Designing PID and BELBIC controllers in path tracking and collision problem in automated highway systems

Jafarzadeh, Saeed; Mirheidari, R.; Motlagh, Mohammad Reza Jahed; Barkhordari, Mojtaba

doi:10.1109/icarcv.2008.4795757

Cited by 21 publications

(15 citation statements)

References 9 publications

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“…(19) and (20), reward signal and sensory input can be an arbitrary function of reference input, r, controller output, u, error (e) signal, and the plant output y p . It is for the designer to find a proper function for control.…”

Section: Brain Emotional Learning Based Intelligent Controller (Belbimentioning

confidence: 99%

“…Recently, researchers have developed a computational model of emotional learning in mammalian brain [17,18]. A Brain Emotional Learning Based Intelligent Controller (BELBIC) [19][20][21][22] has been successfully employed for making decisions and controlling simple linear systems as in [23], as well as in non-linear systems such as control of a power system, speed control of a magnet synchronous motor and automatic voltage regulator (AVR) system [24][25][26][27][28], micro-heat exchanger [29], flight control [30], and positioning control of SIMO Overhead Traveling Crane [31]. The results indicate the ability of BEL-BIC to control unknown non-linear dynamic systems.…”

Section: Introductionmentioning

confidence: 99%

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PSO-BELBIC scheme for two-coupled distillation column process

Dorrah

El-Garhy

EL-Shimy

2011

Journal of Advanced Research

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In the two-coupled distillation column process, keeping the tray temperatures within a specified range around their steady state values assures the specifications for top and bottom product purity. The two-coupled distillation column is a 4 Input/4 Output process. Normally, control engineers decouple the process into four independent loops. They assign a PID controller to control each loop. Tuning of conventional PID controllers is very difficult when the process is subject to external unknown factors. The paper proposes a Brain Emotional Learning Based Intelligent Controller (BELBIC) to replace conventional PID controllers. Moreover, the values of BELBIC and PID gains are optimized using a particle swarm optimization (PSO) technique with minimization of Integral Square Error (ISE) for all loops. The paper compares the performance of the proposed PSO-BELBICs with that of conventional PSO-PID controllers. PSO-BELBICs prove their usefulness in improving time domain behavior with keeping robustness for all loops.

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Section: Brain Emotional Learning Based Intelligent Controller (Belbimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

PSO-BELBIC scheme for two-coupled distillation column process

Dorrah

El-Garhy

EL-Shimy

2011

Journal of Advanced Research

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“…[4] a new intelligent control approach for path tracking and collision control of a vehicle used in automated highway systems has been studied, which a modified BELBIC controller has been applied to a sixth order model of the vehicle. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Verification of intelligent control of a launch vehicle with HILS

et al. 2011

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The reliability of an intelligent self tuning controller called the brain emotional learning based intelligent controller (BELBIC) to attitude control of a nonlinear launch vehicle (LV) simulation with hardware-in-the loop simulation (HILS) is studied. To set up the HIL system of the LV a six-degree of freedom simulation of the LV and a hydraulic actuator, which was used for the pitch channel thrust vector control (TVC) actuator of the LV, is performed. The results of the BELBIC controller with a fuzzy controller (FC) and a PID controller in this HILS of the LV to control the pitch channel of the LV have been compared.

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“…The first applied mathematical model of LS was proposed by Morén and Balkenius [7][8] which is a neuropsychological motivated mathematical model. This basic model and its modified versions [9][10] have been utilized in various applications including: control application, prediction and alarm systems [11][12][13][14][15][16][17][18][19]. A control algorithm based on LS model was introduced by Lucas et al [9][10] which is an action generation mechanism based on sensory inputs and emotional cues.…”

Section: Introductionmentioning

confidence: 99%

A simple Mathematical Fuzzy Model of Brain Emotional Learning to Predict Kp Geomagnetic Index

Lotfi¹,

Keshavarz²

2014

Int J Intell Syst Appl Eng

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Abstract:In this paper, we propose fuzzy mathematical model of brain limbic system (LS) which is responsible for emotional stimuli. Here the proposed model is utilized to predict the chaotic activity of the earth's magnetosphere. Numerical results show that the correlation of the results obtained from the proposed fuzzy model is higher than non-fuzzy models. Hence, the proposed model can be applied in real time chaotic time series prediction.

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Designing PID and BELBIC controllers in path tracking and collision problem in automated highway systems

Cited by 21 publications

References 9 publications

PSO-BELBIC scheme for two-coupled distillation column process

PSO-BELBIC scheme for two-coupled distillation column process

Verification of intelligent control of a launch vehicle with HILS

A simple Mathematical Fuzzy Model of Brain Emotional Learning to Predict Kp Geomagnetic Index

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