An Intelligent Hybrid Artificial Neural Network-Based Approach for Control of Aerial Robots

Siddharth Patel, Andriy Sarabakha, Dogan Kircali, Erdal Kayacan*

*Corresponding author for this work

    Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

    Abstract

    In this work, a learning model-free control method is proposed for accurate trajectory tracking and safe landing of unmanned aerial vehicles (UAVs). A realistic scenario is considered where the UAV commutes between stations at high-speeds, experiences a single motor failure while surveying an area, and thus requires to land safely at a designated secure location. The proposed challenge is viewed solely as a control problem. A hybrid control architecture – an artificial neural network (ANN)-assisted proportional-derivative controller – is able to learn the system dynamics online and compensate for the error generated during different phases of the considered scenario: fast and agile flight, motor failure, and safe landing. Firstly, it deals with unmodelled dynamics and operational uncertainties and demonstrates superior performance compared to a conventional proportional-integral-derivative controller during fast and agile flight. Secondly, it behaves as a fault-tolerant controller for a single motor failure case in a coaxial hexacopter thanks to its proposed sliding mode control theory-based learning architecture. Lastly, it yields reliable performance for a safe landing at a secure location in case of an emergency condition. The tuning of weights is not required as the structure of the ANN controller starts to learn online, each time it is initialised, even when the scenario changes – thus, making it completely model-free. Moreover, the simplicity of the neural network-based controller allows for the implementation on a low-cost low-power onboard computer. Overall, the real-time experiments show that the proposed controller outperforms the conventional controller.

    Original languageEnglish
    JournalJournal of Intelligent and Robotic Systems: Theory and Applications
    Volume97
    Issue2
    Pages (from-to)387-398
    Number of pages12
    ISSN0921-0296
    DOIs
    Publication statusPublished - Feb 2020

    Keywords

    • Artificial neural networks
    • Fast and agile manoeuvres
    • Fault tolerant control
    • Sliding mode control
    • Unmanned aerial vehicles

    Fingerprint

    Dive into the research topics of 'An Intelligent Hybrid Artificial Neural Network-Based Approach for Control of Aerial Robots'. Together they form a unique fingerprint.

    Cite this