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- 저자 : Caro Lucas (검색결과 4 건)
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Fuzzy Temperature & Humidity System Optimization By Simulated Annealing
Bahareh Pourbabaee,Mahdi Shadalooee,Caro Lucas 제어로봇시스템학회 2008 제어로봇시스템학회 국제학술대회 논문집 Vol.2008 No.10
One of the considerable topic during human life has been climate parameters control such as temperature & humidity and preparing desirable condition in order to keep creatures growing. In this paper, temperature & humidity of an animal keeping center is modeled and returned to its desirable condition by the use of fuzzy logic theory and Simulated Annealing (SA) algorithm .Whenever the climate, as a fuzzy system output variable, become unsatisfactory, Simulated Annealing algorithm finds the best way of returning system temperature and humidity to their set points with a minimum time and energy among the different ways of system temperature and humidity reduction. Our work shows the efficiency, convergence behavior of SA algorithm in a short time during its searching process, apparently. The temperature and humidity data used in this paper get from one of the animal keeping center in Tehran, Iran.
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Hooman Homayouni,Fariba Bahrami,Caro Lucas 제어로봇시스템학회 2008 제어로봇시스템학회 국제학술대회 논문집 Vol.2008 No.10
Inspired by human locomotion system, we have proposed a gait controller for planar walking of a biped robot. On the basis of biomechanical and neurophysiologic studies of human gait, a two-layer controller is proposed. Lower layer of the controller consists of models of three neural mechanisms identified in the spinal cord: cutaneous reflex, stretch reflex, and central pattern generator (CPG). Upper layer controller maintains stability of the robot by employing fuzzy rule-based approach in applying two main strategies derived from biomechanical studies of human locomotion, that is ankle torque control and foot placement adjustment. Proposed controller does not require precise model of the mechanical system of the robot. Simulation results prove the capability of the proposed controller to generate a stable planar biped gait for the model robot. Stable limit-cycle for kinematic variables emerges from interaction of mechanical system with low-level mechanisms and high-level fuzzy controller. In facing disturbances, in form of a push from both front and behind, robot shows great ability to maintain its balance. This balance control is provided by the upper layer rule-based controller.
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A Particle-swarm-based Approach for Optimum Design of BELBIC Controller in AVR System
Sima Valizadeh,Mohammad-Reza Jamali,Caro Lucas 제어로봇시스템학회 2008 제어로봇시스템학회 국제학술대회 논문집 Vol.2008 No.10
In this paper, Particle Swarm Optimization algorithm is applied to design an optimum intelligent controller based on brain emotional learning. BELBIC controller is tuned to improve the time domain parameters such as percent overshoot, steady state error, settling time and rise time of the step response of an Automatic Voltage Regulator. Also the convergence characteristic of fitness function averaged over the whole particles in each generation is investigated. PSO-BELBIC performance is compared with the classic PSO-PID controller.
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Rasoul Mohammadi Milasi,Mohammad Reza Jamali,Caro Lucas 대한전기학회 2007 International Journal of Control, Automation, and Vol.5 No.4
In this paper, an intelligent method called BELBIC (Brain Emotional Learning Based Intelligent Controller) is used to control of Locally Linear Neuro-Fuzzy Model (LOLIMOT) of Washing Machine. The Locally Linear Neuro-Fuzzy Model of Washing Machine is obtained based on previously extracted data. One of the important issues in using BELBIC is its parameters setting. On the other hand, the controller design for Washing Machine is a multi objective problem. Indeed, the two objectives, energy consumption and effectiveness of washing process, are main issues in this problem, and these two objectives are in contrast. Due to these challenges, a Multi Objective Genetic Algorithm is used for tuning the BELBIC parameters. The algorithm provides a set of non-dominated set points rather than a single point, so the designer has the advantage of selecting the desired set point. With considering the proper parameters after using additional assumptions, the simulation results show that this controller with optimal parameters has very good performance and considerable saving in energy consumption.
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