Modeling, Simulation and Control of Multirotor Systems
Abstract
Multirotor systems have seen increasing utilization in recent years in both lift applications, such as multirotor drones for consumers, industry, military, transportation etc., and for power production purposes in the context of multirotor wind turbines. Efficient modeling, simulation and control of such systems requires a thorough understanding of the underlying physics.
This thesis approaches the modeling problem by leveraging a cybernetic approach that construes the overall system as an interconnection of three distinct subsystems: A mechanical module, an airloads module and an inflow module. We believe the results in the thesis improve upon the state-of-the-art in the understanding of how to model airloads and inflow effects with relevance to both multirotor wind turbines and multirotor drones. A paper describing the developed airloads model was awarded the ”Best Paper Award” on the international research conference ICSTCC 2022.
The model described in the thesis is employed towards the development of novel control strategies, design considerations and simulation tools. The thesis also explores the necessity of each subsystem, as some are more important than others, depending on the multirotor configuration. Other system properties such as stability, scalability and passivity are also investigated using analytical methods, numerical methods and simulation. Numerical validation of the models against experimental data and empirical data from theory are also performed, successfully recreating well-known aerodynamic phenomena.
Finally, a numerical solver is used to find optimal steady-state control strategies that are used to inform simpler, yet as efficient, control strategies based on the underlying system properties. The efficacy of traditional control strategies are compared to optimal solutions and a discussion on the distinct features of multirotor control problems is presented.
The results presented in the thesis contain information not only about how to model and control multirotor systems, but it also makes it possible to extrapolate the knowledge in order to evaluate current designs, suggest improvements and even inform new designs to fully and optimally utilize the unique features of multirotor systems.