative simulation results show that variations of the contact force can be almost attenuated. As a result, the system stability is guaranteed, and the performance robustness is verified.

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Section: Modified T-s-fuzzy Controllermentioning

confidence: 99%

Pantograph Catenary Contact Force Regulation Based on Modified Takagi-Sugeno Fuzzy Models

Hai

Tiem²,

Lan³

et al. 2023

Eng. Technol. Appl. Sci. Res.

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“…Acknowledging the merits of FLC, the untapped potential lies in adapting this method to consider real-world conditions. This paper introduces an approach using the T-S fuzzy control [13][14][15] to the control design of the IPOC. The objective is to combine the strengths of T-S fuzzy control with an awareness of external constraints, such as track limits and stabilization time, in order to create a control system that not only guarantees stability, but also respects the boundaries of practical application.…”

Section: Introductionmentioning

confidence: 99%

A T-S Fuzzy Approach with Extended LMI Conditions for Inverted Pendulum on a Cart

Nguyen,

Tran

2024

Eng. Technol. Appl. Sci. Res.

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The Inverted Pendulum On a Cart (IPOC) system poses a challenge in control engineering due to its inherent instability, nonlinearity, and underactuation. This addresses the fundamental issues arising from its underactuated nature and introduces an approach that combines Takagi-Sugeno (T-S) fuzzy control with an awareness of real-world constraints to create a control system ensuring both stability and practicality. By aligning theoretical insights with extended considerations, the Linear Matrix Inequality (LMI)-based control design is demonstrated in a comprehensive framework. Theorems are introduced and validated, leading to the derivation of LMI conditions. The simulation results are assessed with accompanying comments to demonstrate the effectiveness of the theorems. Through this integration of T-S fuzzy control with additional considerations, the paper aims to bridge the gap between theory and practical applications, advancing the field of control engineering.

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Yüzey Pürüzlülüğünün Tahmininde Farklı Yöntemlerin İncelenmesi

DEMİRDÖĞEN¹,

Kılıç

Öztürk

2022

Çukurova Üniversitesi Mühendislik Fakültesi Dergisi

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Machining is one of the most widely used manufacturing processes in the mold industry and which affects the manufacturing cost significantly. Particularly, the desired surface roughness/quality at a low cost at minimum machining time is the ultimate target. Surface quality depends on many parameters such as cutting speed, feed, depth of cut, vibration, coolant, insert properties/geometry used. In this study, surface roughnesses after turning of hot work tool steel at different parameters are investigated. At the same time, regression, artificial neural network, and fuzzy logic prediction models are developed from the experimental data. Therefore, surface roughness values at the different parameters are determined. The closest estimate with approximately 5% error is obtained by the Sugeno fuzzy logic model when it compared to experimental results.

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A Takagi-Sugeno Fuzzy Model for Greenhouse Climate

Cited by 6 publications

References 20 publications

Pantograph Catenary Contact Force Regulation Based on Modified Takagi-Sugeno Fuzzy Models

Pantograph Catenary Contact Force Regulation Based on Modified Takagi-Sugeno Fuzzy Models

A T-S Fuzzy Approach with Extended LMI Conditions for Inverted Pendulum on a Cart

Yüzey Pürüzlülüğünün Tahmininde Farklı Yöntemlerin İncelenmesi

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