Designing Stable MIMO Fuzzy Controllers

A vehicle headlamp is a high-sophisticated device basically composed of three elements: housing, lighting system and lens. Nowadays, they are assembled with no dimensional control, obtaining a final product with high physical deviations. Since the car manufacturer is requiring more and more precision, the number of faulty headlamps is increasing, and thus the cost to the headlamp producer. To solve this problem, a new automatic assembly machine was built. It integrated different technologies such as sensor systems, linear actuators and computational capacities. This work presents the conception, design and development of a fuzzy control algorithm for an automatic machine capable of correcting the dimensional tolerances of vehicle headlamps during its assembly process. Particulary, this paper describes the problem of modeling and control an industrial system, with a high complex dynamics and unknown model, by a rule-based model constructed from measured data. To validate the proposed control system, different experimental tests were carried out in both the lab and the assembly line in the factory. The obtained results show how the prototype, together with the proposed fuzzy software, solve the problem of assembling vehicle headlamps obtaining a final product with minimum dimensional errors.

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“…But, the design of fuzzy controllers has always been somewhat abstract in that it is usually based on intuition and heuristic reasoning [8].…”

Section: Design Of the Fuzzy Control Strategymentioning

confidence: 99%

Fuzzy controller for the high-accuracy automatic assembly of vehicle headlamps

García

Ortega

García

et al. 2010

International Conference on Fuzzy Systems

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“…A variable structure fuzzy logic controller developed for this purpose and in another study presented a systematic procedure for constructing a multi-input multi-output fuzzy controller 3,6 . Similarly a method of rule based fuzzy logic was developed using a forward and backward inference 10 .…”

Section: Introductionmentioning

confidence: 99%

Decision Support Model for Automated Railway Level Crossing System Using Fuzzy Logic Control

Pattanaik

Yadav

2015

Procedia Computer Science

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This paper aims to discuss a decision support model for automated railway level crossing (LC) using fuzzy logic control (FLC) for providing robust decision making at unmanned railway level crossings, to save the overall operation time, to avoid any accidental fatalities, and to eliminate human errors. The decision support model proposed here provides intelligent decisive action signals as similar to a human brain (e.g. during arrival and departure of trains at railway level crossing). FLC model is designed which recognizes the events (i.e. arrival and departure of trains) and accordingly output action signals are generated (i.e. to warning siren, control actions for opening and closing of gates). This type of model can be implemented in unmanned railway level where the chances of accidents are higher and reliable control operation is required. Three primary inputs to the specified model are considered based on visual, acoustic, and vibration. This novel system makes use of all these three parameters as input for its decision taking parameters, which increases the robustness of this model as compared with previously proposed models where the input is dependent on a single event. The FLC structure implemented to generate this model is multiple input multiple output (MIMO) system.

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“…A wide class of these controllers constitutes several components namely the rule-base engine, the fuzzification process, the inference mechanism and the defuzzification process. To avoid defuzzification ambiguities which may arise from more than one crisp output value, some weighted-based techniques are commonly used such as the averaging, the center of gravity (centroid), or the root-sum-square methods [6][7][8][9]. Other FLCs are based on the conventional fuzzy control (Mamdani Type fuzzy control), fuzzy PID control, neuro-fuzzy control, fuzzy sliding-mode control, adaptive fuzzy control, supervisory fuzzy control, and the Takagi and Sugeno (T-S) modelbased fuzzy control [10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

A new approach for automatic control modeling, analysis and design in fully fuzzy environment

Gabr

2015

Ain Shams Engineering Journal

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The paper presents a new approach for the modeling, analysis and design of automatic control systems in fully fuzzy environment based on the normalized fuzzy matrices. The approach is also suitable for determining the propagation of fuzziness in automatic control and dynamical systems where all system coefficients are expressed as fuzzy parameters. A new consolidity chart is suggested based on the recently newly developed system consolidity index for testing the susceptibility of the system to withstand changes due to any system or input parameters changes effects. Implementation procedures are elaborated for the consolidity analysis of existing control systems and the design of new ones, including systems comparisons based on such implementation consolidity results. Application of the proposed methodology is demonstrated through illustrative examples, covering fuzzy impulse response of systems, fuzzy Routh-Hurwitz stability criteria, fuzzy controllability and observability. Moreover, the use of the consolidity chart for the appropriate design of control system is elaborated through handling the stabilization of inverted pendulum through pole placement technique. It is also shown that the regions comparison in consolidity chart is based on type of consolidity region shape such as elliptical or circular, slope or angle in degrees of the centerline of the geometric shape, the centroid of the geometric shape, area of the geometric shape, length of principal diagonals of the shape, and the diversity ratio of consolidity points for each region. Finally, it is recommended that the proposed consolidity chart approach be extended as a unified theory for modeling, analysis and design of continuous and digital automatic control systems operating in fully fuzzy environment. Ó 2015 Faculty of Engineering, Ain Shams University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Designing Stable MIMO Fuzzy Controllers

Cited by 16 publications

References 14 publications

Fuzzy controller for the high-accuracy automatic assembly of vehicle headlamps

Fuzzy controller for the high-accuracy automatic assembly of vehicle headlamps

Decision Support Model for Automated Railway Level Crossing System Using Fuzzy Logic Control

A new approach for automatic control modeling, analysis and design in fully fuzzy environment

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