Compact Fuzzy Systems Based on Boolean Relations

Espitia, Helbert Eduardo; Soriano, Jairo; Machón-González, Iván; Garcı́a, Hilario López

doi:10.3390/app11041793

Cited by 6 publications

(14 citation statements)

References 42 publications

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“…The neuro-fuzzy controller uses the structure of a compact fuzzy system based on Boolean relations such as that presented in [21], which can be used for the identification and control of dynamic systems. Fuzzy systems based on Boolean relations are based on a system design considering possible Boolean regions in the discourse universes; then this system is extended using fuzzy sets for implementation, making it possible to consider the operation of sensors and actuators present in the control system [22].…”

Section: Fuzzy Control Systemmentioning

confidence: 99%

“…In this work, the designed neuro-fuzzy controller uses the structure of a compact fuzzy system based on Boolean relations as presented in [21], which can be used for the identification and control of dynamic systems. Neuro-fuzzy systems based on Boolean relations are founded in a system design considering possible Boolean regions in the universe discourse, then being extended using fuzzy sets for implementation [22].…”

Section: Introductionmentioning

confidence: 99%

“…Various design considerations are presented in this scenario as well as the manipulation of system equations applying Boole and Kleene algebras. In this way, fuzzy systems with compact structures can be established and used for identification and control of dynamic systems [21]. As an example, the application of this type of system is shown in [23] to control an Automatic Voltage Regulator (AVR).…”

Section: Introductionmentioning

confidence: 99%

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Design and Optimization of a Neuro-Fuzzy System for the Control of an Electromechanical Plant

2022

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One characteristic of neuro-fuzzy systems is the possibility of incorporating preliminary information in their structure as well as being able to establish an initial configuration to carry out the training. In this regard, the strategy to establish the configuration of the fuzzy system is a relevant aspect. This document displays the design and implementation of a neuro-fuzzy controller based on Boolean relations to regulate the angular position in an electromechanical plant, composed by a motor coupled to inertia with friction (a widely studied plant that serves to show the control system design process). The structure of fuzzy systems based on Boolean relations considers the operation of sensors and actuators present in the control system. In this way, the initial configuration of fuzzy controller can be determined. In order to perform the optimization of the neuro-fuzzy controller, the continuous plant model is converted to discrete time to be included in the closed-loop controller training equations. For the design process, first the optimization of a Proportional Integral (PI) linear controller is carried out. Thus, linear controller parameters are employed to establish the structure and initial configuration of the neuro-fuzzy controller. The optimization process also includes weighting factors for error and control action in such a way that allows having different system responses. Considering the structure of the control system, the optimization algorithm (training algorithm) employed is dynamic back propagation. The results via simulations show that optimization is achieved in the linear and neuro-fuzzy controllers using different weighting values for the error signal and control action. It is also observed that the proposed control strategy allows disturbance rejection.

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Section: Fuzzy Control Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Design and Optimization of a Neuro-Fuzzy System for the Control of an Electromechanical Plant

2022

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“…The fuzzy controller uses the compact structure presented in [24] that allows having an equivalence with a linear controller (in discrete time) using fuzzy sets to model the actions of direct inputs and the controller feedback. The general scheme of the control system can be seen in Figure 4.…”

Section: Fuzzy Controllermentioning

confidence: 99%

“…For the design of compact fuzzy controllers, linear control strategies are considered as a reference: Considering the presented in [24], the compact fuzzy controllers are implemented considering Equation ( 24) associated with Figure 6, having the following expression:…”

Section: Implementation Of Compact Fuzzy Controllersmentioning

confidence: 99%

Optimization of a Fuzzy Automatic Voltage Controller Using Real-Time Recurrent Learning

2021

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The automatic voltage regulator is an important component in energy generation systems; therefore, the tuning of this system is a fundamental aspect for the suitable energy conversion. This article shows the optimization of a fuzzy automatic voltage controller for a generation system using real-time recurrent learning, which is a technique conventionally used for the training of recurrent neural networks. The controller used consists of a compact fuzzy system based on Boolean relations, designed having equivalences with PI, PD, PID, and second order controllers. For algorithm implementation, the training equations are deduced considering the structure of the second order compact fuzzy controller. The results show that a closed-loop fuzzy control strategy was successfully implemented using real-time recurrent learning. In order to implement the controllers optimization, different weighting values for error and control action are used. The results show the behavior of the configurations used and its performance considering the steady state error, overshoot, and settling time.

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Spatial Dynamics Model of Earthquake Prone Area in Ambon City

Rakuasa

Supriatna

Karsidi

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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Ambon City has limitations in the development of residential areas because it is in an area prone to earthquake disasters, This is because Ambon City is in a very active tectonic area and 65% of settlement land / land built in Ambon City is in an active and weak fault zone and if there is a plate movement there will be an earthquake along the weak zone. One of the first steps to the effort to stigmatize disasters is to estimate how large the area of settlements that have been analyzed using cellular automata markov-chain located in earthquake-prone areas. This study aims to analyze changes in Ambon city land cover in 2012, 2017, 2021 and predict land cover in 2031 and synthesize spatial dynamics of settlement land availability with earthquake-prone areas in Ambon City in 2021 and 2031. This study uses Cellular Automata modeling to predict ambon city land cover in 2031 which will be covered with a map of earthquake-prone areas obtained from BPBD Ambon City to see residential areas that are in earthquake prone areas in Ambon City in 2021 and 2031. The results showed that the area of built land affected in earthquake-prone areas has a straight proportional relationship with the year of land development built. So that the more years then the area of land is built in areas prone to earthquakes and increasing. This research is expected to be used as a reference in the management of sustainable settlement area development and for efforts to organize space based on disaster mitigation in order to minimize losses and casualties due to earthquake disasters that will occur in Ambon City in this future.

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Compact Fuzzy Systems Based on Boolean Relations

Cited by 6 publications

References 42 publications

Design and Optimization of a Neuro-Fuzzy System for the Control of an Electromechanical Plant

Design and Optimization of a Neuro-Fuzzy System for the Control of an Electromechanical Plant

Optimization of a Fuzzy Automatic Voltage Controller Using Real-Time Recurrent Learning

Spatial Dynamics Model of Earthquake Prone Area in Ambon City

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