Optimal tuning of Brain Emotional Learning Based Intelligent Controller using Clonal Selection Algorithm

Jafari, Mohammad; Shahri, Alireza Mohammad; Elyas, Seyyed Hamid

doi:10.1109/iccke.2013.6682810

Cited by 19 publications

(10 citation statements)

References 19 publications

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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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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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“…For instance, genetic algorithm is adopted for optimally tuning BELBIC parameters in [31] while a particle swarm optimisation-based approach is implemented in [32]. Jafari et al in [33] adopted the clonal selection algorithm to obtain BELBIC parameters, where it has been successfully applied for controlling a single-link flexible joint manipulator. Moreover, a fuzzy tuning of BELBIC parameters has been proposed in [34] and successfully applied for controlling a chaotic system and an inverted-double pendulum system.…”

Section: Belbic Structurementioning

confidence: 99%

Biologically inspired adaptive intelligent secondary control for MGs under cyber imperfections

Jafari¹,

Ghasemkhani²,

Sarfi³

et al. 2019

IET cyber-phys. syst.

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“…In this regard, the development of control strategies with less dependency on the full knowledge of the system dynamics and with reliable online training phase is essential. In recent years, learning based approaches have been extensively utilized for successfully solving diverse complex problems [17][18][19][20][21]. From a control system point of view, neural network based approaches are effective when the system dynamics are fully or partially unknown.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Control for Unmanned Aerial Systems with System Uncertainties and Disturbances Using Artificial Neural Network

Jafari

2018

Drones

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Stabilizing the Unmanned Aircraft Systems (UAS) under complex environment including system uncertainties, unknown noise and/or disturbance is so challenging. Therefore, this paper proposes an adaptive neural network based intelligent control method to overcome these challenges. Based on a class of artificial neural network, named Radial Basis Function (RBF) networks an adaptive neural network controller is designed. To handle the unknown dynamics and uncertainties in the system, firstly, we develop a neural network based identifier. Then, a neural network based controller is generated based on both the identified model of the system and the linear or nonlinear controller. To ensure the stability of the system during its online training phase, the linear or nonlinear controller is utilized. The learning capability of the proposed intelligent controller makes it a promising approach to take system uncertainties, noises and/or disturbances into account. The satisfactory performance of the proposed intelligent controller is validated based on the computer based simulation results of a benchmark UAS with system uncertainties and disturbances, such as wind gusts disturbance.

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Optimal tuning of Brain Emotional Learning Based Intelligent Controller using Clonal Selection Algorithm

Cited by 19 publications

References 19 publications

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

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

Biologically inspired adaptive intelligent secondary control for MGs under cyber imperfections

Intelligent Control for Unmanned Aerial Systems with System Uncertainties and Disturbances Using Artificial Neural Network

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