Unmanned Aerial Vehicles (UAV) control has become a very important point of scientific study. The control design challenges of a UAV make it one of the most researched areas in modern control applications. This thesis specifically chooses the Quadrotor as the UAV platform. Considering the quadrotor has 4 rotors and 6 degrees of freedom, it is an underactuated system and is dynamically unstable that has to be stabilized by a suitable control algorithm in order to operate autonomously. This thesis focuses on the quaternion representation of the quadrotor system dynamics and develops an adaptive control for its trajectory tracking problem. The control design uses the certainty equivalence principle where adaptive tracking controls are designed separately for each of the translational and rotational subsystems. With this approach, the success of the outer loop translational control relies on the fast convergence of the inner loop rotational control in order to guarantee the system’s stability while achieving the tracking objective. For the translational subsystem in the outer loop, a modified geometric control technique is considered with an adaptive component for the estimation of the uncertain mass of the quadrotor. For the rotational subsystem in the inner loop a backstepping based control design is adopted due to its systematic design and intuitive approach. An adaptive component is further integrated with it to estimate the integrated components of the uncertain Moment of Inertia matrix and other constant parameters in the system dynamics to guarantee the stability of the inner loop system while achieving the tracking objective. Furthermore, a complete backstepping control design methodology is presented which overcomes the issues of certainty equivalence principle where the inner loop needs to execute significantly faster than the outer loop to stabilize the system.

The modern day power grid is a highly complex system; as such, maintaining stable operations of the grid relies on many factors. Additionally, the increased usage of renewable energy sources significantly complicates matters. Attempts to assess the current stability of the grid make use of several key parameters, however obtaining these parameters to make an assessment has its own challenges. Due to the limited number of measurements and the unavailability of information, it is often difficult to accurately know the current value of these parameters needed for stability assessment. This work attempts to estimate three of these parameters: the Inertia, Topology, and Voltage Phasors. Without these parameters, it is no longer possible to determine the current stability of the grid. Through the use of machine learning, empirical studies, and mathematical optimization it is possible to estimate these three parameters when previously this was not the case. These three methodologies perform estimations through measurement-based approaches. This allows for the obtaining of these parameters without required system knowledge, while improving results when systems information is known. / Ph. D.

The radical changes that are currently occurring in the nature of power systems means that in the future it may no longer be possible to guarantee security of supply using offline security assessment and planning. The increased uncertainty, particularly the reduction and variation in system inertia that will be faced in the future must be overcome through the use of adaptive online solutions for ensuring system security. The introduction of synchronised measurement technology means that the wide area real time measurements that are necessary to implement these online actions are now available.The objective of the research presented in this thesis was to create methods for predicting the post-disturbance frequency behaviour of a power system with the intent of contributing to the development of real time adaptive corrective control for future power systems. Such a prediction method would generate an online prediction based on wide area measurements of frequency and active power that are recorded within the period of approximately one second after a disturbance to the active power balance of the system. Predictions would allow frequency control to respond more quickly and efficiently as it would no longer be necessary to wait for the system frequency behaviour to violate pre-determined thresholds.The research presented in this thesis includes the creation of an online method for the simultaneous detection of the time at which a disturbance occurred in a power system, or area of a power system, and the estimation of the inertia of that system, or area. An existing prediction method based on approximate models has been redesigned to eliminate its dependence on offline information. Furthermore, the thesis presents the novel application of pattern classification theory to frequency prediction and a five class example of pattern classification is implemented.

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