Fuzzy-Regelung

Michels, Kai; Kruse, Rudolf; Klawonn, Frank; Nürnberger, Andreas

doi:10.1007/978-3-642-55812-2

Cited by 4 publications

(8 citation statements)

References 64 publications

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“…R , then y is µ R The conclusion of the rule, i.e., its degree of fulfilment, depends on the degree of membership of the antecedents, which are combined using the min-operator [41]. Each rule, which is fired, results in a fuzzy set.…”

Section: Fuzzy Logic Controlmentioning

confidence: 99%

“…In a last step, the crisp outputs are derived from the output fuzzy sets by means of defuzzification. The most common method is based on calculating the centre of area (k COA ) below the output fuzzy sets and projecting it on the ordinate as shown in Equation 8 [41]. As this method is well suited for overlapping membership functions [42], it was chosen as a good candidate for the fuzzy logic energy management.…”

Section: Fuzzy Logic Controlmentioning

confidence: 99%

“…This results in adjustments to the overall logic. Secondly, the values of the membership functions are optimized, which corresponds to a fine-tuning of the controller [41]. All adjustments to the standard particle swarm optimization were done with the following objectives in mind:…”

Section: Adjustmentsmentioning

confidence: 99%

See 2 more Smart Citations

Experts versus Algorithms? Optimized Fuzzy Logic Energy Management of Autonomous PV Hybrid Systems with Battery and H2 Storage

Gerlach

Bocklisch

2021

Energies

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Off-grid applications based on intermittent solar power benefit greatly from hybrid energy storage systems consisting of a battery short-term and a hydrogen long-term storage path. An intelligent energy management is required to balance short-, intermediate- and long-term fluctuations in electricity demand and supply, while maximizing system efficiency and minimizing component stress. An energy management was developed that combines the benefits of an expert-knowledge based fuzzy logic approach with a metaheuristic particle swarm optimization. Unlike in most existing work, interpretability of the optimized fuzzy logic controller is maintained, allowing the expert to evaluate and adjust it if deemed necessary. The energy management was tested with 65 1-year household load datasets. It was shown that the expert tuned controller is more robust to changes in load pattern then the optimized controller. However, simple readjustments restore robustness, while largely retaining the benefits achieved through optimization. Nevertheless, it was demonstrated that there is no one-size-fits-all tuning. Especially, large power peaks on the demand-side require overly conservative tunings. This is not desirable in situations where such peaks can be avoided through other means.

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Section: Fuzzy Logic Controlmentioning

confidence: 99%

Section: Fuzzy Logic Controlmentioning

confidence: 99%

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Experts versus Algorithms? Optimized Fuzzy Logic Energy Management of Autonomous PV Hybrid Systems with Battery and H2 Storage

Gerlach

Bocklisch

2021

Energies

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“…Knowledge-based models are created from linguistic expert knowledge: knowledge expressed in the form of linguistic rules. This collection of rules is coded into binary or fuzzy rule bases triggering decision trees [6,11] or complete fuzzy systems [25,27]. From this point of view, they are built once and remain static for the whole application process in a similar way as for analytical models.…”

Section: Knowledge-based Modelsmentioning

confidence: 99%

An approach to model-based fault detection in industrial measurement systems with application to engine test benches

Angelov

Giglio

Guardiola

et al. 2006

Meas. Sci. Technol.

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An approach to fault detection (FD) in industrial measurement systems is proposed in this paper which includes an identification strategy for early detection of the appearance of a fault. This approach is model based, i.e. nominal models are used which represent the fault-free state of the on-line measured process. This approach is also suitable for off-line FD. The framework that combines FD with isolation and correction (FDIC) is outlined in this paper. The proposed approach is characterized by automatic threshold determination, ability to analyse local properties of the models, and aggregation of different fault detection statements. The nominal models are built using data-driven and hybrid approaches, combining first principle models with on-line data-driven techniques. At the same time the models are transparent and interpretable. This novel approach is then verified on a number of real and simulated data sets of car engine test benches (both gasoline-Alfa Romeo JTS, and diesel-Caterpillar). It is demonstrated that the approach can work effectively in real industrial measurement systems with data of large dimensions in both on-line and off-line modes.

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“…Digital electronic engine control (DEEC), in this embodiment, functions carried out after input data from the airframe and engine were processed by the DEEC computer included setting the variable vanes, positioning compressor start bleeds, controlling gas-generator, adjusting the augmenter segment sequence valve, and controlling the exhaust nozzle position. 4.…”

mentioning

confidence: 99%

Fuzzy Controller Structures Investigation for Future Gas Turbine Aero-Engines

Fard

Jafari

2021

IJTPP

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The Advisory Council for Aeronautics Research in Europe (ACARE) Flight Path 2050 focuses on ambitious and severe targets for the next generation of air travel systems (e.g., 75% reduction in CO2 emissions per passenger kilometer, a 90% reduction in NOx emissions, and 65% reduction in noise emission of flying aircraft relative to the capabilities of typical new aircraft in 2000). In order to meet these requirements, aircraft engines should work very close to their operating limits. Therefore, the importance of advanced control strategies to satisfy all engine control modes simultaneously while protecting them from malfunctions and physical damages is being more crucial these days. In the last three decades, fuzzy controllers (FCs) have been proposed as a high potential solution for performance improvement of the next generation of aircraft engines. Based on an analytic review, this paper divides the trend of FCs design into two main lines including pure FCs (PFC) and min–max FCs (MMFC). These two main architectures are then designed, implemented on hardware, and applied in a case study to analyze the advantages and disadvantages of each structure. The analysis of hardware-in-the-loop (HIL) simulation results shows that the pure FC structure would be a high potential candidate for maneuverability and response time indices improvement (e.g., military applications); while min–max FC architecture has a great potential for future civil aero-engines where the fuel consumption and steady-state responses are more important. The simulation results are also compared with those of industrial min–max controllers to confirm the feasibility and reliability of the fuzzy controllers for real-world application. The results of this paper propose a general roadmap for fuzzy controllers’ structure selection for new and next generation of aircraft engines.

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Fuzzy-Regelung

Cited by 4 publications

References 64 publications

Experts versus Algorithms? Optimized Fuzzy Logic Energy Management of Autonomous PV Hybrid Systems with Battery and H2 Storage

Experts versus Algorithms? Optimized Fuzzy Logic Energy Management of Autonomous PV Hybrid Systems with Battery and H2 Storage

An approach to model-based fault detection in industrial measurement systems with application to engine test benches

Fuzzy Controller Structures Investigation for Future Gas Turbine Aero-Engines

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