This thesis presents the design, modeling, and control of a fully-actuated multi-rotor unmanned aerial vehicle (UAV). Unlike conventional multi-rotors, which suffer from two degrees of underactuation in their propeller plane, the choice of an unconventional propeller configuration in the new drone leads to an even distribution of actuation across the entire force-torque space. This allows the vehicle to produce any arbitrary combination of forces and torques within a bounded magnitude and hence execute motion trajectories unattainable with conventional multi-rotor designs. This system, referred to as the \omninospace, decouples the position and attitude controllers, simplifying the motion control problem. Position control is achieved using a PID feedback loop with gravity compensation, while attitude control uses a cascade architecture where the inner loop follows an angular rate command set by the outer attitude control loop. A novel model is developed to capture the disturbance effects among interacting actuator airflows of the \omninospace. Given a desired actuator thrust, the model computes the required motor command using the current battery voltage and thrusts of disturbing actuators. A system identification is performed to justify the use of a linear approximation for parameters in the model to reduce its computational footprint in real-time implementation. The \omni benefits from two degrees of actuation redundancy resulting in a control allocation problem where feasible force-torques may be produced through an infinite number of actuator thrust combinations. A novel control allocation approach is formulated as a convex optimization to minimize the \omnis energy consumption subject to the propeller thrust limits. In addition to energy savings, this optimization provides fault tolerance in the scenario of a failed actuator. A functioning prototype of the \omni is built and instrumented. Experiments carried out with this prototype demonstrate the capabilities of the new drone and its control system in following various translational and rotational trajectories, some of which would not be possible with conventional multi-rotors. The proposed optimization-based control allocation helps reduce power consumption by as much as 6\%, while being able to operate the drone in the event of a propeller failure. / Thesis / Master of Applied Science (MASc)

An approach to utilize Distributed Generation (DG) to minimize the total load shedding by analyzing the power system in Transactive energy framework is proposed. An algorithm to optimize power system in forward and spot markets to maximize an electric utility’s profit by optimizing purchase of power from DG is developed. The proposed algorithm is a multi-objective optimization with the main objective to maximize a utility’s profit by minimizing overall cost of production, load shedding, and purchase of power from distributed generators. This work also proposes a method to price power in forward and spot markets using existing LMP techniques. Transactive accounting has been performed to quantify the consumer payments in both markets. The algorithm is tested in two test systems; a 6-bus system and modified IEEE 14-bus system. The results show that by investing in DG, utility benefits from profit increase, load shedding reduction, and transmission line loading improvement.

This research formulates and implements a novel closed-loop optimal control system that drives a train between two stations in an optimal time, energy efficient, or mixed objective manner. The optimal controller uses sensor feedback from the train and in real-time computes the most efficient control decision for the train to follow given knowledge of the track profile ahead of the train, speed restrictions and required arrival time windows. The control problem is solved both on an open track and while safely driving no closer than a fixed distance behind another locomotive. In contrast to other research in the field, this thesis achieves a real-time capable and embeddable closed-loop optimization with advanced modeling and numerical solving techniques with a non-linear optimal control problem. This controller is first formulated as a non-convex control problem and then converted to an advanced convex second-order cone problem with the intent of using a simple numerical solver, ensuring global optimality, and improving control robustness. Convex and non-convex numerical methods of solving the control problem are investigated and closed-loop performance results with a simulated vehicle are presented under realistic modeling conditions on advanced tracks both on desktop and embedded computer architectures. It is observed that the controller is capable of robust vehicle driving in cases both with and without modeling uncertainty. The benefits of pairing the optimal controller with a parameter estimator are demonstrated for cases where very large mismatches exists between the controller model and the simulated vehicle. Stopping performance is consistently within 25cm of target stations, and the worst case closed-loop optimization time was within 100ms for the computation of a 1000 point control horizon on an i7-6700 machine. / Thesis / Master of Applied Science (MASc) / This research formulates and implements a novel closed-loop optimal control system that drives a train between two stations in an optimal time, energy efficient, or mixed objective manner. It is deployed on a commuter vehicle and directly manages the motoring and braking systems. The optimal controller uses sensor feedback from the train and in real-time computes the most efficient control decision for the train to follow given knowledge of the track profile ahead of the train, speed restrictions and required arrival time windows. The final control implementation is capable of safe, high accuracy and optimal driving all while computing fast enough to reliably deploy on a rail vehicle.