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

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  • Wojciech Giernacki

    (Faculty of Automatic Control, Robotics and Electrical Engineering, Institute of Robotics and Machine Intelligence, Poznan University of Technology, ul. Piotrowo 3a, 60-965 Poznan, Poland)

Abstract

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.

Suggested Citation

  • Wojciech Giernacki, 2022. "Minimum Energy Control of Quadrotor UAV: Synthesis and Performance Analysis of Control System with Neurobiologically Inspired Intelligent Controller (BELBIC)," Energies, MDPI, vol. 15(20), pages 1-23, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:20:p:7566-:d:941568
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    References listed on IDEAS

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    1. Yulin Li & Ben Niu & Guangdeng Zong & Jinfeng Zhao & Xudong Zhao, 2022. "Command filter-based adaptive neural finite-time control for stochastic nonlinear systems with time-varying full-state constraints and asymmetric input saturation," International Journal of Systems Science, Taylor & Francis Journals, vol. 53(1), pages 199-221, January.
    2. Chou, Jui-Sheng & Truong, Dinh-Nhat, 2021. "A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean," Applied Mathematics and Computation, Elsevier, vol. 389(C).
    3. Yanwei Zhao & Haoyan Zhang & Zhongyu Chen & Huanqing Wang & Xudong Zhao, 2022. "Adaptive neural decentralised control for switched interconnected nonlinear systems with backlash-like hysteresis and output constraints," International Journal of Systems Science, Taylor & Francis Journals, vol. 53(7), pages 1545-1561, May.
    4. Adam Bondyra & Marek Kołodziejczak & Radosław Kulikowski & Wojciech Giernacki, 2022. "An Acoustic Fault Detection and Isolation System for Multirotor UAV," Energies, MDPI, vol. 15(11), pages 1-19, May.
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