Engineering Commons^™

Investigation And Control Of Görtler Vortices In High-Speed Flows, Omar Es-Sahli

Theses and Dissertations

High-amplitude freestream turbulence and surface roughness elements can excite a laminar boundary-layer flow sufficiently enough to cause streamwise-oriented vortices to develop. These vortices resemble elongated streaks having alternate spanwise variations of the streamwise velocity. Following the transient growth phase, the fully developed vortex structures downstream undergo an inviscid secondary instability mechanism and, ultimately, transition to turbulence. This mechanism becomes much more complicated in high-speed boundary layer flows due to compressibility and thermal effects, which become more significant for higher Mach numbers. In this research, we formulate and test an optimal control algorithm to suppress the growth rate of the aforementioned …

Stability Of Deep Neural Networks For Feedback-Optimal Pinpoint Landings, Omkar S. Mulekar, Hancheol Cho, Riccardo Bevilacqua

Student Works

The ability to certify systems driven by neural networks is crucial for future rollouts of machine learning technologies in aerospace applications. In this study, the neural networks are used to represent a fuel-optimal feedback controller for two different 3-degree-of-freedom pinpoint landing problems. It is shown that the standard sum-ofsquares Lyapunov candidate is too restrictive to assess the stability of systems with fuel-optimal control profiles. Instead, a parametric Lyapunov candidate (i.e. a neural network) can be trained to sufficiently evaluate the closed-loop stability of fuel-optimal control profiles. Then, a stability-constrained imitation learning method is applied, which simultaneously trains a neural network …

Intelligent Traffic Control With Connected And Automated Vehicles, Yang Shi

Doctoral Dissertations

The recent advancements in communication technology, transportation infrastructure, computational techniques, and artificial intelligence are driving a revolution in future transportation systems. Connected and Automated Vehicles (CAVs) are attracting a lot of attention due to their potential to reduce traffic accidents, ease congestion, and improve traffic efficiency. This study focuses on addressing the challenges in controlling future CAV-enabled transportation systems. The aim is to develop a framework for the control of CAV-based traffic systems to improve roadway safety, travel efficiency, and energy efficiency. The study proposes new methods for vehicle speed control and traffic signal control at signalized intersections and corridors …

Quadcopter Control Using Single Network Adaptive Critics, Alberto Velazquez, Lei Xu, Tohid Sardarmehni

Mechanical Engineering Faculty Publications and Presentations

In this paper, optimal tracking control is found for an inputaffine nonlinear quadcopter using Single Network Adaptive Critics (SNAC). The quadcopter dynamics consists of twelve states and four controls. The states are defined using two related reference frames: the earth frame, which describes the position and angles, and the body frame, which describes the linear and angular velocities. The quadcopter has six outputs and four controls, so it is an underactuated nonlinear system. The optimal control for the system is derived by solving a discrete-time recursive Hamilton-Jacobi-Bellman equation using a linear in-parameter neural network. The neural network is trained to …

Modeling, Estimation, And Optimal Control Of Lithium-Ion Battery At Cell And System Level, Hamidreza Mirzaei

All Dissertations

Lithium-ion batteries (LIBs) have been regarded as a crucial technology for electrifying a variety of applications, ranging from powering computers, phones, and hybrid electric vehicles (HEVs) to being a critical part of the modern centralized and distributed power grids. Battery systems performance is governed by embedded battery management systems (BMSs). The BMS includes battery state estimation algorithms and control rules, and it acts as the brain of battery-powered systems. The purpose of this research is to address unresolved difficulties associated with control algorithms in BMSs at all levels, from single battery cells to battery packs in hybrid electric vehicles (HEVs).\\ …

Model-Based Design Of An Optimal Lqg Regulator For A Piezoelectric Actuated Smart Structure Using A High-Precision Laser Interferometry Measurement System, Grant P. Gallagher

Master's Theses

Smart structure control systems commonly use piezoceramic sensors or accelerometers as vibration measurement devices. These measurement devices often produce noisy and/or low-precision signals, which makes it difficult to measure small-amplitude vibrations. Laser interferometry devices pose as an alternative high-precision position measurement method, capable of nanometer-scale resolution. The aim of this research is to utilize a model-based design approach to develop and implement a real-time Linear Quadratic Gaussian (LQG) regulator for a piezoelectric actuated smart structure using a high-precision laser interferometry measurement system to suppress the excitation of vibratory modes.

The analytical model of the smart structure is derived using the …

Optimal Sizing And Control Of Hybrid Rocket Vehicles, Srija Ryakam

Doctoral Dissertations and Master's Theses

In the present work, a genetic algorithm is used to optimize a hybrid rocket engine in order to minimize the propellant required for a specific mission. In a hybrid rocket engine, the mass flow rate of the oxidizer can be throttled to enhance the performance of the rocket. First, an analysis of the internal ballistics and the ascent trajectory has been carried out for different mass flow rates of the oxidizer as a function of time, for a fixed amount of oxidizer, in order to study the effect of throttling. Two equivalent problems are considered: in the first problem the …

Optimal Tracking Current Control Of Switched Reluctance Motor Drives Using Reinforcement Q-Learning Scheduling, Hamad Alharkan, Sepehr Saadatmand, Mehdi Ferdowsi, Pourya Shamsi

Electrical and Computer Engineering Faculty Research & Creative Works

In this article, a novel Q-learning scheduling method for the current controller of a switched reluctance motor (SRM) drive is investigated. The Q-learning algorithm is a class of reinforcement learning approaches that can find the best forward-in-time solution of a linear control problem. An augmented system is constructed based on the reference current signal and the SRM model to allow for solving the algebraic Riccati equation of the current-tracking problem. This article introduces a new scheduled-Q-learning algorithm that utilizes a table of Q-cores that lies on the nonlinear surface of an SRM model without involving any information about the model …

Development Of A Robust And Tunable Aircraft Guidance Algorithm, Jacob R. Spangenberg

Browse all Theses and Dissertations

A set of guidance control laws is developed for application to a reduced order dynamic aircraft model. A feedback control formulation utilizing a linear quadratic regulator (LQR) is developed, together with methods for easing the design burden associated with gain tuning. Metrics are developed to assess the stability margin of the controller over the full flight envelope of a notional unmanned aerial vehicle (UAV) model. A feedforward control path is then added to the architecture. The performance of the guidance control laws is assessed through time domain step response metrics as well as through execution of a design mission. The …

Development Of A Robust And Tunable Aircraft Guidance Algorithm, Jacob R. Spangenberg

Browse all Theses and Dissertations

A set of guidance control laws is developed for application to a reduced order dynamic aircraft model. A feedback control formulation utilizing a linear quadratic regulator (LQR) is developed, together with methods for easing the design burden associated with gain tuning. Metrics are developed to assess the stability margin of the controller over the full flight envelope of a notional unmanned aerial vehicle (UAV) model. A feedforward control path is then added to the architecture. The performance of the guidance control laws is assessed through time domain step response metrics as well as through execution of a design mission. The …

Target Control Of Networked Systems, Isaac S. Klickstein

Mechanical Engineering ETDs

The control of complex networks is an emerging field yet it has already garnered interest from across the scientific disciplines, from robotics to sociology. It has quickly been noticed that many of the classical techniques from controls engineering, while applicable, are not as illuminating as they were for single systems of relatively small dimension. Instead, properties borrowed from graph theory provide equivalent but more practical conditions to guarantee controllability, reachability, observability, and other typical properties of interest to the controls engineer when dealing with large networked systems. This manuscript covers three topics investigated in detail by the author: (i) the …

Real-Time Predictive Control Of Connected Vehicle Powertrains For Improved Energy Efficiency, Joseph Oncken

Dissertations, Master's Theses and Master's Reports

The continued push for the reduction of energy consumption across the automotive vehicle fleet has led to widespread adoption of hybrid and plug-in hybrid electric vehicles (PHEV) by auto manufacturers. In addition, connected and automated vehicle (CAV) technologies have seen rapid development in recent years and bring with them the potential to significantly impact vehicle energy consumption. This dissertation studies predictive control methods for PHEV powertrains that are enabled by CAV technologies with the goal of reducing vehicle energy consumption.

First, a real-time predictive powertrain controller for PHEV energy management is developed. This controller utilizes predictions of future vehicle velocity …

Neural Network Predictive Controller For Grid-Connected Virtual Synchronous Generator, Sepehr Saadatmand, Mohamad Saleh Sanjari Nia, Pourya Shamsi, Mehdi Ferdowsi, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, a neural network predictive controller is proposed to regulate the active and the reactive power delivered to the grid generated by a three-phase virtual inertia-based inverter. The concept of the conventional virtual synchronous generator (VSG) is discussed, and it is shown that when the inverter is connected to non-inductive grids, the conventional PI-based VSGs are unable to perform acceptable tracking. The concept of the neural network predictive controller is also discussed to replace the traditional VSGs. This replacement enables inverters to perform in both inductive and non-inductive grids. The simulation results confirm that a well-trained neural network …

Optimal Direct Yaw Moment Control Of A 4wd Electric Vehicle, Winston James Wight

Master's Theses

This thesis is concerned with electronic stability of an all-wheel drive electric vehicle with independent motors mounted in each wheel. The additional controllability and speed permitted using independent motors can be exploited to improve the handling and stability of electric vehicles. In this thesis, these improvements arise from employing a direct yaw moment control (DYC) system that seeks to adapt the understeer gradient of the vehicle and achieve neutral steer by employing a supervisory controller and simultaneously tracking an ideal yaw rate and ideal sideslip angle. DYC enhances vehicle stability by generating a corrective yaw moment realized by a torque …

Utilizing Trajectory Optimization In The Training Of Neural Network Controllers, Nicholas Kimball

Master's Theses

Applying reinforcement learning to control systems enables the use of machine learning to develop elegant and efficient control laws. Coupled with the representational power of neural networks, reinforcement learning algorithms can learn complex policies that can be difficult to emulate using traditional control system design approaches. In this thesis, three different model-free reinforcement learning algorithms, including Monte Carlo Control, REINFORCE with baseline, and Guided Policy Search are compared in simulated, continuous action-space environments. The results show that the Guided Policy Search algorithm is able to learn a desired control policy much faster than the other algorithms. In the inverted pendulum …

Optimal Control Strategies For Complex Biological Systems, Afroza Shirin

Mechanical Engineering ETDs

To better understand and to improve therapies for complex diseases such as cancer or diabetes, it is not sufficient to identify and characterize the interactions between molecules and pathways in complex biological systems, such as cells, tissues, and the human body. It also is necessary to characterize the response of a biological system to externally supplied agents (e.g., drugs, insulin), including a proper scheduling of these drugs, and drug combinations in multi drugs therapies. This obviously becomes important in applications which involve control of physiological processes, such as controlling the number of autophagosome vesicles in a cell, or regulating the …

Verification Of Stochastic Reach-Avoid Using Rkhs Embeddings, Adam J. Thorpe

Electrical and Computer Engineering ETDs

A solution to the terminal-hitting and first-hitting stochastic reach-avoid problem for a Markov control process is presented. This solution takes advantage of a nonparametric representation of the stochastic kernel as a conditional distribution embedding within a reproducing kernel Hilbert space (RKHS). Because the disturbance is modeled as a data-driven stochastic process, this representation avoids intractable integrals in the dynamic recursion of the reach-avoid problem since the expectations can be calculated as an inner product within the RKHS. An example using a high-dimensional chain of integrators is presented, as well as for Clohessy-Wiltshire-Hill (CWH) dynamics.

Sufficient Conditions For Optimal Control Problems With Terminal Constraints And Free Terminal Times With Applications To Aerospace, Sankalp Kishan Bhan

McKelvey School of Engineering Theses & Dissertations

Motivated by the flight control problem of designing control laws for a Ground Collision Avoidance System (GCAS), this thesis formulates sufficient conditions for a strong local minimum for a terminally constrained optimal control problem with a free-terminal time. The conditions develop within the framework of a construction of a field of extremals by means of the method of characteristics, a procedure for the solution of first-order linear partial differential equations, but modified to apply to the Hamilton-Jacobi-Bellman equation of optimal control. Additionally, the thesis constructs these sufficient conditions for optimality with a mathematically rigorous development. The proof uses an approach …

Optimal Control Of Wave Energy Converters, Shangyan Zou

Dissertations, Master's Theses and Master's Reports

In this dissertation, we address the optimal control of the Wave Energy Converters. The Wave Energy Converters introduced in this study can be categorized as the single body heaving device, the single body pitching device, the single body three degrees of freedoms device, and the Wave Energy Converters array. Different types of Wave Energy Converters are modeled mathematically, and different optimal controls are developed for them. The objective of the optimal controllers is to maximize the energy extraction with and without the motion and control constraints. The development of the unconstrained control is first introduced which includes the implementation of …

Extrinsic And Intrinsic Control Of Integrative Processes In Neural Systems, Anirban Nandi

McKelvey School of Engineering Theses & Dissertations

At the simplest dynamical level, neurons can be understood as integrators. That is, neurons accumulate excitation from afferent neurons until, eventually, a threshold is reached and they produce a spike. Here, we consider the control of integrative processes in neural circuits in two contexts. First, we consider the problem of extrinsic neurocontrol, or modulating the spiking activity of neural circuits using stimulation, as is desired in a wide range of neural engineering applications. From a control-theoretic standpoint, such a problem presents several interesting nuances, including discontinuity in the dynamics due to the spiking process, and the technological limitations associated with …

Numerical Methods For Nonlinear Optimal Control Problems And Their Applications In Indoor Climate Control, Runxin He

McKelvey School of Engineering Theses & Dissertations

Efficiency, comfort, and convenience are three major aspects in the design of control systems for residential Heating, Ventilation, and Air Conditioning (HVAC) units. In this dissertation, we study optimization-based algorithms for HVAC control that minimizes energy consumption while maintaining a desired temperature, or even human comfort in a room. Our algorithm uses a Computer Fluid Dynamics (CFD) model, mathematically formulated using Partial Differential Equations (PDEs), to describe the interactions between temperature, pressure, and air flow. Our model allows us to naturally formulate problems such as controlling the temperature of a small region of interest within a room, or to control …

Development Of An Optimal Controller And Validation Test Stand For Fuel Efficient Engine Operation, Jack G. Rehn Iii

Master's Theses (2009 -)

There are numerous motivations for improvements in automotive fuel efficiency. As concerns over the environment grow at a rate unmatched by hybrid and electric automotive technologies, the need for reductions in fuel consumed by current road vehicles has never been more present. Studies have shown that a major cause of poor fuel consumption in automobiles is improper driving behavior, which cannot be mitigated by purely technological means. The emergence of autonomous driving technologies has provided an opportunity to alleviate this inefficiency by removing the necessity of a driver. Before autonomous technology can be relied upon to reduce gasoline consumption on …

Control Oriented Nonlinear Model Reduction For Distributed Parameter Systems, Samir Sahyoun

Doctoral Dissertations

The development of model reduction techniques for physical systems modeled by partial differential equations (PDEs) has been a very active research area. Large number of states is needed to accurately capture the dynamics of such systems which makes them unsuitable for control design. The order of the system must be reduced prior to control design. In this dissertation, new methods that generalize the popular proper orthogonal decomposition (POD) to nonlinear PDEs are investigated. In particular, cluster based POD algorithms are developed and applied to the one and two dimensional Burgers equations that govern a nonlinear convective ow. Each cluster contains …

Optimizing Control Of Shell Eco-Marathon Prototype Vehicle To Minimize Fuel Consumption, Chad Louis Bickel

Master's Theses

Every year the automotive industry strives to increase fuel efficiency in vehicles. When most vehicles are designed, fuel efficiency cannot always come first. The Shell Eco-marathon changes that by challenging students everywhere to develop the most fuel-efficient vehicle possible. There are many different factors that affect fuel efficiency, and different teams focus on different vehicle parameters. Currently, there is no straightforward design tool that can be used to help in Shell Eco-marathon vehicle design. For this reason, it is difficult to optimize every vehicle parameter for maximum fuel efficiency.

In this study, a simulation is developed by using basic vehicle …

An Engage Or Retreat Differential Game With Two Targets, Bikash Shrestha

Browse all Theses and Dissertations

This thesis develops the equilibrium solution for a two-target engage or retreat differential game. In this game, the attacking player is modeled as a massless particle moving with simple motion about an infinite, obstacle-free plane. The opposing player, referred to as the defender, is tasked with the protection of two high-value targets. The mobile attacker must choose to either engage one of the high-value targets or retreat across a predefined boundary. Simultaneously, the defensive player must choose whether to minimize or maximize the attacker's integral utility in an effort to persuade the attacker to choose retreat from certain initial conditions. …

An Optimal Energy Management Strategy For Hybrid Electric Vehicles, Amir Rezaei

Dissertations, Master's Theses and Master's Reports

Hybrid Electric Vehicles (HEVs) are used to overcome the short-range and long charging time problems of purely electric vehicles. HEVs have at least two power sources. Therefore, the Energy Management (EM) strategy for dividing the driver requested power between the available power sources plays an important role in achieving good HEV performance.

This work, proposes a novel real-time EM strategy for HEVs which is named ECMS-CESO. ECMS-CESO is based on the Equivalent Consumption Minimization Strategy (ECMS) and is designed to Catch Energy Saving Opportunities (CESO) while operating the vehicle. ECMS-CESO is an instantaneous optimal controller, i. e., it does not …

Studies Of Uncertainties In Smart Grid: Wind Power Generation And Wide-Area Communication, Can Huang

Doctoral Dissertations

This research work investigates the uncertainties in Smart Grid, with special focus on the uncertain wind power generation in wind energy conversion systems (WECSs) and the uncertain wide-area communication in wide-area measurement systems (WAMSs).

For the uncertain wind power generation in WECSs, a new wind speed modeling method and an improved WECS control method are proposed, respectively. The modeling method considers the spatial and temporal distributions of wind speed disturbances and deploys a box uncertain set in wind speed models, which is more realistic for practicing engineers. The control method takes maximum power point tracking, wind speed forecasting, and wind …

A Radial Basis Function Method For Solving Optimal Control Problems., Hossein Mirinejad

Electronic Theses and Dissertations

This work presents two direct methods based on the radial basis function (RBF) interpolation and arbitrary discretization for solving continuous-time optimal control problems: RBF Collocation Method and RBF-Galerkin Method. Both methods take advantage of choosing any global RBF as the interpolant function and any arbitrary points (meshless or on a mesh) as the discretization points. The first approach is called the RBF collocation method, in which states and controls are parameterized using a global RBF, and constraints are satisfied at arbitrary discrete nodes (collocation points) to convert the continuous-time optimal control problem to a nonlinear programming (NLP) problem. The …

Boundary Control Of Parabolic Pde Using Adaptive Dynamic Programming, Behzad Talaei

Doctoral Dissertations

"In this dissertation, novel adaptive/approximate dynamic programming (ADP) based state and output feedback control methods are presented for distributed parameter systems (DPS) which are expressed as uncertain parabolic partial differential equations (PDEs) in one and two dimensional domains. In the first step, the output feedback control design using an early lumping method is introduced after model reduction. Subsequently controllers were developed in four stages; Unlike current approaches in the literature, state and output feedback approaches were designed without utilizing model reduction for uncertain linear, coupled nonlinear and two-dimensional parabolic PDEs, respectively. In all of these techniques, the infinite horizon cost …

Optimal Battery Operations And Design Considering Capacity Fade Mechanisms, Bharatkumar Suthar

McKelvey School of Engineering Theses & Dissertations

Safely and capacity fade are the key issues that restrict the use of the lithium-ion battery for many applications. These issues are being tackled in a variety of ways. This dissertation focuses on using detailed continuum-level electrochemical models to study transport, kinetics, and mechanical processes in the lithium-ion batteries. These models can be used to quantify the effect of capacity fade mechanisms (side reactions and mechanical degradation) and improve the safety aspects of the lithium ion batteries. Three capacity-fade mechanisms—solid electrolyte interface side reaction, lithium-plating side reaction and mechanical degradation due to intercalation-induced stresses—are considered in the dissertation. Monitoring and …