Vibration Control Using Combined Independent Modal Space Control and Fuzzy Logic

1995 ◽  
Author(s):  
Mehran Asdigha ◽  
Robert Greif
Keyword(s):  
Physical Space ◽  
Fuzzy Rule ◽  
Fuzzy Reasoning ◽  
Performance Criterion ◽  
Active Damping ◽  
Rule Base ◽  
Control Law ◽  
Traditional Algorithm ◽  
Control Forces ◽  
Modal Space

Abstract Independent Modal Space Control (IMSC) is an established technique in active suppression of vibrations, in which the control law is developed exclusively in the modal space, allowing for independent design of the modal control forces. These forces can be transformed to the physical domain through modal transformation. The resulting controller is fixed-gain, with the active damping introduced to the system determined independently for each mode and is a function of the velocity for the under-damped case. In this work we propose to modify IMSC using fuzzy reasoning. The result is a new non-linear control law, embedding fuzzy reasoning and an implicit fuzzy rule-base that transforms the traditional algorithm from a fixed-gain to a variable-gain controller. The algorithm uses information about the displacement profile across the sensed locations to distribute the active damping rationally among the modal controllers. This new algorithm complements the “local” view of the traditional algorithm in the modal space, with a “global” view of the displacements in the physical space. The results show significant improvement in the settling time as the performance criterion.

Fuzzy Rule Interpolation

2011 ◽  
pp. 728-733 ◽  
Author(s):  
Szilveszter Kovács
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Rule ◽  
Fuzzy Reasoning ◽  
Fuzzy Relation ◽  
Rule Base ◽  
Rule Based ◽  
Fuzzy Rule Base ◽  
Current Observation ◽  
Rule Bases ◽  
Takagi Sugeno

The “fuzzy dot” (or fuzzy relation) representation of fuzzy rules in fuzzy rule based systems, in case of classical fuzzy reasoning methods (e.g. the Zadeh-Mamdani- Larsen Compositional Rule of Inference (CRI) (Zadeh, 1973) (Mamdani, 1975) (Larsen, 1980) or the Takagi - Sugeno fuzzy inference (Sugeno, 1985) (Takagi & Sugeno, 1985)), are assuming the completeness of the fuzzy rule base. If there are some rules missing i.e. the rule base is “sparse”, observations may exist which hit no rule in the rule base and therefore no conclusion can be obtained. One way of handling the “fuzzy dot” knowledge representation in case of sparse fuzzy rule bases is the application of the Fuzzy Rule Interpolation (FRI) methods, where the derivable rules are deliberately missing. Since FRI methods can provide reasonable (interpolated) conclusions even if none of the existing rules fires under the current observation. From the beginning of 1990s numerous FRI methods have been proposed. The main goal of this article is to give a brief but comprehensive introduction to the existing FRI methods.

Fuzzy Rule Base Design With Probabilistic Weights

2016 ◽  
Author(s):  
Leonardo G. Melo ◽  
Luís A. Lucas ◽  
Myriam R. Delgado
Keyword(s):  
Fuzzy Rule ◽  
Rule Base ◽  
Fuzzy Rule Base ◽  
Base Design

Fuzzy rule based cluster analysis to segment consumers’ preferences to eco and non-eco friendly products

2021 ◽  
Vol 24 (4) ◽  
pp. 343-351
Author(s):  
Sanjukta Ghosh ◽  
Doan Van Thang ◽  
Suresh Chandra Satapathy ◽  
Sachi Nandan Mohanty
Keyword(s):  
Fuzzy Rule ◽  
Environmental Knowledge ◽  
Purchase Behavior ◽  
Health Improvement ◽  
Rule Base ◽  
Environmental Attitude ◽  
Rule Based ◽  
Product Categories ◽  
Central Pollution Control Board ◽  
Develop Strategy

Environment protection and basic health improvement of all social communities is now considered as one of the key parameters for the development. It has become a responsibility for both industry and academia to optimize the usage of finite natural resources and preserve them. Efficient promotion and strategic marketing of Eco Friendly products can contribute to this development. It is important to consider any market as a heterogeneous mix, which requires well-organized and intelligent split or segmentation. A survey was conducted in Kolkata, metropolitan city in India, through a structured questionnaire to measure Perceived Environmental Knowledge, Perceived Environmental Attitude and Green Purchase Behavior associated to 18 product categories identified by Central Pollution Control Board for Eco Mark Scheme, 2002. Two hundred and twenty three data inputs from the respondents were analysed for this study. Here in this study a fuzzy rule based clustering technique was performed to segregate customers into two sections considering three parameters like Perceived Environmental Knowledge, Perceived Environmental Attitude and Green Purchase Behavior associated to Eco friendly product, which acts as an input variable. The rule base has linguistic variables like Significantly High, Little High, Medium, Little Low and Significantly Low and output as “Eco friendly” or “Non-ecofriendly” consumers. A set of 5×5×5= 125 rules were developed for output determination. They were designed manually and the method is applied for detection of a set of good rules. Thirteen such good rules were identified through Fuzzy Reasoning Tool, which can lead to better Decision Making and facilitate the marketers to develop strategy and take up effective marketing decisions.

A DIVIDE-AND-CONQUER STRATEGY FOR QUALITATIVE SIMULATION AND FUZZY IDENTIFICATION OF COMPLEX DYNAMICAL SYSTEMS

2011 ◽  
Vol 19 (03) ◽  
pp. 423-452 ◽  
Author(s):  
RAFFAELLA GUGLIELMANN ◽  
LILIANA IRONI
Keyword(s):  
Scale Up ◽  
Fuzzy Rule ◽  
Qualitative Reasoning ◽  
Divide And Conquer ◽  
Rule Base ◽  
Qualitative Simulation ◽  
Biological Domain ◽  
Large Systems ◽  
Behavioural Description

Fuzzy systems properly integrated with Qualitative Reasoning approaches yield a hybrid identification method, called FS-QM, that outperforms traditional data-driven approaches in terms of robustness, interpretability and efficiency in both rich and poor data contexts. This results from the embedment of the entire system dynamics predicted by the simulation of its qualitative model, represented by fuzzy-rules, into the fuzzy system. However, the intrinsic limitation of qualitative simulation to scale up to complex and large systems significantly reduces its efficient applicability to real-world problems. The novelty of this paper deals with a divide-and-conquer approach that aims at making qualitative simulation tractable and the derived behavioural description comprehensible and exhaustive, and consequently usable to perform system identification. The partition of the complete model into smaller ones prevents the generation of a complete temporal ordering of all unrelated events, that is one of the major causes of intractable branching in qualitative simulation. The set of generated behaviours is drastically but beneficially reduced as it still captures the entire range of possible dynamical distinctions. Thus, the properties of the correspondent fuzzy-rule base, that guarantee robustness and interpretability of the identified model, are preserved. The strategy we propose is discussed through a case study from the biological domain.

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