MODELING, AUTONOMOUS NAVIGATION AND CONTROL OF MULTIROTOR UAVs: Merging Conventional and Proposed New Methodologies
Organizers and Lecturers
Simone Martini, Kimon P. Valavanis, Serhat Sonmez1, Laura Sopegno2, Matt J. Rutherford
D. F. Ritchie School of Engineering and Computer Science
DU Unmanned Systems Research Institute (DU2SRI)
University of Denver – Denver, CO 80208
E-mail:
Summary
The goal of the Tutorial is to provide a comprehensive framework that supports modeling, navigation, and control of multirotor UAVs with different onboard sensor modalities and endowed with different levels of autonomous functionality. A combination of conventional and new (linear, nonlinear, and learning-based) techniques for controller design are proposed, implemented and tested, under nominal and detrimental conditions, and different noise levels and wind gusts.
The first part of the tutorial centers around detailed and accurate multirotor modeling. The two classical modeling formulations of Newton-Euler (N-E) and Euler-Largange (E-L) are described, and the equations of motion are derived. Gyroscopic effects are introduced and included in both models. Different drag types (i.e., drag due to blade flapping, induced, translational, profile and parasitic) are explicitly derived and are accounted for. First order motor dynamics are also part of the model, along with feedback noise. Sudden changes of mass (for example, due to payload variations), and wind gusts are considered when deriving the multirotor model.
However, to prove the exact mathematical equivalence of the N-E and E-L formulations, a corrected multirotor attitude model is derived following the conventional and found in the literature E-L formulation, which is named c-E-L. When considering the N-E and the c-E-L formulations (for different types of multirotor UAVs, quadrotors, hexacopters, etc.), either complete or simplified, it is shown that obtained results are identical. For completeness purposes, and for comparisons, when needed, a quaternion-based modeling alternative is also discussed.
The second part of the tutorial focuses on the configuration of the multirotor navigation control architecture and on controller design, development, implementation, and testing, as well as performance evaluation and comparison. The overall navigation control architecture is hierarchical, hardware agnostic, and it follows a modular, component (i.e., sensors) plug in–plug out, design. It is fault tolerant in nature as it accounts for single and multiple failures, it includes an emergency landing component and allows for human–multirotor interaction. Controller design spans model-based, model-free, and combined data-driven and model-based approaches. Linear, linearized and fully nonlinear control approaches are presented first, followed by Fuzzy Logic and Artificial Neural Network based designs. Learning-based (machine learning, deep learning, reinforcement learning) control of multirotors is introduced along with advantages and disadvantages of online and offline techniques. Empasis is given in what is being learned, why, how, and how fast.
Then, a new novel, analytical controller design methodology is introduced, implemented, and tested, which takes advantage of the Koopman operator, and results in a fully controllable and stable (or stabilizable) system. Trajectory tracking performance is evaluated and compared by applying different controllers on multirotors functioning in 3-D uncertain environments. Chosen trajectories include non-aggressive and aggressive multirotor maneuvers, under nominal and detrimental conditions.The third part of the tutorial presents a ‘simulation platform’ to serve as the ‘common denominator’ when conducting simulated experiments in diverse environments. The software tools that are used to develop this platform are MATLAB/Simulink, ROS/Gazebo, Simscape and the X-Plane Simulator. The aim is to acquire, through detailed studies, information about ‘how good’ a controller is, and under what conditions. Moreover, the developed software will be Open Source to be used by interested researchers and scientists.
Workshop Objectives
The central objective of the Tutorial is to provide a foundational basis for accurate modeling and controller design for multirotors, facilitating at the same time implementation, testing and performance evaluation under realistic nominal and detrimental conditions.
Key Topics
The topics that will be addressed and discussed center around the conventional Newton-Euler and Euler-Lagrange modeling formulations, their similarities and differences and their exact equivalence, which, then, paves the way for accurate controller designs. Controller designs span model based, and data driven techniques, a combination of both, soft computing approaches and learning control. Two key topics that are detailed and addressed are the Koopman based framework for controller designs and learning based modeling and control for multirotor UAVs.
Workshop Format
The Tutorial will include a series of detailed presentations what will include multimedia modules.
Target Audience
The Tutorial is suitable for graduate students conducting research in related areas, scientists, and engineers, as well as developers and manufacturers of UAVs.
Tentative Outcome
It is expected that participants will obtain a deep understanding of modeling challenges that need to be addressed and overcome to obtain ‘accurate models’ for a class of highly nonlinear and underactuated systems, and how such models may be used to derive and implement robust controllers with performance guarantees.
1Serhat Sonmez is now with Istanbul Medeniyet University, Turkey
2Laura Sopegno is also with the University of Palermo, Italy