The e-sport of drone-racing involves human pilots to race against time. Recently, drone races have also gone fully-autonomous. As a result, these agile robotic platforms not only pose challenges of flying fast to the participating pilots but also create challenges for the flight control computers. As a result, the concept of autonomous drone racing has gained significant attention from research groups around the world. These races aim to push the boundaries of perception and control algorithms, while simultaneously mitigating the real-world uncertainty of execution on autonomous systems. While perception algorithms face challenges due to limited feature detection, high motion blur and computational requirements, control algorithms face challenges of convergence to the desired trajectories that are planned out in the race arena.
This thesis addresses the challenge of control for racing, which is responsible for guiding the drone to design and track desired trajectories for fast flights. The control sub-modules of racing drones are responsible for generating trajectories for fastest possible flights and also for obeying these generated commands. Additionally, the requirement of limited algorithm complexity is added to match the philosophy of computationally efficient algorithms at the Micro Air Vehicle Laboratory. However, to address the requirements of these control sub-modules, the prerequisite of accurate state estimation always persists. Assigning control actions to a robot without information on the current state of the robot is rather unwise. As a result, this thesis first aims to perform accurate state estimation before designing controllers for time-optimal trajectory tracking. Again, another constraint of using only a single sensor (i.e. the Inertial Measurement Unit) is added to make the drone race in GPS denied environments. As a result, the goal of the thesis is two-fold i.e. making accurate state estimators while using limited sensors and designing optimal controllers for taking the quickest trajectory through the arena. To achieve the goal of accurate state estimation, existing techniques are studied. Several features from each of these methods are selected to design a new estimator. To achieve the goal of time-optimal trajectory generation, firstly, the flaws of traditional control methods are pointed out. A new optimal-control technique is proposed, which makes use of fundamental principles dating back several decades. This principle is then fused along with present-day optimization solvers. Finally, the proposed state estimation and control algorithm are compared against prior (benchmarked) techniques in the area. Compared to existing optimal control techniques, the proposed algorithm leads to faster trajectories and consumes less computational power onboard.