Theses and Dissertations from UMD
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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
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Item ESTIMATION AND CONTROL OF A DISTRIBUTED PARAMETER SYSTEM BY A TEAM OF MOBILE SENSORS AND ACTUATORS(2021) Cheng, Sheng; Paley, Derek A; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The recent development of mobile robots has dramatically extended the scenarios where robots can be deployed to complete tasks autonomously. One of the tasks is monitoring and controlling large-scale spatiotemporal processes, e.g., oil spills and forest fires, which is mainly conducted by human operators. These tasks can pose health threats, cause severe environmental issues, and incur substantial financial costs. Autonomous robots can free human operators from danger and complete tasks in a timely and economically efficient manner. In this dissertation, estimation and control of spatiotemporal processes using mobile sensors and actuators are studied. Spatiotemporal processes vary in both space and time, whose dynamics can be characterized by partial differential equations (PDEs). Since the state space of a PDE is infinite-dimensional, a system with PDE dynamics is also known as a distributed parameter system (DPS). The performance of the estimation and control of a DPS can be enhanced (compared to stationary sensors and actuators) due to the additional degree of freedom induced from the mobility of the sensors and actuators. However, the vehicles carrying sensors and actuators usually have limited onboard resources (e.g., fuels and batteries) whose usage requires judicious decisions. Hence, we propose a new optimization framework that addresses the goal of estimation and control of a spatiotemporal process while considering the limited onboard resources. In the first part of this dissertation, an optimization framework is proposed to control a DPS modeled by a 2D diffusion-advection equation using a team of mobile actuators. The framework simultaneously seeks optimal control of the DPS and optimal guidance of the mobile actuators such that a cost function associated with both the DPS and the mobile actuators is minimized subject to the dynamics of each. We establish conditions for the existence of a solution to the proposed problem. Since computing an optimal solution requires approximation, we also establish the conditions for convergence to the exact optimal solution of the approximate optimal solution. That is, when evaluating these two solutions by the original cost function, the difference becomes arbitrarily small as the approximation gets finer. Two numerical examples demonstrate the performance of the optimal control and guidance obtained from the proposed approach. In the second part of this dissertation, an optimization framework is proposed to design guidance for a possibly heterogeneous team of multiple mobile sensors to estimate a spatiotemporal process modeled by a 2D diffusion-advection process. Owing to the abstract linear system representation of the process, we apply the Kalman-Bucy filter for estimation, where the sensors provide linear outputs. We propose an optimization problem that minimizes the sum of the trace of the covariance operator of the Kalman-Bucy filter and a generic mobility cost of the mobile sensors, subject to the sensors' motion modeled by linear dynamics. We establish the existence of a solution to this problem. Moreover, we prove convergence to the exact optimal solution of the approximate optimal solution. That is, when evaluating these two solutions using the original cost function, the difference becomes arbitrarily small as the approximation gets finer. To compute the approximate solution, we use Pontryagin's minimum principle after approximating the infinite-dimensional terms originating from the diffusion-advection process. The approximate solution is applied in simulation to analyze how a single mobile sensor's performance depends on two important parameters: sensor noise variance and mobility penalty. We also illustrate the application of the framework to multiple sensors, in particular the performance of a heterogeneous team of sensors. In the third part of this dissertation, a cooperative framework for estimating and controlling a spatiotemporal process using collocated mobile sensors and actuators is proposed. We model the spatiotemporal process by a 2D diffusion equation that represents the dynamics. Measurement and actuation of the process dynamics are performed by mobile agents whose motion is described by single-integrator dynamics. The estimation and control framework is formulated using a Kalman filter and an optimization problem. The former uses sensor measurements to reconstruct the process state, while the latter uses the estimated state to plan the actuation and guidance of the mobile agents. The optimization problem seeks the actuation and guidance that minimize the sum of the quadratic costs of the process state, actuation input, and guidance effort. Constraints include the process and agent dynamics as well as actuation and speed bounds. The framework is implemented with the optimization problem solved periodically using a nonlinear program solver. Numerical studies analyze and evaluate the performance of the proposed framework using a nondimensional parameterization of the optimization problem. The framework is also implemented on an outdoor multi-quadrotor testbed with a simulated spatiotemporal process and synthetic measurement and actuation.Item CONSENSUS, PREDICTION AND OPTIMIZATION IN DIRECTED NETWORKS(2017) Mai, Van Sy; Abed, Eyad H.; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation develops theory and algorithms for distributed consensus in multi-agent networks. The models considered are opinion dynamics models based on the well known DeGroot model. We study the following three related topics: consensus of networks with leaders, consensus prediction, and distributed optimization. First, we revisit the problem of agreement seeking in a weighted directed network in the presence of leaders. We develop new sufficient conditions that are weaker than existing conditions for guaranteeing consensus for both fixed and switching network topologies, emphasizing the importance not only of persistent connectivity between the leader and the followers but also of the strength of the connections. We then study the problem of a leader aiming to maximize its influence on the opinions of the network agents through targeted connection with a limited number of agents, possibly in the presence of another leader having a competing opinion. We reveal fundamental properties of leader influence defined in terms of either the transient behavior or the achieved steady state opinions of the network agents. In particular, not only is the degree of this influence a supermodular set function, but its continuous relaxation is also convex for any strongly connected directed network. These results pave the way for developing efficient approximation algorithms admitting certain quality certifications, which when combined can provide effective tools and better analysis for optimal influence spreading in large networks. Second, we introduce and investigate problems of network monitoring and consensus prediction. Here, an observer, without exact knowledge of the network, seeks to determine in the shortest possible time the asymptotic agreement value by monitoring a subset of the agents. We uncover a fundamental limit on the minimum required monitoring time for the case of a single observed node, and analyze the case of multiple observed nodes. We provide conditions for achieving the limit in the former case and develop algorithms toward achieving conjectured bounds in the latter through local observation and local computation. Third, we study a distributed optimization problem where a network of agents seeks to minimize the sum of the agents' individual objective functions while each agent may be associated with a separate local constraint. We develop new distributed algorithms for solving this problem. In these algorithms, consensus prediction is employed as a means to achieve fast convergence rates, possibly in finite time. An advantage of our distributed optimization algorithms is that they work under milder assumptions on the network weight matrix than are commonly assumed in the literature. Most distributed algorithms require undirected networks. Consensus-based algorithms can apply to directed networks under an assumption that the network weight matrix is doubly stochastic (i.e., both row stochastic and column stochastic), or in some recent literature only column stochastic. Our algorithms work for directed networks and only require row stochasticity, a mild assumption. Doubly stochastic or column stochastic weight matrices can be hard to arrange locally, especially in broadcast-based communication. We achieve the simplification to the row stochastic assumption through a distributed rescaling technique. Next, we develop a unified convergence analysis of a distributed projected subgradient algorithm and its variation that can be applied to both unconstrained and constrained problems without assuming boundedness or commonality of the local constraint sets.Item Model-Predictive Strategy Generation for Multi-Agent Pursuit-Evasion Games(2015) Raboin, Eric James; Nau, Dana S; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Multi-agent pursuit-evasion games can be used to model a variety of different real world problems including surveillance, search-and-rescue, and defense-related scenarios. However, many pursuit-evasion problems are computationally difficult, which can be problematic for domains with complex geometry or large numbers of agents. To compound matters further, practical applications often require planning methods to operate under high levels of uncertainty or meet strict running-time requirements. These challenges strongly suggest that heuristic methods are needed to address pursuit-evasion problems in the real world. In this dissertation I present heuristic planning techniques for three related problem domains: visibility-based pursuit-evasion, target following with differential motion constraints, and distributed asset guarding with unmanned sea-surface vehicles. For these domains, I demonstrate that heuristic techniques based on problem relaxation and model-predictive simulation can be used to efficiently perform low-level control action selection, motion goal selection, and high-level task allocation. In particular, I introduce a polynomial-time algorithm for control action selection in visibility-based pursuit-evasion games, where a team of pursuers must minimize uncertainty about the location of an evader. The algorithm uses problem relaxation to estimate future states of the game. I also show how to incorporate a probabilistic opponent model learned from interaction traces of prior games into the algorithm. I verify experimentally that by performing Monte Carlo sampling over the learned model to estimate the location of the evader, the algorithm performs better than existing planning approaches based on worst-case analysis. Next, I introduce an algorithm for motion goal selection in pursuit-evasion scenarios with unmanned boats. I show how a probabilistic model accounting for differential motion constraints can be used to project the future positions of the target boat. Motion goals for the pursuer boat can then be selected based on those projections. I verify experimentally that motion goals selected with this technique are better optimized for travel time and proximity to the target boat when compared to motion goals selected based on the current position of the target boat. Finally, I introduce a task-allocation technique for a team of unmanned sea-surface vehicles (USVs) responsible for guarding a high-valued asset. The team of USVs must intercept and block a set of hostile intruder boats before they reach the asset. The algorithm uses model-predictive simulation to estimate the value of high-level task assignments, which are then realized by a set of learned low-level behaviors. I show experimentally that using model-predictive simulations based on Monte-Carlo sampling is more effective than hand-coded evaluation heuristics.Item Learning in Engineered Multi-agent Systems(2014) Menon, Anup; Baras, John S; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Consider the problem of maximizing the total power produced by a wind farm. Due to aerodynamic interactions between wind turbines, each turbine maximizing its individual power--as is the case in present-day wind farms--does not lead to optimal farm-level power capture. Further, there are no good models to capture the said aerodynamic interactions, rendering model based optimization techniques ineffective. Thus, model-free distributed algorithms are needed that help turbines adapt their power production on-line so as to maximize farm-level power capture. Motivated by such problems, the main focus of this dissertation is a distributed model-free optimization problem in the context of multi-agent systems. The set-up comprises of a fixed number of agents, each of which can pick an action and observe the value of its individual utility function. An individual's utility function may depend on the collective action taken by all agents. The exact functional form (or model) of the agent utility functions, however, are unknown; an agent can only measure the numeric value of its utility. The objective of the multi-agent system is to optimize the welfare function (i.e. sum of the individual utility functions). Such a collaborative task requires communications between agents and we allow for the possibility of such inter-agent communications. We also pay attention to the role played by the pattern of such information exchange on certain aspects of performance. We develop two algorithms to solve this problem. The first one, engineered Interactive Trial and Error Learning (eITEL) algorithm, is based on a line of work in the Learning in Games literature and applies when agent actions are drawn from finite sets. While in a model-free setting, we introduce a novel qualitative graph-theoretic framework to encode known directed interactions of the form "which agents' action affect which others' payoff" (interaction graph). We encode explicit inter-agent communications in a directed graph (communication graph) and, under certain conditions, prove convergence of agent joint action (under eITEL) to the welfare optimizing set. The main condition requires that the union of interaction and communication graphs be strongly connected; thus the algorithm combines an implicit form of communication (via interactions through utility functions) with explicit inter-agent communications to achieve the given collaborative goal. This work has kinship with certain evolutionary computation techniques such as Simulated Annealing; the algorithm steps are carefully designed such that it describes an ergodic Markov chain with a stationary distribution that has support over states where agent joint actions optimize the welfare function. The main analysis tool is perturbed Markov chains and results of broader interest regarding these are derived as well. The other algorithm, Collaborative Extremum Seeking (CES), uses techniques from extremum seeking control to solve the problem when agent actions are drawn from the set of real numbers. In this case, under the assumption of existence of a local minimizer for the welfare function and a connected undirected communication graph between agents, a result regarding convergence of joint action to a small neighborhood of a local optimizer of the welfare function is proved. Since extremum seeking control uses a simultaneous gradient estimation-descent scheme, gradient information available in the continuous action space formulation is exploited by the CES algorithm to yield improved convergence speeds. The effectiveness of this algorithm for the wind farm power maximization problem is evaluated via simulations. Lastly, we turn to a different question regarding role of the information exchange pattern on performance of distributed control systems by means of a case study for the vehicle platooning problem. In the vehicle platoon control problem, the objective is to design distributed control laws for individual vehicles in a platoon (or a road-train) that regulate inter-vehicle distances at a specified safe value while the entire platoon follows a leader-vehicle. While most of the literature on the problem deals with some inadequacy in control performance when the information exchange is of the nearest neighbor-type, we consider an arbitrary graph serving as information exchange pattern and derive a relationship between how a certain indicator of control performance is related to the information pattern. Such analysis helps in understanding qualitative features of the `right' information pattern for this problem.Item Synthesis of Strategies for Non-Zero-Sum Repeated Games(2008-08-21) Au, Tsz-Chiu; Nau, Dana; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)There are numerous applications that involve two or more self-interested autonomous agents that repeatedly interact with each other in order to achieve a goal or maximize their utilities. This dissertation focuses on the problem of how to identify and exploit useful structures in agents' behavior for the construction of good strategies for agents in multi-agent environments, particularly non-zero-sum repeated games. This dissertation makes four contributions to the study of this problem. First, this thesis describes a way to take a set of interaction traces produced by different pairs of players in a two-player repeated game, and then find the best way to combine them into a strategy. The strategy can then be incorporated into an existing agent, as an enhancement of the agent's original strategy. In cross-validated experiments involving 126 agents for the Iterated Prisoner's Dilemma, Iterated Chicken Game, and Iterated Battle of the Sexes, my technique was able to make improvement to the performance of nearly all of the agents. Second, this thesis investigates the issue of uncertainty about goals when a goal-based agent situated in a nondeterministic environment. The results of this investigation include the necessary and sufficiency conditions for such guarantee, and an algorithm for synthesizing a strategy from interaction traces that maximizes the probability of success of an agent even when no strategy can assure the success of the agent. Third, this thesis introduces a technique, Symbolic Noise Detection (SND), for detecting noise (i.e., mistakes or miscommunications) among agents in repeated games. The idea is that if we can build a model of the other agent's behavior, we can use this model to detect and correct actions that have been affected by noise. In the 20th Anniversary Iterated Prisoner's Dilemma competition, the SND agent placed third in the "noise" category, and was the best performer among programs that had no "slave" programs feeding points to them. Fourth, the thesis presents a generalization of SND that can be wrapped around any existing strategy. Finally, the thesis includes a general framework for synthesizing strategies from experience for repeated games in both noisy and noisy-free environments.