Expert Knowledge-Based Traction Control of a Truck Using Fuzzy Logic

1996 ◽  
Vol 29 (1) ◽  
pp. 7867-7872
Author(s):  
Ka C. Cheok ◽  
Kazuyuki Kobayashi ◽  
Francis B. Hoogterp
Keyword(s):  
Fuzzy Logic ◽  
Expert Knowledge ◽  
Traction Control ◽  
Knowledge Based

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

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1777
Author(s):  
Lisa Gerlach ◽  
Thilo Bocklisch
Keyword(s):  
Fuzzy Logic ◽  
Energy Management ◽  
Fuzzy Logic Controller ◽  
Expert Knowledge ◽  
Large Power ◽  
Hybrid Energy ◽  
Knowledge Based ◽  
Long Term Storage ◽  
Fuzzy Logic Approach

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.

An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic

Geomorphology ◽  
2014 ◽  
Vol 214 ◽  
pp. 128-138 ◽  
Author(s):  
A-Xing Zhu ◽  
Rongxun Wang ◽  
Jianping Qiao ◽  
Cheng-Zhi Qin ◽  
Yongbo Chen ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Landslide Susceptibility ◽  
Expert Knowledge ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Knowledge Based

scholarly journals Digital soil mapping approach based on fuzzy logic and field expert knowledge

2013 ◽  
Vol 37 (4) ◽  
pp. 287-298 ◽  
Author(s):  
Michele Duarte de Menezes ◽  
Sérgio Henrique Godinho Silva ◽  
Phillip Ray Owens ◽  
Nilton Curi
Keyword(s):  
Fuzzy Logic ◽  
Spatial Information ◽  
Expert Knowledge ◽  
Soil Mapping ◽  
Soil Survey ◽  
Digital Soil Mapping ◽  
Small Areas ◽  
Knowledge Based ◽  
Mapping Techniques ◽  
Soil Surveys

In Brazil, soil surveys in more detailed scale are still scarce and necessary to more adequately support the decision makers for planning soil and environment activities in small areas. Hence, this review addresses some digital soil mapping techniques that enable faster production of soil surveys, beyond fitting continuous spatial distribution of soil properties into discrete soil categories, in accordance with the inherent complexity of soil variation, increasing the accuracy of spatial information. The technique focused here is knowledge-based in expert systems, under fuzzy logic and vector of similarity. For that, a contextualization of each tool in the soil types and properties prediction is provided, as well as some options of knowledge extraction techniques. Such tools have reduced the inconsistency and costs associated with the traditional manual processes, relying on a relatively low density of soil samples. On the other hand, knowledge-based technique is not automatic, and just as the traditional soil survey, the knowledge of soil-landscape relationships is irreplaceable.

Implementation of Fuzzy Logic Control on Battery Charging System

2019 ◽  
Vol 3 (1) ◽  
pp. 118-126 ◽  
Author(s):  
Prihangkasa Yudhiyantoro
Keyword(s):  
Complex System ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Expert Knowledge ◽  
Experimental Result ◽  
Rule Base ◽  
Battery Charging ◽  
Charging System ◽  
Logic Control ◽  
Charging Control

This paper presents the implementation fuzzy logic control on the battery charging system. To control the charging process is a complex system due to the exponential relationship between the charging voltage, charging current and the charging time. The effective of charging process controller is needed to maintain the charging process. Because if the charging process cannot under control, it can reduce the cycle life of the battery and it can damage the battery as well. In order to get charging control effectively, the Fuzzy Logic Control (FLC) for a Valve Regulated Lead-Acid Battery (VRLA) Charger is being embedded in the charging system unit. One of the advantages of using FLC beside the PID controller is the fact that, we don’t need a mathematical model and several parameters of coefficient charge and discharge to software implementation in this complex system. The research is started by the hardware development where the charging method and the combination of the battery charging system itself to prepare, then the study of the fuzzy logic controller in the relation of the charging control, and the determination of the parameter for the charging unit will be carefully investigated. Through the experimental result and from the expert knowledge, that is very helpful for tuning of the  embership function and the rule base of the fuzzy controller.

Survey of the State-of-the-Art Expert/Knowledge Based Systems in Civil Engineering.

1986 ◽  
Author(s):  
Simon S. Kim ◽  
Mary Lou Maher ◽  
Raymond E. Levitt ◽  
Martin F. Rooney ◽  
Thomas J. Siller
Keyword(s):  
Civil Engineering ◽  
Expert Knowledge ◽  
State Of The Art ◽  
The State ◽  
Knowledge Based Systems ◽  
Knowledge Based
Sign in / Sign up

Export Citation Format

Share Document