A review on design and implementation of type‐2 fuzzy controllers

Jahanshahi, Hadi; Alotaibi, Ahmed; Soradi‐Zeid, Samaneh; Castillo, Oscar

doi:10.1002/mma.8492

Cited by 10 publications

(9 citation statements)

References 172 publications

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“…Type-2 fuzzy logic controllers are specifically constructed to handle uncertain input data, hence improving their ability to handle linguistic uncertainties. It is noteworthy that the use of FOU (higher order uncertainty) results in enhanced outcome and more seamless management of uncertainties [66][67][68][69].…”

Section: Introductionmentioning

confidence: 99%

Robust adaptive backstepping control of H‐bridge inverter based on type‐2 fuzzy optimization of parameters

Azarinfar,

Khosravi,

Soroush

et al. 2024

IET Power Electronics

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A type‐2 fuzzy logic‐based adaptive backstepping control (T2FABSC) approach is designed for an H‐bridge inverter. This inverter has an LC filter to decrease the level of total harmonic distortion (THD) that can affect the efficiency of the system. Reduction of THD and stability insurance of the filter are challenging trade‐offs, tackled here by a controller design. The performance, however, is not suitable for actual applications under a wider range of disturbances, and the parameters need to be adjusted once more for more dependable operations. Backstepping control is given a Lyapunov definition‐based adaptive mechanism that can improve this scheme's stability and robustness in the face of numerous disturbances. Additionally, the system is viewed as a “Black box” without the need for a precise mathematical model, which might lead to a lighter computing burden and simpler implementation. Moreover, a fuzzy type‐2 structure is adopted that can optimize the gains of the adaptive Backstepping controller (ABSC) in challenging conditions. The antlion optimization algorithm is employed to optimize the parameters of the membership functions in the fuzzy system, hence improving the performance of the controller. In addition, compared with different optimization algorithms, it can more quickly and accurately locate the best solutions. Finally, the findings of both the simulations and the experimental outputs are examined, proving the T2FABSC's significant robustness and faster dynamics in a variety of challenging circumstances.

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Section: Introductionmentioning

confidence: 99%

Robust adaptive backstepping control of H‐bridge inverter based on type‐2 fuzzy optimization of parameters

Azarinfar,

Khosravi,

Soroush

et al. 2024

IET Power Electronics

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“…The use of state-of-the-art automatic control methods can help to improve the effectiveness and efficiency of various processes and systems, leading to better outcomes and more sustainable solutions [58][59][60][61][62][63][64][65][66][67]. Among the stated control strategies, intelligent controllers have significant advantages [68][69][70][71][72][73]. Artificial intelligence does combine a wide variety of new technologies to give systems an ability to make decisions in new and unfamiliar conditions [74,75].…”

Section: Introductionmentioning

confidence: 99%

A New Fuzzy Reinforcement Learning Method for Effective Chemotherapy

et al. 2023

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A key challenge for drug dosing schedules is the ability to learn an optimal control policy even when there is a paucity of accurate information about the systems. Artificial intelligence has great potential for shaping a smart control policy for the dosage of drugs for any treatment. Motivated by this issue, in the present research paper a Caputo–Fabrizio fractional-order model of cancer chemotherapy treatment was elaborated and analyzed. A fix-point theorem and an iterative method were implemented to prove the existence and uniqueness of the solutions of the proposed model. Afterward, in order to control cancer through chemotherapy treatment, a fuzzy-reinforcement learning-based control method that uses the State-Action-Reward-State-Action (SARSA) algorithm was proposed. Finally, so as to assess the performance of the proposed control method, the simulations were conducted for young and elderly patients and for ten simulated patients with different parameters. Then, the results of the proposed control method were compared with Watkins’s Q-learning control method for cancer chemotherapy drug dosing. The results of the simulations demonstrate the superiority of the proposed control method in terms of mean squared error, mean variance of the error, and the mean squared of the control action—in other words, in terms of the eradication of tumor cells, keeping normal cells, and the amount of usage of the drug during chemotherapy treatment.

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“…This paper studies the current sharing and voltage balancing problems of direct current microgrids (DC-MGs) consisting of distributed generation units (DGUs) connected by a communication network. The main challenge is that the DC-MG model is prone to unknown dynamic models and external disturbances [8], and this paper introduces a comprehensive review about the most recent advances in the design and implementation of type-2 fuzzy control schemes both for integer-and fractional-order systems [9]. In this article, an intelligent system utilizing type-3 fuzzy logic for automated image quality tuning in televisions is presented.…”

Section: Introductionmentioning

confidence: 99%

Behavioral Analysis of an Interval Type-2 Fuzzy Controller Designed with Harmony Search Enhanced with Shadowed Type-2 Fuzzy Parameter Adaptation

et al. 2023

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The challenges we face in today’s world are increasingly complex, and effectively managing uncertainty when modeling control problems can yield significant benefits. However, the complexity of these models often leads to higher computational costs. Therefore, the main contribution of this article is the use of the theory of shadowed type-2 fuzzy sets to address these challenges and to control the search space exploration in the harmony search algorithm by employing two alpha planes, and with this, it was possible to reduce the computational cost and obtain effective results. Furthermore, the application of this approach aims to find optimal parameters for the membership functions of a type-2 fuzzy controller and analyze its behavior. By adopting the proposed methodology, it becomes possible to minimize computational costs while still achieving feasible solutions for interval type-2 control problems. A key aspect is that symmetry is considered in the design of the controller to also obtain good results. To validate the effectiveness of the approach, extensive simulations were conducted with varying levels of noise introduced to the type-2 controller. This comprehensive analysis allowed for a thorough examination of the results obtained. The findings of the simulations are presented, showcasing the advantages of the proposed methodology. By incorporating noise into the system, it was observed that the objective function, in this case, the root mean square error (RMSE), was reduced. Moreover, the signal obtained with the presence of noise demonstrated a superior performance compared to the noise-free reference. In conclusion, the proposed approach of utilizing shadowed type-2 fuzzy systems, combined with the harmony search algorithm, offers a promising solution for managing complex control problems. By carefully analyzing the behavior of the system through simulations, it is evident that the inclusion of noise helps improve the system’s performance.

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A review on design and implementation of type‐2 fuzzy controllers

Cited by 10 publications

References 172 publications

Robust adaptive backstepping control of H‐bridge inverter based on type‐2 fuzzy optimization of parameters

Robust adaptive backstepping control of H‐bridge inverter based on type‐2 fuzzy optimization of parameters

A New Fuzzy Reinforcement Learning Method for Effective Chemotherapy

Behavioral Analysis of an Interval Type-2 Fuzzy Controller Designed with Harmony Search Enhanced with Shadowed Type-2 Fuzzy Parameter Adaptation

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