Brain Emotional Learning Based Intelligent Decoupler for Nonlinear Multi‐Input Multi‐Output Distillation Columns

El-Saify, M. H.; El-Garhy, Ahmed M.; El-Sheikh, G. A.

doi:10.1155/2017/8760351

Cited by 19 publications

(10 citation statements)

References 40 publications

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“…The authors showed that a significant increase in closed-loop performance was achieved by having a particle swarm optimization algorithm to search for the best BELBIC parameters. Those results were corroborated by the more recent work of [30].…”

Section: The Bel-based Control Systemsupporting

confidence: 82%

Evolutionary-Based BEL Controller Applied to a Magneto-Rheological Structural System

2018

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This work addresses the problem of finding the best controller parameters in order to improve the response of a single degree-of-freedom structural system under earthquake excitation. The control paradigm considered is based on brain emotional learning (BEL) and the actuation over the building dynamics is carried out by changing the stiffness of a magneto-rheological damper. A typical BEL-based controller requires the definition of several parameters which can prove difficult and non-intuitive to obtain. For this reason, an evolutionary-based search technique has been added to the current problem framework in order to automate the controller design. In particular, the particle swarm optimization method is chosen as the evolutionary based technique to be integrated within the current control paradigm. The obtained results suggest that, indeed, it is possible to parametrize a BEL controller using an evolutionary-based algorithm. Moreover, a simulation shows that the obtained results can outperform the ones obtained by manual tuning each controller parameter individually.

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Section: The Bel-based Control Systemsupporting

confidence: 82%

Evolutionary-Based BEL Controller Applied to a Magneto-Rheological Structural System

2018

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“…1 and (1). The subtraction of large orbitofrontal cortex output (W T sd SI sd ) in ( 1) depicts the unlearning characterstic of amygdala [22,30].…”

Section: Speed Control Loopmentioning

confidence: 99%

Brain Emotional Learning and Adaptive Model Predictive Controller for Induction Motor Drive: A New Cascaded Vector Control Topology

Affan

Uddin

2021

Int. J. Control Autom. Syst.

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With the development of high-speed microprocessors, it is now possible to implement mathematically complex vector control algorithms without compromising on the performance of motor drive. Among vector control techniques space vector proportional-integral (PI), direct-torque control (DTC), field-oriented control (FOC), model-predictive control (MPC) are being widely used in industries. But their limitations have urged researchers to develop more advance techniques. In this paper, a new technique learning and adaptive model — based predictive control (termed as LAMPC) is proposed for the vector control of three phase induction motor. In the proposed method, the dynamic model of induction motor is updated adaptively based on prediction (receding horizon principle) for the inner control loop (current control) while the brain emotional learning-based intelligent controller (BELIC) is used for the outer control loop (speed control). The proposed methodology offers desired dynamic response, precise tracking, good disturbance handling capability along with satisfactory steady-state performance. To show the effectiveness of the proposed approach, benchmark simulation results for various inputs are presented using MATLAB/Simulink. Finally, the detailed qualitative and quantitative comparison of the proposed LAMPC is made with the most relevant vector techniques to show its significance.

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“…The application of PSO in the context of BEL-based controller design is not a novelty and has already been considered in several articles [39,[56][57][58][59]. The main difference on this work, regarding the above references, concerns the formulation of the objective function.…”

Section: The Pso Optimization Algorithmmentioning

confidence: 99%

Semi-Active Vibration Control of a Non-Collocated Civil Structure Using Evolutionary-Based BELBIC

2019

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A buildings resilience to seismic activity can be increased by providing ways for the structure to dynamically counteract the effect of the Earth’s crust movements. This ability is fundamental in certain regions of the globe, where earthquakes are more frequent, and can be achieved using different strategies. State-of-the-art anti-seismic buildings have, embedded on their structure, mostly passive actuators such as base isolation, Tuned Mass Dampers (TMD) and viscous dampers that can be used to reduce the effect of seismic or even wind induced vibrations. The main disadvantage of this type of building vibration reduction strategies concerns their inability to adapt their properties in accordance to both the excitation signal or structural behaviour. This adaption capability can be promoted by adding to the building active type actuators operating under a closed-loop. However, these systems are substantially larger than passive type solutions and require a considerable amount of energy that may not be available during a severe earthquake due to power grid failure. An intermediate solution between these two extremes is the introduction of semi-active actuators such as magneto–rheological dampers. The inclusion of magneto–rheological actuators is among one of the most promising semi-active techniques. However, the overall performance of this strategy depends on several aspects such as the actuators number and location within the structure and the vibration sensors network. It can be the case where the installation leads to a non-collocated system which presents additional challenges to control. This paper proposes to tackle the problem of controlling the vibration of a non-collocated three-storey building by means of a brain–emotional controller tuned using an evolutionary algorithm. This controller will be used to adjust the stiffness coefficient of a magneto–rheological actuator such that the building’s frame oscillation under earthquake excitation, is mitigated. The obtained results suggest that, using this control strategy, it is possible to reduce the building vibration to secure levels.

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Brain Emotional Learning Based Intelligent Decoupler for Nonlinear Multi‐Input Multi‐Output Distillation Columns

Cited by 19 publications

References 40 publications

Evolutionary-Based BEL Controller Applied to a Magneto-Rheological Structural System

Evolutionary-Based BEL Controller Applied to a Magneto-Rheological Structural System

Brain Emotional Learning and Adaptive Model Predictive Controller for Induction Motor Drive: A New Cascaded Vector Control Topology

Semi-Active Vibration Control of a Non-Collocated Civil Structure Using Evolutionary-Based BELBIC

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