There is an increasing interest in automated mobile equipment in the construction, agriculture and mining industries to improve productivity, efficiency and operator safety. In general, these machines belong to a class of mobile vehicles with a tool for manipulating its environment to accomplish a repetitive task. Forces and motions are inherently coupled between the tool (<em>e.g.</em> bucket or blade) and the means of vehicle propulsion (<em>e.g</em>. wheels or tracks). Furthermore, they are often operated within uncertain and unstructured environments. A particularly challenging case involves the use of a bulldozer for the removal of excavated material. Modeling and control of mobile robots that interact forcibly with their environment, such as robotic excavation machinery, is a challenging problem that has not been adequately addressed in prior research. This thesis investigates the low-level modeling and control of a 3-DOF robotic bulldozing operation. Motivated by a bulldozing process in an underground mining application, a theoretical nonlinear hybrid dynamic model was developed. The model includes discrete operation modes contained within a hybrid dynamic model framework. The dynamics of the individual modes are represented by a set of linear and nonlinear differential equations. An instrumented scaled-down bulldozer and environment were developed to emulate the full scale operation. Model parameter estimation and validation was completed using experimental data from this system. The model was refined based on a global sensitivity analysis. The refined model was found to be suitable for simulation and design of robotic bulldozing control laws. Optimal blade position control laws were designed based on the hybrid dynamic model to maximize the predicted material removal rate of the bulldozing process. A stability analysis of the underlying deterministic closed-loop process dynamics was performed using Lyapunov’s second method. Monte Carlo simulation was used for further performance and stability analysis of the closed-loop process dynamics including stochastic state disturbances and input constraints. Results of the Monte Carlo simulation were also used for tuning the blade position control laws. Experiments were conducted with the scaled-down robotic bulldozing system. The control laws were implemented with various tuning values. As a comparison, a rule-based blade control algorithm was also designed and implemented. The experimental results with the optimal control laws demonstrated a 33% increase in the average material removal rate compared to the rule-based controller. / Doctor of Philosophy (PhD)

Recently, due to the increasing adoption of distributed energy resource (DER) technologies including battery energy storage (BES) and electric vehicle (EV) systems, bidirectional power converters are becoming more popular. These converters are broadly utilized as interface devices and provide a bidirectional power flow in applications where the primary power supply can both supply and receive energy. A dc capacitor, called the dc-link, is an important component of such bidirectional converters. For a wide range of applications, the converter is required to control the dc-link voltage. Commonly, a proportional-integrating (PI) controller is used by the dc capacitor voltage controller to generate a set-point for the inner current controller. This approach tightly regulates the dc-link voltage to a given value. The research presented in this dissertation shows that such an approach compromises the stability margins of the converter for reverse power flow and weak grid conditions. It is shown that by allowing a small variation of dc capacitor voltage in proportion to the amount of power flowing through the converter, the stability and robustness margins are improved. This approach also simplifies the design process and can be applied to both dc/dc and dc/ac (single-phase and three-phase) converters. Moreover, it grants an inherent power sharing capability when multiple converters share the same dc-link terminals; removing the need to a communication link between parallel converters. The proposed controller is equipped with a current limiting mechanism to protect the converter during low-voltage/over-current transients. Detailed analyses, simulations, comparisons, and experimental results are included to illustrate the effectiveness of the proposed control approach. To mathematically establish the properties of the proposed method in a single-phase dc/ac application, this dissertation also derives a new and systematic modeling approach for a grid-connected bidirectional single-phase inverter controlled in stationary frame. Implementing the control system in the stationary frame has advantages over rotating frame. However, the combination of dc and ac state variables and nonlinearities make its stability analysis challenging. In the proposed model, an imaginary subsystem is properly generated and augmented to allow a full transformation to a synchronous rotating frame. The proposed modeling strategy is modular and has a closed form which facilitates further extensions. It is successfully used to demonstrate enhanced stability margins of the proposed controller.

In this work, the data analysis of oscillating flapping fins is conducted for mathematical model. Data points of heave and surge force obtained by the CFD (Computational Fluid Dynamics) for different geometrical kinds of flapping fins. The fin undergoes a combination of vertical and angular oscillatory motion, while travelling at constant forward speed. The surge thrust and heave lift are generated by the combined motion of the flapping fins, especially due to the carrier vehicle’s heave and pitch motion will be investigated to acquire system identification with CFD data available while the fin pitching motion is selected as a function of fin vertical motion and it is imposed by an external mechanism. The data series applied to model unsteady lifting flow around the system will be employed to develop an optimization algorithm to establish an approximation transfer function model for heave force and obtain a predicting black box system with nonlinear theory for surge force with fin motion control synthesis.

An anguilliform swimming robot replicating an idealized motion is a complex marine vehicle necessitating both a theoretical and experimental analysis to completely understand its propulsion characteristics. The ideal anguilliform motion within is theorized to produce ``wakeless'' swimming (Vorus, 2011), a reactive swimming technique that produces thrust by accelerations of the added mass in the vicinity of the body. The net circulation for the unsteady motion is theorized to be eliminated. The robot was designed to replicate the desired, theoretical motion by applying control theory methods. Independent joint control was used due to hardware limitations. The fluid velocity vectors in the propulsive wake downstream of the tethered, swimming robot were measured using Particle Image Velocimetry (PIV). Simultaneously, a load cell measured the thrust (or drag) forces of the robot via a hydrodynamic tether. The measured field velocities and thrust forces were compared to the theoretical predictions for each. The desired, ideal motion was not replicated consistently during PIV testing, producing off-design scenarios. The thrust-computing method for the ideal motion was applied to the actual, recorded motion and compared to the load cell results. The theoretical field velocities were computed differently by accounting for shed vortices due to a different shape than ideal. The theoretical thrust shows trends similar to the measured thrust over time. Similarly promising comparisons are found between the theoretical and measured flow-field velocities with respect to qualitative trends and velocity magnitudes. The initial thrust coefficient prediction was deemed insufficient, and a new one was determined from an iterative process. The off-design cases shed flow structures into the downstream wake of the robot. The first is a residual disturbance of the shed boundary layer, which is to be expected for the ideal case, and dissipates within one motion cycle. The second are larger-order vortices that are being shed at two distinct times during a half-cycle. These qualitative and quantitative comparisons were used to confirm the possibility of the original hypothesis of ``wakeless'' swimming. While the ideal motion could not be tested consistently, the results of the off-design cases agree significantly with the adjusted theoretical computations. This shows that the boundary conditions derived from slender-body constraints and the assumptions of ideal flow theory are sufficient enough to predict the propulsion characteristics of an anguilliform robot undergoing this specific motion.

The focus of this thesis is to explore the automation technology used in the modern entertainment industry. Upon completion of my thesis, I will deliver a working prototype of the chosen technology and present its capabilities in a choreographed show.

This thesis presents a real-time autonomous guidance and control method for a quadrotor in a GPS-denied environment. The quadrotor autonomously seeks a destination while it avoids obstacles whose shape and position are initially unknown. We implement the obstacle avoidance and destination seeking methods using off-the-shelf sensors, including a vision-sensing camera. The vision-sensing camera detects the positions of points on the surface of obstacles. We use this obstacle position data and a potential-field method to generate velocity commands. We present a backstepping controller that uses the velocity commands to generate the quadrotor's control inputs. In indoor experiments, we demonstrate that the guidance and control methods provide the quadrotor with sufficient autonomy to fly point to point, while avoiding obstacles.

Mathematical models are used in engineering and the sciences to estimate properties of systems of interest, increasing our understanding of the surrounding world and driving technological innovation. Unfortunately, as the systems of interest grow in complexity, so to do the models necessary to accurately describe them. Analytic solutions for problems with such models are provably intractable, motivating the use of approximate yet still accurate estimation techniques. Particle filtering methods have emerged as a popular tool in the presence of such models, spreading from its origins in signal processing to a diverse set of fields throughout engineering and the sciences including medical research, economics, robotics, and geophysics. In groundwater hydrology, a key component of aquifer assessment is the determination of the properties which permit water resource managers to estimate aquifer drawdown and safe yield. Presented is a particle filtering approach to estimate aquifer properties from transient data sets, leveraging recently published analytically-derived models for confined aquifers. The approach is examined experimentally through validation against three common aquifer testing problems: determination of (i) transmissivity and storage coefficient from non-leaky confined aquifer performance tests, (ii) transmissivity, storage coefficient, and vertical hydraulic conductivity of a confining unit from leaky confined aquifer performance tests, and (iii) transmissivity and storage coefficient from non-leaky confined aquifer performance tests with noisy data and boundary effects. The first two problems are well-addressed and the presented approach compares favorably to the results obtained from other published methods. The third problem, which the presented method can tackle more naturally than previously-published methods, underscores the flexibility of particle filtering and, in turn, the promise such methods offer for a myriad of other geoscience problems

Omni-directional (O-D) infrared (IR) vision is an effective capability for mobile systems in robotics, due to its advantages: illumination invariance, wide field-of-view, ease of identifying heat-emitting objects, and long term tracking without interruption. Unfortunately, O-D IR sensors have low resolution, low frame rates, high cost, sensor noise, and an increase in tracking time. In order to overcome these disadvantages, we propose an autonomous system application in indoor scenarios including 1) Dynamic 3D Reconstruction (D3DR) of the target view in real time images, 2) Human Behavior-based Target Tracking from O-D thermal images, 3) Thermal Multisensor Fusion (TMF), and 4) Visual Perception for Social Cognition from the motion behavior of the human target.

The focus of this thesis is the development and implementation of a neural network model predictive controller to be used for controlling the integrated recycle heat exchanger (Intrex) in a 300MW circulating fluidized bed (CFB) boiler. Discussion of the development of the controller will include data collection and preprocessing, controller design and controller tuning. The controller will be programmed directly into the plant distributed control system (DCS) and does not require the continuous use of any third party software. The intrexes serve as the loop seal in the CFB as well as intermediate and finishing superheaters. Heat is transferred to the steam in the intrex superheaters from the circulating ash which can vary in consistency, quantity and quality. Fuel composition can have a large impact on the ash quality and in turn, on intrex performance. Variations in MW load and airflow settings will also impact intrex performance due to their impact on the quantity of ash circulating in the CFB. Insufficient intrex heat transfer will result in low main steam temperature while excessive heat transfer will result in high superheat attemperator sprays and/or loss of unit efficiency. This controller will automatically adjust to optimize intrex ash flow to compensate for changes in the other ash properties by controlling intrex air flows. The controller will allow the operator to enter a target intrex steam temperature increase which will cause all of the intrex air flows to adjust simultaneously to achieve the target temperature. The result will be stable main steam temperature and in turn stable and reliable operation of the CFB.

This thesis presents a new mechanical design for an exoskeleton actuator to power the sagittal plane motion in the human hip. The device uses a DC motor to drive a Scotch yoke mechanism and series elasticity to take advantage of the cyclic nature of human gait and to reduce the maximum power and control requirements of the exoskeleton. The Scotch yoke actuator creates a position-dependent transmission that varies between 4:1 and infinity, with the peak transmission ratio aligned to the peak torque periods of the human gait cycle. Simulation results show that both the peak and average motor torque can be reduced using this mechanism, potentially allowing a less powerful motor to be used. Furthermore, the motor never needs to reverse direction even when the hip joint does. Preliminary testing shows the exoskeleton can provide an assistive torque and is capable of accurate position tracking at speeds covering the range of human walking. This thesis provides a detailed analysis of how the dynamic nature of human walking can be leveraged, how the hip actuator was designed, and shows how the exoskeleton performed during preliminary human trials.