Biologically-Inspired Intelligent Flocking Control for Networked Multi-UAS with Uncertain Network Imperfections

Jafari, Mohammad; Xu, Hao

doi:10.3390/drones2040033

Cited by 5 publications

(5 citation statements)

References 32 publications

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“…Notably, the amygdala lacks the ability to unlearn any previously acquired emotional responses, highlighting the orbitofrontal cortex's responsibility in inhibiting inappropriate reactions. The BEL model receives two pivotal external inputs: namely, emotional signal (ES) and sensory input (SI) [20,21,28,29].…”

Section: Brain Emotional Learningmentioning

confidence: 99%

“…Various approaches have been employed to adjust the BEL parameters [21,[28][29][30]. In this study, we adopted the trial-and-error approach similar to those suggested in the literature [21].…”

Section: Thalamusmentioning

confidence: 99%

“…For further details on the Emotional Learning and BEL-based controller utilized in this study, the readers are directed to [19][20][21]28,29].…”

Section: Brain Emotional Learning For Closed-loop Control Of Autonomo...mentioning

confidence: 99%

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Machine Learning-Based Control of Autonomous Vehicles for Solar Panel Cleaning Systems in Agricultural Solar Farms

Hajiahmadi,

Jafari,

Reyhanoglu

2024

AgriEngineering

Self Cite

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This paper presents a machine learning (ML)-based approach for the intelligent control of Autonomous Vehicles (AVs) utilized in solar panel cleaning systems, aiming to mitigate challenges arising from uncertainties, disturbances, and dynamic environments. Solar panels, predominantly situated in dedicated lands for solar energy production (e.g., agricultural solar farms), are susceptible to dust and debris accumulation, leading to diminished energy absorption. Instead of labor-intensive manual cleaning, robotic cleaners offer a viable solution. AVs equipped to transport and precisely position these cleaning robots are indispensable for the efficient navigation among solar panel arrays. However, environmental obstacles (e.g., rough terrain), variations in solar panel installation (e.g., height disparities, different angles), and uncertainties (e.g., AV and environmental modeling) may degrade the performance of traditional controllers. In this study, a biologically inspired method based on Brain Emotional Learning (BEL) is developed to tackle the aforementioned challenges. The developed controller is implemented numerically using MATLAB-SIMULINK. The paper concludes with a comparative analysis of the AVs’ performance using both PID and developed controllers across various scenarios, highlighting the efficacy and advantages of the intelligent control approach for AVs deployed in solar panel cleaning systems within agricultural solar farms. Simulation results demonstrate the superior performance of the ML-based controller, showcasing significant improvements over the PID controller.

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Section: Brain Emotional Learningmentioning

confidence: 99%

Section: Thalamusmentioning

confidence: 99%

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Machine Learning-Based Control of Autonomous Vehicles for Solar Panel Cleaning Systems in Agricultural Solar Farms

Hajiahmadi,

Jafari,

Reyhanoglu

2024

AgriEngineering

Self Cite

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“…Valencia and Kim, in [42], used BELBIC to build a control system capable of autonomously operating multiple quadrotors in the leader-follower configuration. Interesting research may be found in the works of Jafari et al, especially [43,44], where real-time flocking control of multi-agent systems in the presence of system uncertainties and dynamic environments and distributed intelligent flocking control of networked multi-UAS were considered, respectively.…”

Section: The State Of the Artmentioning

confidence: 99%

Minimum Energy Control of Quadrotor UAV: Synthesis and Performance Analysis of Control System with Neurobiologically Inspired Intelligent Controller (BELBIC)

Giernacki

2022

Energies

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There is a strong trend in the development of control systems for multi-rotor unmanned aerial vehicles (UAVs), where minimization of a control signal effort is conducted to extend the flight time. The aim of this article is to shed light on the problem of shaping control signals in terms of energy-optimal flights. The synthesis of a UAV autonomous control system with a brain emotional learning based intelligent controller (BELBIC) is presented. The BELBIC, based on information from the feedback loop of the reference signal tracking system, shows a high learning ability to develop an appropriate control action with low computational complexity. This extends the capabilities of commonly used fixed-value proportional–integral–derivative controllers in a simple but efficient manner. The problem of controller tuning is treated here as a problem of optimization of the cost function expressing control signal effort and maximum precision flight. The article introduces several techniques (bio-inspired metaheuristics) that allow for quick self-tuning of the controller parameters. The performance of the system is comprehensively analyzed based on results of the experiments conducted for the quadrotor model.

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“…A real improvement can be achieved if all the aircraft can properly collaborate to accomplish the mission target by coordinating their tasks. Within this frame, an extensive research field focuses on the swarming behavior, or flocking, known in nature as the collective behavior of a large number of agents able to interact and share a common objective [12][13][14][15][16][17][18][19][20]. In particular, flocking is the ability of a group of autonomous agents (being aircraft or robots) to move like a swarm of birds or insects.…”

Section: Introductionmentioning

confidence: 99%

Decentralized Triangular Guidance Algorithms for Formations of UAVs

Bassolillo

Blasi

D’Amato

et al. 2021

Drones

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This paper deals with the design of a guidance control system for a swarm of unmanned aerial systems flying at a given altitude, addressing flight formation requirements that can be formulated constraining the swarm to be on the nodes of a triangular mesh. Three decentralized guidance algorithms are presented. A classical fixed leader–follower scheme is compared with two alternative schemes: the former is based on the self-identification of one or more time-varying leaders; the latter is an algorithm without leaders. Several operational scenarios have been simulated involving swarms with obstacles and an increasing number of aircraft in order to prove the effectiveness of the proposed guidance schemes.

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Biologically-Inspired Intelligent Flocking Control for Networked Multi-UAS with Uncertain Network Imperfections

Cited by 5 publications

References 32 publications

Machine Learning-Based Control of Autonomous Vehicles for Solar Panel Cleaning Systems in Agricultural Solar Farms

Machine Learning-Based Control of Autonomous Vehicles for Solar Panel Cleaning Systems in Agricultural Solar Farms

Minimum Energy Control of Quadrotor UAV: Synthesis and Performance Analysis of Control System with Neurobiologically Inspired Intelligent Controller (BELBIC)

Decentralized Triangular Guidance Algorithms for Formations of UAVs

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