Creating autonomous Micro Aerial Vehicles for executing complex missions poses various challenges, including safe navigation in the presence of external wind disturbances. Most current navigation methods handle external wind disturbances through real-time estimation and rejection algorithms in the control stage, but lack safety guarantees in strong winds. Recent robust methods provide safety guarantees but can be overly conservative. With the availability of more powerful computing devices, data-driven control algorithms are becoming increasingly feasible. Combining Gaussian Process models with Model Predictive Control has shown to enhance safety and performance in various control applications. Moreover, Model Predictive Control has been extended to solve more complex optimization algorithms that combine trajectory generation and tracking, preventing reference trajectories that are risky and challenging to control in the presence of disturbances.

This research aims to improve quadrotor navigation in windy environments by using Gaussian Processes to model wind disturbances. The Gaussian Process model is integrated with a Model Predictive Contouring Control formulation that combines trajectory generation and control into one optimization problem. A nominal model of the quadrotor is derived and the Gaussian Process disturbance model is trained with the quadrotor position as input and wind disturbance as output. The wind disturbance map, along with the nominal model, is implemented in the Model Predictive Contouring Controller to consider both the mean and uncertainty of the resulting probabilistic model.

This study validates the use of Gaussian Processes to model complex wind disturbances in quadrotor navigation. The wind disturbance map is trained from available state information, with data collected using an optimal exploration design to minimize uncertainty and reduce exploration times. The hyperparameters involved in training the Gaussian Process model are discussed and the implementation of sparse Gaussian Processes is outlined to make it real-time feasible for the Model Predictive Contouring Control formulation. Including the prediction model by incorporating chance constraints results in improved tracking and increased robustness in cluttered environments compared to the nominal Model Predictive Contouring Control formulation. The proposed algorithm is shown to outperform state-of-the-art methods for safe quadrotor navigation in windy and cluttered environments, being able to handle more complex wind fields than existing methods while also being less conservative.

Recent developments have enabled the mass production of cheap, high-performance multirotors. As a result of this, the multirotor has found a large variety of different uses. Testing new algorithms using real-life flights is costly in terms of time and potentially in terms of materials in the case of a crash. Simulators have quickly gained prominence as a tool in algorithm development. The accuracy of these simulators is vital to ensure the step from simulated flights to real-life testing is as small as possible. Current simulator models are held back due to a lack of properly identified multirotor coefficients. Often, the coefficients from a different multirotor are used, even if the new multirotor is vastly different in shape and size. Additionally, most researchers do not have a sufficient overview of which parts of the simulator are most important in providing an accurate simulator environment.

This research will give an overview of the different parts that influence the simulator results. The simulation of the motor controllers and rotor aerodynamics is identified as a major contributor to inaccuracies. The coefficients used in these two components will be found by performing a system identification process of the NXP HoverGames quadcopter. This identification process entails determining the mass, dimensions, inertia, motor response and thrust, torque, rotor drag and rolling moment coefficients. Experiments will be performed to find the correct coefficient values and to evaluate the impact that each effect has on the simulator's performance. A focus is put on ease of repeatability, such that other researchers can use the same process for their specific multirotor.

The theoretical background behind each effect in the motor controller and rotor aerodynamics models is discussed. This theoretical knowledge is used to perform experiments to find the coefficients for each effect and validate them. As a result, new coefficients for the motor controller simulator, thrust, torque and rotor drag were found. Additionally, this process showed that the rolling moment is much smaller than the errors currently existing in the model and this effect has, therefore, been ignored. The motor controller simulation is identified as the largest cause of inaccuracies in the current model. This simulation maps the motor commands to the output rotational velocity of the rotor and simulates the time response of this system. The formulas currently used for this do not provide a sufficient model of real-life behaviour. An analysis is done to gain a better understanding of what variables influence motor behaviour.

Finally, the new coefficients are put to the test in a variety of different trajectories to compare the trajectory tracking performance with the old model. This comparison consists of two parts. In the first part, a qualitative analysis is used to understand the difference in simulator behaviour. In the second part, a set of metrics are defined to quantify the difference in accuracy. This comparison process is used to prove that the new model provides significant improvements compared to the old coefficients, especially in the case of faster, more aggressive manoeuvres.