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Auto-Tuning of Attitude Control System for Heterogeneous Multirotor UAS
Remote Sensing ; 14(7):1540, 2022.
Article in English | ProQuest Central | ID: covidwho-1785891
ABSTRACT
This paper presents a heterogeneous configuration of the multirotor unmanned aerial system (UAS) that features the combined characteristics of the helicopter and quadrotor in a single multirotor design, featuring the endurance and energy efficiency similar to a helicopter, while keeping the mechanical simplicity, control, and manoeuvrability of the standard quadrotor. Power needed for a rotorcraft to hover has the inverse relation with the rotor disc. Therefore, multiple small rotors of the quadrotor are energetically outperformed by a large rotor of the helicopter, for a similar size. Designing the stable control system for such a dynamically complex multirotor configuration remains the main challenge as the studies previously carried out on these designs have successfully demonstrated energy efficiency but at the cost of degraded attitude control. Advancements in the energetics of the multirotor results in enhanced endurance and range that could be highly effective in remote operation applications. However, a stable control system is required for accurate positioning. In this paper, a cascaded PID control approach is proposed to provide the control solution for this heterogeneous multirotor. Automatic tuning is proposed to design the PID controller for each loop of the cascade structure. A relay feedback experiment is conducted in a controlled environment, followed by identification of the open-loop frequency response and estimation of dynamics. Subsequently, PID controllers are tuned through approximated models with the help of tuning rules. A custom-designed flight controller is used to experimentally implement the proposed control structure. Presented experimental results demonstrate the efficacy of the proposed control strategy for heterogeneous multirotor UAS.
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Full text: Available Collection: Databases of international organizations Database: ProQuest Central Language: English Journal: Remote Sensing Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: ProQuest Central Language: English Journal: Remote Sensing Year: 2022 Document Type: Article