Stability analysis of brain emotional intelligent controller with application to electrically driven robot manipulators

Zirkohi, Majid Moradi

doi:10.1049/iet-smt.2019.0261

Cited by 7 publications

(2 citation statements)

References 42 publications

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“…The limbic system computational model in the human brain served as the inspiration for this controller [18]. It has several uses in electric power systems, automobile systems, and spacecraft [19,20]. A few recently published papers in the suggested field exist.…”

Section: Introductionmentioning

confidence: 99%

Performance Improvement of an Electric Vehicle Charging Station Using Brain Emotional Learning-Based Intelligent Control

Zaid,

Albalawi,

Alatwi

et al. 2024

Processes

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Electric vehicle (EV) charging facilities are essential to their development and deployment. These days, autonomous microgrids that use renewable energy resources to energize charging stations for electric vehicles alleviate pressure on the public electricity grid. Nevertheless, controlling and managing such charging stations’ energy is difficult due to the nonlinearity and irregular character of renewable energy sources. The current research recommends using a Brain Emotional Learning Intelligent Control (BELBIC) controller to enhance an autonomous EV charging station’s performance and power management. The charging station uses a battery to store energy and is primarily powered by photovoltaic (PV) solar energy. The principles of BELBIC are dependent on emotional cues and sensory inputs, and they are based on an emotion processing system in the brain. Noise and parameter variations do not affect this kind of controller. In this study, the performance of a conventional proportional–integral (PI) controller and the suggested BELBIC controller is evaluated for variations in solar insolation. The various parts of an EV charging station are simulated and modelled by the MATLAB/Simulink framework. The findings show that, in comparison to the conventional PI controller, the suggested BELBIC controller greatly enhances the transient responsiveness of the EV charging station’s performance. The EV keeps charging while the storage battery perfectly saves and keeps steady variations in PV power, even in the face of any PV insolation disturbances. The suggested system’s simulation results are provided and scrutinized to confirm the concept’s suitability. The findings validate the robustness of the suggested BELBIC control versus parameter variations.

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Section: Introductionmentioning

confidence: 99%

Performance Improvement of an Electric Vehicle Charging Station Using Brain Emotional Learning-Based Intelligent Control

Zaid,

Albalawi,

Alatwi

et al. 2024

Processes

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“…The idea of this controller was derived from the computational model of the limbic system in the human brain [26]. It has various applications in space vehicles, electric power systems, and automotive systems [27][28][29]. The main advantages of the BELBIC controller are its good robustness, simplicity, effectiveness, and flexibility in selecting the emotional cues and sensory inputs for a certain application.…”

Section: Introductionmentioning

confidence: 99%

Analysis of a Hybrid Wind/Photovoltaic Energy System Controlled by Brain Emotional Learning-Based Intelligent Controller

et al. 2022

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Recently, hybrid wind/PV microgrids have gained great attention all over the world. It has the merits of being environmentally friendly, reliable, sustainable, and efficient compared to its counterparts. Though there has been great development in this issue, the control and energy management of these systems still face challenges. The source of those challenges is the intermittent nature of both wind and PV energy. On the other hand, a new intelligent control technique called Brain Emotional Learning-Based Intelligent Controller (BELBIC) has garnered more interest. This paper proposes the control and energy management of hybrid wind/PV microgrids using a BELBIC controller. To design the system, simple power and energy analyses were proposed. The proposed microgrid was modeled and simulated using MATLAB. The responses of the energy system were tested under two different types of disturbances, namely step and ramp disturbances. These disturbances are applied to the wind speed, the irradiation level of the PV, and the load power. The results indicate that the AC load voltage and frequency are steady with negligible transients against the previous disturbance. In addition, the performance is better than that of the classical PI controller. Also, energy management acts perfectly to compensate for the intermittence and disturbances of the wind and PV energies. On the other hand, the system robustness against model parameters uncertainties in the microgrid parameters are studied.

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Intelligent robust pitch control of wind turbine using brain emotional learning

Cao

Yazdani

Mahmoudi

2021

Int Trans Electr Energ Syst

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Summary This article proposes an implementation of the brain emotional learning‐based intelligent controller (BELBIC) for high‐precision and robust pitch control of a 5‐MW wind turbine. The proposed model‐free controller is a biologically inspired method emulating the learning in the mammalian's limbic system and it is independent of the model dynamics and variations that might occur in a system. The auto‐learning capability of the BELBIC allows accommodating the nonlinearities associated with the wind turbine model and provides a reasonable degree of disturbance enabling precise and robust tracking of the pitch angle, even under unforeseen wind conditions. To investigate the trajectory tracking performance and robustness of the BELBIC in various unpredictable wind conditions, multiple uncertain wind speed conditions including gust and random wind, are simulated in MATLAB/Simulink. The results of simulations are compared with two benchmark control methods, fuzzy‐proportional‐integral‐derivative and gain‐scheduling proportional‐integral. The simulation results clearly indicate that the BELBIC serves better performance and robustness while guaranteeing quick and precise pitch angle response as well as its ability in dealing with nonlinearity and unforeseen wind conditions in comparison to the other two benchmark control methods.

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Stability analysis of brain emotional intelligent controller with application to electrically driven robot manipulators

Cited by 7 publications

References 42 publications

Performance Improvement of an Electric Vehicle Charging Station Using Brain Emotional Learning-Based Intelligent Control

Performance Improvement of an Electric Vehicle Charging Station Using Brain Emotional Learning-Based Intelligent Control

Analysis of a Hybrid Wind/Photovoltaic Energy System Controlled by Brain Emotional Learning-Based Intelligent Controller

Intelligent robust pitch control of wind turbine using brain emotional learning

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