VTOL drones were designed and built at the beginning of the 20th century for military applications due to easy take-off and landing operations. Many companies like Lockheed, Convair, NASA and Bell Labs built their own aircrafts but only a few from them came in to the market. Usually, flight automation starts from first principles modeling which helps in the controller design and dynamic analysis of the system.

In this project, a VTOL drone with a shape similar to a Convair XFY-1 is studied and the primary focus is stabilizing and controlling the flight path of the drone in
its hover and horizontal flying modes. The model of the plane is obtained using first principles modeling and controllers are designed to stabilize the yaw, pitch and roll rotational motions.

The plane is modeled for its yaw, pitch and roll rotational motions. Subsequently, the rotational dynamics of the system are linearized about the hover flying mode, hover to horizontal flying mode, horizontal flying mode, horizontal to hover flying mode for ease of implementation of linear control design techniques. The controllers are designed based on an H∞ loop shaping procedure and the results are verified on the actual nonlinear model for the stability of the closed loop system about hover flying, hover to horizontal transition flying, horizontal flying, horizontal to hover transition flying. An experiment is conducted to study the dynamics of the motor by recording the PWM input to the electronic speed controller as input and the rotational speed of the motor as output. A theoretical study is also done to study the thrust generated by the propellers for lift, slipstream velocity analysis, torques acting on the system for various thrust profiles.

Fully distributed wireless sensor networks (WSNs) without fusion center have advantages such as scalability in network size and energy efficiency in communications. Each sensor shares its data only with neighbors and then achieves global consensus quantities by in-network processing. This dissertation considers robust distributed parameter estimation methods, seeking global consensus on parameters of adaptive learning algorithms and statistical quantities.

Diffusion adaptation strategy with nonlinear transmission is proposed. The nonlinearity was motivated by the necessity for bounded transmit power, as sensors need to iteratively communicate each other energy-efficiently. Despite the nonlinearity, it is shown that the algorithm performs close to the linear case with the added advantage of power savings. This dissertation also discusses convergence properties of the algorithm in the mean and the mean-square sense.

Often, average is used to measure central tendency of sensed data over a network. When there are outliers in the data, however, average can be highly biased. Alternative choices of robust metrics against outliers are median, mode, and trimmed mean. Quantiles generalize the median, and they also can be used for trimmed mean. Consensus-based distributed quantile estimation algorithm is proposed and applied for finding trimmed-mean, median, maximum or minimum values, and identification of outliers through simulation. It is shown that the estimated quantities are asymptotically unbiased and converges toward the sample quantile in the mean-square sense. Step-size sequences with proper decay rates are also discussed for convergence analysis.

Another measure of central tendency is a mode which represents the most probable value and also be robust to outliers and other contaminations in data. The proposed distributed mode estimation algorithm achieves a global mode by recursively shifting conditional mean of the measurement data until it converges to stationary points of estimated density function. It is also possible to estimate the mode by utilizing grid vector as well as kernel density estimator. The densities are estimated at each grid point, while the points are updated until they converge to a global mode.

In recent years, wide bandgap (WBG) devices enable power converters with higher power density and higher efficiency. On the other hand, smart grid technologies are getting mature due to new battery technology and computer technology. In the near future, the two technologies will form the next generation of smart grid enabled by WBG devices. This dissertation deals with two applications: silicon carbide (SiC) device used for medium voltage level interface (7.2 kV to 240 V) and gallium nitride (GaN) device used for low voltage level interface (240 V/120 V). A 20 kW solid state transformer (SST) is designed with 6 kHz switching frequency SiC rectifier. Then three robust control design methods are proposed for each of its smart grid operation modes. In grid connected mode, a new LCL filter design method is proposed considering grid voltage THD, grid current THD and current regulation loop robust stability with respect to the grid impedance change. In grid islanded mode, µ synthesis method combined with variable structure control is used to design a robust controller for grid voltage regulation. For grid emergency mode, multivariable controller designed using H infinity synthesis method is proposed for accurate power sharing. Controller-hardware-in-the-loop (CHIL) testbed considering 7-SST system is setup with Real Time Digital Simulator (RTDS). The real TMS320F28335 DSP and Spartan 6 FPGA control board is used to interface a switching model SST in RTDS. And the proposed control methods are tested. For low voltage level application, a 3.3 kW smart grid hardware is built with 3 GaN inverters. The inverters are designed with the GaN device characterized using the proposed multi-function double pulse tester. The inverter is controlled by onboard TMS320F28379D dual core DSP with 200 kHz sampling frequency. Each inverter is tested to process 2.2 kW power with overall efficiency of 96.5 % at room temperature. The smart grid monitor system and fault interrupt devices (FID) based on Arduino Mega2560 are built and tested. The smart grid cooperates with GaN inverters through CAN bus communication. At last, the three GaN inverters smart grid achieved the function of grid connected to islanded mode smooth transition

The purpose of this dissertation is to develop a design technique for fractional PID controllers to achieve a closed loop sensitivity bandwidth approximately equal to a desired bandwidth using frequency loop shaping techniques. This dissertation analyzes the effect of the order of a fractional integrator which is used as a target on loop shaping, on stability and performance robustness. A comparison between classical PID controllers and fractional PID controllers is presented. Case studies where fractional PID controllers have an advantage over classical PID controllers are discussed. A frequency-domain loop shaping algorithm is developed, extending past results from classical PID’s that have been successful in tuning controllers for a variety of practical systems.

From time immemorial, epilepsy has persisted to be one of the greatest impediments to human life for those stricken by it. As the fourth most common neurological disorder, epilepsy causes paroxysmal electrical discharges in the brain that manifest as seizures. Seizures have the effect of debilitating patients on a physical and psychological level. Although not lethal by themselves, they can bring about total disruption in consciousness which can, in hazardous conditions, lead to fatality. Roughly 1\% of the world population suffer from epilepsy and another 30 to 50 new cases per 100,000 increase the number of affected annually. Controlling seizures in epileptic patients has therefore become a great medical and, in recent years, engineering challenge.



In this study, the conditions of human seizures are recreated in an animal model of temporal lobe epilepsy. The rodents used in this study are chemically induced to become chronically epileptic. Their Electroencephalogram (EEG) data is then recorded and analyzed to detect and predict seizures; with the ultimate goal being the control and complete suppression of seizures.



Two methods, the maximum Lyapunov exponent and the Generalized Partial Directed Coherence (GPDC), are applied on EEG data to extract meaningful information. Their effectiveness have been reported in the literature for the purpose of prediction of seizures and seizure focus localization. This study integrates these measures, through some modifications, to robustly detect seizures and separately find precursors to them and in consequence provide stimulation to the epileptic brain of rats in order to suppress seizures. Additionally open-loop stimulation with biphasic currents of various pairs of sites in differing lengths of time have helped us create control efficacy maps. While GPDC tells us about the possible location of the focus, control efficacy maps tells us how effective stimulating a certain pair of sites will be.



The results from computations performed on the data are presented and the feasibility of the control problem is discussed. The results show a new reliable means of seizure detection even in the presence of artifacts in the data. The seizure precursors provide a means of prediction, in the order of tens of minutes, prior to seizures. Closed loop stimulation experiments based on these precursors and control efficacy maps on the epileptic animals show a maximum reduction of seizure frequency by 24.26\% in one animal and reduction of length of seizures by 51.77\% in another. Thus, through this study it was shown that the implementation of the methods can ameliorate seizures in an epileptic patient. It is expected that the new knowledge and experimental techniques will provide a guide for future research in an effort to ultimately eliminate seizures in epileptic patients.

For a sensor array, part of its elements may fail to work due to hardware failures. Then the missing data may distort in the beam pattern or decrease the accuracy of direction-of-arrival (DOA) estimation. Therefore, considerable research has been conducted to develop algorithms that can estimate the missing signal information. On the other hand, through those algorithms, array elements can also be selectively turned off while the missed information can be successfully recovered, which will save power consumption and hardware cost.

Conventional approaches focusing on array element failures are mainly based on interpolation or sequential learning algorithm. Both of them rely heavily on some prior knowledge such as the information of the failures or a training dataset without missing data. In addition, since most of the existing approaches are developed for DOA estimation, their recovery target is usually the co-variance matrix but not the signal matrix.

In this thesis, a new signal recovery method based on matrix completion (MC) theory is introduced. It aims to directly refill the absent entries in the signal matrix without any prior knowledge. We proposed a novel overlapping reshaping method to satisfy the applying conditions of MC algorithms. Compared to other existing MC based approaches, our proposed method can provide us higher probability of successful recovery. The thesis describes the principle of the algorithms and analyzes the performance of this method. A few application examples with simulation results are also provided.

Buck converters are electronic devices that changes a voltage from one level to a lower one and are present in many everyday applications. However, due to factors like aging, degradation or failures, these devices require a system identification process to track and diagnose their parameters. The system identification process should be performed on-line to not affect the normal operation of the device. Identifying the parameters of the system is essential to design and tune an adaptive proportional-integral-derivative (PID) controller.

Three techniques were used to design the PID controller. Phase and gain margin still prevails as one of the easiest methods to design controllers. Pole-zero cancellation is another technique which is based on pole-placement. However, although these controllers can be easily designed, they did not provide the best response compared to the Frequency Loop Shaping (FLS) technique. Therefore, since FLS showed to have a better frequency and time responses compared to the other two controllers, it was selected to perform the adaptation of the system.

An on-line system identification process was performed for the buck converter using indirect adaptation and the least square algorithm. The estimation error and the parameter error were computed to determine the rate of convergence of the system. The indirect adaptation required about 2000 points to converge to the true parameters prior designing the controller. These results were compared to the adaptation executed using robust stability condition (RSC) and a switching controller. Two different scenarios were studied consisting of five plants that defined the percentage of deterioration of the capacitor and inductor within the buck converter. The switching logic did not always select the optimal controller for the first scenario because the frequency response of the different plants was not significantly different. However, the second scenario consisted of plants with more noticeable different frequency responses and the switching logic selected the optimal controller all the time in about 500 points. Additionally, a disturbance was introduced at the plant input to observe its effect in the switching controller. However, for reasonable low disturbances no change was detected in the proper selection of controllers.

In many applications, measured sensor data is meaningful only when the location of sensors is accurately known. Therefore, the localization accuracy is crucial. In this dissertation, both location estimation and location detection problems are considered.

In location estimation problems, sensor nodes at known locations, called anchors, transmit signals to sensor nodes at unknown locations, called nodes, and use these transmissions to estimate the location of the nodes. Specifically, the location estimation in the presence of fading channels using time of arrival (TOA) measurements with narrowband communication signals is considered. Meanwhile, the Cramer-Rao lower bound (CRLB) for localization error under different assumptions is derived. Also, maximum likelihood estimators (MLEs) under these assumptions are derived.

In large WSNs, distributed location estimation algorithms are more efficient than centralized algorithms. A sequential localization scheme, which is one of distributed location estimation algorithms, is considered. Also, different localization methods, such as TOA, received signal strength (RSS), time difference of arrival (TDOA), direction of arrival (DOA), and large aperture array (LAA) are compared under different signal-to-noise ratio (SNR) conditions. Simulation results show that DOA is the preferred scheme at the low SNR regime and the LAA localization algorithm provides better performance for network discovery at high SNRs. Meanwhile, the CRLB for the localization error using the TOA method is also derived.

A distributed location detection scheme, which allows each anchor to make a decision as to whether a node is active or not is proposed. Once an anchor makes a decision, a bit is transmitted to a fusion center (FC). The fusion center combines all the decisions and uses a design parameter $K$ to make the final decision. Three scenarios are considered in this dissertation. Firstly, location detection at a known location is considered. Secondly, detecting a node in a known region is considered. Thirdly, location detection in the presence of fading is considered. The optimal thresholds are derived and the total probability of false alarm and detection under different scenarios are derived.

Vertical taking off and landing (VTOL) drones started to emerge at the beginning of this century, and finds applications in the vast areas of mapping, rescuing, logistics, etc. Usually a VTOL drone control system design starts from a first principles model. Most of the VTOL drones are in the shape of a quad-rotor which is convenient for dynamic analysis.

In this project, a VTOL drone with shape similar to a Convair XFY-1 is studied and the primary focus is developing and examining an alternative method to identify a system model from the input and output data, with which it is possible to estimate system parameters and compute model uncertainties on discontinuous data sets. We verify the models by designing controllers that stabilize the yaw, pitch, and roll angles for the VTOL drone in the hovering state.

This project comprises of three stages: an open-loop identification to identify the yaw and pitch dynamics, an intermediate closed-loop identification to identify the roll action dynamic and a closed-loop identification to refine the identification of yaw and pitch action. In open and closed loop identifications, the reference signals sent to the servos were recorded as inputs to the system and the angles and angular velocities in yaw and pitch directions read by inertial measurement unit were recorded as outputs of the system. In the intermediate closed loop identification, the difference between the reference signals sent to the motors on the contra-rotators was recorded as input and the roll angular velocity is recorded as output. Next, regressors were formed by using a coprime factor structure and then parameters of the system were estimated using the least square method. Multiplicative and divisive uncertainties were calculated from the data set and were used to guide PID loop-shaping controller design.

This thesis addresses the design and control of three phase inverters. Such inverters are

used to produce three-phase sinusoidal voltages and currents from a DC source. They

are critical for injecting power from renewable energy sources into the grid. This is

especially true since many of these sources of energy are DC sources (e.g. solar

photovoltaic) or need to be stored in DC batteries because they are intermittent (e.g. wind

and solar). Two classes of inverters are examined in this thesis. A control-centric design

procedure is presented for each class. The first class of inverters is simple in that they

consist of three decoupled subsystems. Such inverters are characterized by no mutual

inductance between the three phases. As such, no multivariable coupling is present and

decentralized single-input single-output (SISO) control theory suffices to generate

acceptable control designs. For this class of inverters several families of controllers are

addressed in order to examine command following as well as input disturbance and noise

attenuation specifications. The goal here is to illuminate fundamental tradeoffs. Such

tradeoffs include an improvement in the in-band command following and output

disturbance attenuation versus a deterioration in out-of-band noise attenuation.

A fundamental deficiency associated with such inverters is their large size. This can be

remedied by designing a smaller core. This naturally leads to the second class of inverters

considered in this work. These inverters are characterized by significant mutual

inductances and multivariable coupling. As such, SISO control theory is generally not

adequate and multiple-input multiple-output (MIMO) theory becomes essential for

controlling these inverters.