Stability of Fuzzy Control Systems

Driankov, Dimiter; Hellendoorn, Hans; Reinfrank, Michael

doi:10.1007/978-3-662-03284-8_6

Cited by 2 publications

(4 citation statements)

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“…The linguistic value heat is subsequently translated into the crisp value within the defuzzification, to process the signal further, out of the FL. This kind of the nonlinear control process is suitable for the real world and is particularly recommended for the plants that are actually nonlinear [52]. Thus, it is widely applied to improve the performance of the energy converting devices [53][54][55].…”

Section: Model Of the Fuzzy-logic Controllermentioning

confidence: 99%

“…According to the functions defined, the FVE crisp value is quantified to the NF, NM, ZO, PM, PF fuzzy sets. The Λ-type membership functions were applied due to common use and proven efficiency [42][43][44]52]. The Γ-type and L-type of the membership functions were applied due to the thresholds of the sensors and actuators [40,42,52].…”

Section: Model Of the Fuzzy-logic Controllermentioning

confidence: 99%

“…The aggregation of the active rules is realized with the algebraic sum method. The inference and defuzzyfication are realized with the output membership functions (Figure 8) and the center of gravity method (CoG), respectively [52]. Finally, the resultant output control surface of the FL is presented in Figure 9.…”

Section: Model Of the Fuzzy-logic Controllermentioning

confidence: 99%

“…It was done following the procedure of state variable trajectory tracking [58]. This procedure is applicable and recommended for nonlinear systems, such as the fuzzy-logic ones [52,58]. Accordingly, a system is stable, if the state variables tend to the origin from any initial state on the phase plan.…”

Section: Model Of the Fuzzy-logic Controllermentioning

confidence: 99%

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Fuzzy Control of Waves Generation in a Towing Tank

Drzewiecki

Guziński

2020

Energies

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This paper presents the results of research related to the transformation of electrical energy into potential and kinetic energy of waves generated on the water surface. The waves are generated to model the environmental conditions for the needs of the model tests. The model tests are performed on model-scale objects to predict the features of full-scale maritime objects. It is done to improve human safety and the survivability of constructions. Electrical energy is transformed into the energy of the water waves using a wave maker. The wave maker considered is a facility with an electrohydraulic drive and an actuator submerged into the water. The actuator movement results in the waves being mechanically-generated in accordance with the wave maker theory. The study aimed to investigate the advantage of the newly implemented fuzzy-logic controller over the hitherto cascading proportional-integral controllers of the wave maker actuator. The research was focused on experimental investigation of the transformation process outcomes harvested under the fuzzy-logic controller, versus the cascading proportional-integral controllers. The waves were generated and measured in the real towing tank, located in the Maritime Advanced Research Centre (CTO S.A.). The investigation confirmed the advantage of the fuzzy-logic controller. It provides more accurate transformation of energy into the desired form of the water waves of specified parameters—frequency and amplitude—and more flat amplitude-frequency characteristic of the transformation process.

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Section: Model Of the Fuzzy-logic Controllermentioning

confidence: 99%

Section: Model Of the Fuzzy-logic Controllermentioning

confidence: 99%

Section: Model Of the Fuzzy-logic Controllermentioning

confidence: 99%

Section: Model Of the Fuzzy-logic Controllermentioning

confidence: 99%

See 2 more Smart Citations

Fuzzy Control of Waves Generation in a Towing Tank

Drzewiecki

Guziński

2020

Energies

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Fuzzy-controlled individual-cell equaliser using discontinuous inductor current-mode Cuk convertor for lithium–ion chemistries

Lee

Jiun-Yi

2005

IEE Proc., Electr. Power Appl.

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An intelligent battery equalisation-scheme based on fuzzy-logic control (FLC) is presented to adaptively control the equalising process for series-connected lithium-ion batteries. The proposed individual-cell equalisation scheme (ICE) is a bidirectional DC-DC convertor based on the C# uk convertor operated in the discontinuous inductor current mode (DICM). The proposed method can be used to achieve a zero-current-switching (ZCS) cell-balancing control scheme to reduce the switching losses in MOSFET switches. Further, a fuzzy-logic-controlled strategy is constructed with a set of membership functions to prescribe the cell equalising behaviour within a safe equalising region for reduced cell-equalisation time. The simulation and experimental results show the advantage of the predicted equalising performance for lithium-ion battery stacks. The proposed fuzzy-logic-control battery-equalisation controller (FLC-BEC) abridges the equalisation time by about 30% compared with the equaliser without FLC. The power losses of the MOSFET switch are significantly reduced by about 33.8%, and the equalisation efficiency is increased by about 8.5% compared with the same equalisation scheme in continuous-inductor-current mode (CICM). The proposed equalisation method maintains safe operations during the charge/discharge state to increase the charge capacity of lithium-ion battery-pack cells.

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Stability of Fuzzy Control Systems

Cited by 2 publications

References 0 publications

Fuzzy Control of Waves Generation in a Towing Tank

Fuzzy Control of Waves Generation in a Towing Tank

Fuzzy-controlled individual-cell equaliser using discontinuous inductor current-mode Cuk convertor for lithium–ion chemistries

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