Abstract
This paper presents a survey of controller design techniques aimed at autonomous navigation and control of Unmanned Aerial Vehicles (UAVs), focusing on the challenge of aerodynamic uncertainty. Although many roadblocks exist, the most significant and challenging task for UAV navigation and control is the one of aerodynamic/model uncertainty. Current autopilots and controller designs for autonomous airplanes are mainly concerned with the feature of constant, unknown aerodynamic parameters, i.e., control and stability derivatives of the platform. This research focuses on a thorough investigation of the related theory and its applicability, centering on specific techniques that are able to control UAVs with rapidly changing, time-varying aerodynamic characteristics during flight. The scientific merit of this work is the comprehensive overview provided and the technical study that is performed, highlighting the advantages and disadvantages for each technique with respect to its efficiency and performance.
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Recommended by Associate Editor H. Jin Kim under the direction of Editor Chan Gook Park. This research was supported in part by National Science Foundation (NSF) Grant CMMI/DCSD 1728454.
Michail G. Michailidis received his B.Sc. in Mathematics and M.Sc. in Applied Mathematics from the Aristotle University of Thessaloniki, Greece. He graduated from the University of Denver in 2019 with a Ph.D. in Electrical Engineering and he is currently a research scientist at DU2SRI. His ongoing research project is controller design for unmanned aerial vehicles with time-varying aerodynamic uncertainties and his area of expertise is modeling, control and performance optimization of dynamical systems.
Matthew J. Rutherford is an Associate Professor in the Department of Computer Science with a joint appointment in the Department of Electrical and Computer Engineering at the University of Denver. He is also a Deputy Director of the DU Unmanned Systems Research Institute (DU2SRI). His research portfolio includes: the development of advanced controls and communication mechanisms for autonomous aerial and ground robots; applications of real-time computer vision to robotics problems using GPU-based parallel processing; testing and dynamic evaluation of embedded, real-time systems; development of complex mechatronic systems (mechanical, electrical, and software); the development of software techniques to reduce the amount of energy being consumed by hardware; development of a high-precision propulsion system for underwater robots.
Kimon P. Valavanis is a John Evans Professor and Director of Research and Innovation in the Department of Electrical and Computer Engineering, with a joint appointment in the Department of Computer Science at the University of Denver. He is also Director of the DU Unmanned Systems Research Institute (DU2SRI). He holds a Guest Professor appointment in the Department of Telecommunications, Faculty of Electrical Engineering and Computing at the University of Zagreb, Croatia. His research interests span the areas of intelligent control, robotics and automation, and distributed intelligent systems, focusing on: integrated control and diagnostics of unmanned systems; modeling and formation control of cooperative robot teams; navigation/control of unmanned aerial vehicles; modeling, design and development of complex mechatronic systems; design of the next generation of unmanned systems; mathematical theories for intelligent machines. He is Fellow of the American Association for the Advancement of Science (AAAS), Senior Member of IEEE, Editor-in-Chief of the Journal of Intelligent and Robotic Systems (Springer), and Fulbright Scholar.
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Michailidis, M.G., Rutherford, M.J. & Valavanis, K.P. A Survey of Controller Designs for New Generation UAVs: The Challenge of Uncertain Aerodynamic Parameters. Int. J. Control Autom. Syst. 18, 801–816 (2020). https://doi.org/10.1007/s12555-018-0489-8
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DOI: https://doi.org/10.1007/s12555-018-0489-8