Robust tracking control design for fractional-order interval type-2 fuzzy systems

Kavikumar, Ramasamy; Sakthivel, R.; Kwon, Oh‐Min; Selvaraj, Palanisamy

doi:10.1007/s11071-021-07163-y

Cited by 14 publications

(7 citation statements)

References 45 publications

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“…Different recommendation systems have different recommendation algorithms because different recommendation systems have different requirements. Recommendation algorithm is the decisive factor for the quality of recommendation system [10]. For users, the recommendation system can quickly find the items they want.…”

Section: Methodsmentioning

confidence: 99%

“…(6) Reestimate the value of η i and repeat (4) and (5) (7) Decision threshold: according to the stop threshold, if kV ðl+1Þ − V l k≤ε, the iteration will be stopped (8) According to the clustering matrix after the convergence of the objective function, the probability of web pages belonging to the network community is determined (9) According to the initial set of web pages collected in (1), use the feature attributes contained in the web pages to classify the web pages a (10) Classify a web page and calculate the k web pages that are most similar to the query keywords, and then calculate the similarity of the web pages. That is, we found the page set of a × k pages that are most similar to the query keywords (11) Use the web page set calculated in (10) to calculate the web page set C with high evaluation (12) Remove the web pages browsed by the user in the web page set C; that is, assign C − U to C, and then select N web pages with the highest evaluation from C…”

Section: Journal Of Sensorsmentioning

confidence: 99%

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Application of Sensor and Fuzzy Clustering Algorithm in Hybrid Recommender System

Zhu

2022

Journal of Sensors

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In order to solve the problem of topic drift and topic enlargement in hybrid recommendation system, a possibility C clustering algorithm based on fuzzy clustering, namely, IPCM (improved possible clustering method) algorithm, is proposed. This method improves the initial value sensitivity of PCM algorithm and introduces the user interest model into the initial matrix, so that the results obtained by the convergence of IPCM algorithm are closer to the recommended topics required by users. The recommended technology algorithm is also fused by learning from each other to form a fusion recommendation algorithm. The fusion recommendation algorithm and IPCM algorithm are applied to the result sorting, and the accuracy of the applied results is compared with that of the traditional PageRank algorithm, so as to judge the accuracy of the algorithm. The feasibility and superiority of the algorithm are verified by experiments. The experimental results show that IPCM algorithm can speed up the search for useful information and reduce the search time. Moreover, when the query range is reduced, the accuracy of the algorithm is higher than that of the traditional algorithm, which can be improved by 10% ~30%. Conclusion. This method can effectively make up for the problems of topic drift and topic enlargement in the recommendation system, with faster speed and higher accuracy.

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Section: Methodsmentioning

confidence: 99%

Section: Journal Of Sensorsmentioning

confidence: 99%

Application of Sensor and Fuzzy Clustering Algorithm in Hybrid Recommender System

Zhu

2022

Journal of Sensors

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“…Here, m n is the only parameter that creates the footprint of the uncertainty of the IT2-FPID [40]. We employ the following parameters: m 2 = α and m 1 = m 3 = 1 − α as suggested in [36,[40][41][42]. Therefore, the selection of the parameter (α) is vital because it directly affects U.…”

Section: Interval Type-2 Fuzzy Pid Controllermentioning

confidence: 99%

Real-Time Interval Type-2 Fuzzy Control of an Unmanned Aerial Vehicle with Flexible Cable-Connected Payload

Candan,

Dik,

Kumbasar

et al. 2023

Algorithms

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This study presents the design and real-time applications of an Interval Type-2 Fuzzy PID (IT2-FPID) control system on an unmanned aerial vehicle (UAV) with a flexible cable-connected payload in comparison to the PID and Type-1 Fuzzy PID (T1-FPID) counterparts. The IT2-FPID control has significant stability, disturbance rejection, and response time advantages. To prove and show these advantages, the DJI Tello, a commercial UAV, is used with a flexible cable-connected payload to test the robustness of PID, T1-FPID, and IT2-FPID controllers. First, the optimal coefficients of the compared controllers are found using the Big Bang–Big Crunch algorithm via the nonlinear UAV model without the payload. Second, once optimised, the controllers are tested using several scenarios, including disturbing the payload and the coverage path planning area to examine their robustness. Third, the controller performance results are evaluated according to reference achievement and point-based tracking under disturbances. Finally, the superiority of the IT2-FPID controller is shown via simulations and real-time experiments with a better overshoot, a faster settling time, and good properties of disturbance rejection compared with the PID and the T1-FPID controllers.

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“…Additionally, type-2 fuzzy modelling and control methods have been shown to have better performance in the literature. For instance, the type-2 fuzzy modelling has provided improved modelling for non-linear systems in [10,11]. Interval type-2 fuzzy modelling with fractional-order (FO)-based LFC methods has been shown in [12,13].…”

Section: Introductionmentioning

confidence: 99%

Design Optimization of Improved Fractional-Order Cascaded Frequency Controllers for Electric Vehicles and Electrical Power Grids Utilizing Renewable Energy Sources

et al. 2023

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Recent developments in electrical power grids have witnessed high utilization levels of renewable energy sources (RESs) and increased trends that benefit the batteries of electric vehicles (EVs). However, modern electrical power grids cause increased concerns due to their continuously reduced inertia resulting from RES characteristics. Therefore, this paper proposes an improved fractional-order frequency controller with a design optimization methodology. The proposed controller is represented by two cascaded control loops using the one-plus-proportional derivative (1 + PD) in the outer loop and a fractional-order proportional integral derivative (FOPID) in the inner loop, which form the proposed improved 1 + PD/FOPID. The main superior performance characteristics of the proposed 1 + PD/FOPID fractional-order frequency controller over existing methods include a faster response time with minimized overshoot/undershoot peaks, an ability for mitigating both high- and low-frequency disturbances, and coordination of EV participation in regulating electrical power grid frequency. Moreover, simultaneous determination of the proposed fractional-order frequency controller parameters is proposed using the recent manta ray foraging optimization (MRFO) algorithm. Performance comparisons of the proposed 1 + PD/FOPID fractional-order frequency controller with existing PID, FOPID, and PD/FOPID controllers are presented in the paper. The results show an improved response, and the disturbance mitigation is also obtained using the proposed MRFO-based 1 + PD/FOPID control and design optimization methodology.

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Robust tracking control design for fractional-order interval type-2 fuzzy systems

Cited by 14 publications

References 45 publications

Application of Sensor and Fuzzy Clustering Algorithm in Hybrid Recommender System

Application of Sensor and Fuzzy Clustering Algorithm in Hybrid Recommender System

Real-Time Interval Type-2 Fuzzy Control of an Unmanned Aerial Vehicle with Flexible Cable-Connected Payload

Design Optimization of Improved Fractional-Order Cascaded Frequency Controllers for Electric Vehicles and Electrical Power Grids Utilizing Renewable Energy Sources

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