Control methodologies for deterministic disturbance rejection and trajectory tracking have been of great interest to researchers in the fields of controls, mechatronics, robotics and signal processing in the past two decades. The applications of these methods span a wide range from satellite attitude control requiring an accuracy of a few meters, to positioning of the read/write head in hard disk drives with an accuracy of less than one nanometer. This dissertation addresses the problem of trajectory tracking and deterministic disturbance rejection in discrete time systems when the trajectory/disturbance is unknown, but can be realized as an a ne combination of known basis functions. Despite the prior work on this problem that assumes known and time invariant plant dynamics, we consider multi input single output systems with unknown dynamics. Moreover, we investigate the cases where the disturbance or system dynamics varies slowly or abruptly but infrequently. Within the broad class of disturbances/trajectories that satisfy the stated criteria, an elaborate study is conducted on periodic and superposition of multiple sinusoidal sequences. We propose two novel adaptive control methods for the aforementioned problem. The first scheme can be classified as an indirect adaptive algorithm since it consists of two parts, namely a system identification mechanism that provides a dynamic model of the closed loop system, and the adaptive compensator which deploys the aforementioned dynamic model to synthesize the control law. The second proposed method is a direct adaptive controller, meaning that the control law is generated directly and the stated separation is not possible.

Besides providing theoretical guarantees, we experimentally evaluate our algorithms on a challenging control task for nano-scale positioning of the read write head in a dual stage hard disk drive (HDD). Even with the advent of NAND ash based data storage devices, the HDD continues to thrive as the most cost effective, reliable solution for rewritable, very high density data storage. It remains a key technology particularly with the tremendously growing popularity of server based cloud computing and novel hybrid enterprise storage solutions. We described that the control methodologies that can address the problem under our study are crucial for Bit Patterned Media Recording which is one of the two breakthroughs in magnetic recording that have been immensely investigated in the past few years. Extensive computer simulations and implementation on a digital signal processor unit are performed to validate the effectiveness of our proposed algorithms in full spectrum compensation of the repeatable runout in dual stage HDDs. This application introduces unique control challenges since it requires estimating a very large number of parameters that is order(s) of magnitude greater than prior work and frequency contents span from 120Hz to extremely large values (above 20KHz) where the plant dynamic uncertainties are large.

The recent development of distribution-level phasor measurement units, a.k.a. micro-PMUs, has been an important step towards achieving situational awareness in power distribution networks. The challenge however is to transform the large amount of data that is generated by micro-PMUs to actionable information and then match the information to use-cases with practical value to system operators. This open problem is addressed in this

thesis. First, we introduce novel data-driven event detection techniques to pick out valuable portion of data from extremely large raw micro-PMU data. Subsequently, data-driven event classifier are developed to effectively classify power quality events. Importantly, we use field expert knowledge and utility records to conduct an extensive data-driven event labeling. Moreover, certain aspects from event detection analysis are adopted as additional

features to be fed into the classifier model. In this regard, a multi-class support vector machine (multi-SVM) classifier is trained and tested over 15 days of real-world data from two micro-PMUs on a distribution feeder in Riverside, CA. In total, we analyze 1.2 billion measurement points, and 10,700 events. The effectiveness of the developed event classifier is compared with prevalent multi-class classification methods, including k-nearest neighbor method as well as decision-tree method. Importantly, five real-world use-cases are presented for the proposed data analytics tools, including:

Transient Load Modeling for Application in Frequency Regulation Market;

Static Load Modeling;

Remote Asset Monitoring;

Protection System Diagnosis;

Lightning Initiated Contingency Analysis.

A longstanding grand-challenge problem in AI is to build machines that are able to think and interact like humans do. A specific embodiment of this problem is a generalization of the cocktail party problem encountered in signal processing and blind signal separation: If an AI agent were to drop in at a crowded cocktail party then can it separate out and reconstruct the different underlying stories and narratives being discussed from a mixture of fragments of all the on-going conversations. Such a problem has taken on a renewed urgency: Narratives play a defining role in influencing critical decisions and worldviews of both the society at large and individuals, but the continual emergence of a multitude of conflicting narratives –enabled by large-scale adoption of social media– has created a global emergency, where the basic tenets of civil society and governance are being increasingly compromised. These narratives, some of which can be labeled as conspiracy theories, are composed of numerous characters connected by semantically diverse relationships situated in multiple and overlapping contexts. Injecting false facts happens in the context of such discussions, and solving such a misinformation problem is beyond a supervised classification task in natural language processing (NLP). In this dissertation, we develop a pipeline of interlocking computational and statistical modules - based on NLP tools and complex network theories– to extract meaningful narrative networks by distilling millions of social media posts. We develop a framework for semantic parsing of such narrative graphs (e.g. who are outsiders, their motivations and threats, and strategies of insiders) and evaluate the quality of these automatically derived communities in different ways. This framework takes a step towards enabling human-smart AI, where it can view the world as a human would.