Matching Items (24)
Description
This dissertation considers an integrated approach to system design and controller design based on analyzing limits of system performance. Historically, plant design methodologies have not incorporated control relevant considerations. Such an approach could result in a system that might not meet its specifications (or one that requires a complex control architecture to do so). System and controller designers often go through several iterations in order to converge to an acceptable plant and controller design. The focus of this dissertation is on the design and control an air-breathing hypersonic vehicle using such an integrated system-control design framework. The goal is to reduce the number of system-control design iterations (by explicitly incorporate control considerations in the system design process), as well as to influence the guidance/trajectory specifications for the system. Due to the high computational costs associated with obtaining a dynamic model for each plant configuration considered, approximations to the system dynamics are used in the control design process. By formulating the control design problem using bilinear and polynomial matrix inequalities, several common control and system design constraints can be simultaneously incorporated into a vehicle design optimization. Several design problems are examined to illustrate the effectiveness of this approach (and to compare the computational burden of this methodology against more traditional approaches).
ContributorsSridharan, Srikanth (Author) / Rodriguez, Armando A (Thesis advisor) / Mittelmann, Hans D (Committee member) / Si, Jennie (Committee member) / Tsakalis, Konstantinos S (Committee member) / Arizona State University (Publisher)
Created2014
Description
The problem of systematically designing a control system continues to remain a subject of intense research. In this thesis, a very powerful control system design environment for Linear Time-Invariant (LTI) Multiple-Input Multiple-Output (MIMO) plants is presented. The environment has been designed to address a broad set of closed loop metrics and constraints; e.g. weighted H-infinity closed loop performance subject to closed loop frequency and/or time domain constraints (e.g. peak frequency response, peak overshoot, peak controls, etc.). The general problem considered - a generalized weighted mixed-sensitivity problem subject to constraints - permits designers to directly address and tradeoff multivariable properties at distinct loop breaking points; e.g. at plant outputs and at plant inputs. As such, the environment is particularly powerful for (poorly conditioned) multivariable plants. The Youla parameterization is used to parameterize the set of all stabilizing LTI proper controllers. This is used to convexify the general problem being addressed. Several bases are used to turn the resulting infinite-dimensional problem into a finite-dimensional problem for which there exist many efficient convex optimization algorithms. A simple cutting plane algorithm is used within the environment. Academic and physical examples are presented to illustrate the utility of the environment.
ContributorsPuttannaiah, Karan (Author) / Rodriguez, Armando A (Thesis advisor) / Tsakalis, Konstantinos S (Committee member) / Si, Jennie (Committee member) / Arizona State University (Publisher)
Created2013
Description
This project is to develop a new method to generate GPS waypoints for better terrain mapping efficiency using an UAV. To create a map of a desired terrain, an UAV is used to capture images at particular GPS locations. These images are then stitched together to form a complete map of the terrain. To generate a good map using image stitching, the images are desired to have a certain percentage of overlap between them. In high windy condition, an UAV may not capture image at desired GPS location, which in turn interferes with the desired percentage of overlap between images; both frontal and sideways; thus causing discrepancies while stitching the images together. The information about the exact GPS locations at which the images are captured can be found on the flight logs that are stored in the Ground Control Station and the Auto pilot board. The objective is to look at the flight logs, predict the waypoints at which the UAV might have swayed from the desired flight path. If there are locations where flight swayed from intended path, the code should generate a new set of waypoints for a correction flight. This will save the time required for stitching the images together, thus making the whole process faster and more efficient.
ContributorsGhadage, Prasannakumar Prakashrao (Author) / Saripalli, Srikanth (Thesis advisor) / Berman, Spring M (Thesis advisor) / Thangavelautham, Jekanthan (Committee member) / Arizona State University (Publisher)
Created2014
Description
Toward the ambitious long-term goal of a fleet of cooperating Flexible Autonomous Machines operating in an uncertain Environment (FAME), this thesis addresses various perception and control problems in autonomous aerial robotics. The objective of this thesis is to motivate the use of perspective cues in single images for the planning and control of quadrotors in indoor environments. In addition to providing empirical evidence for the abundance of such cues in indoor environments, the usefulness of these perspective cues is demonstrated by designing a control algorithm for navigating a quadrotor in indoor corridors. An Extended Kalman Filter (EKF), implemented on top of the vision algorithm, serves to improve the robustness of the algorithm to changing illumination.
In this thesis, vanishing points are the perspective cues used to control and navigate a quadrotor in an indoor corridor. Indoor corridors are an abundant source of parallel lines. As a consequence of perspective projection, parallel lines in the real world, that are not parallel to the plane of the camera, intersect at a point in the image. This point is called the vanishing point of the image. The vanishing point is sensitive to the lateral motion of the camera and hence the quadrotor. By tracking the position of the vanishing point in every image frame, the quadrotor can navigate along the center of the corridor.
Experiments are conducted using the Augmented Reality (AR) Drone 2.0. The drone is equipped with the following componenets: (1) 720p forward facing camera for vanishing point detection, (2) 240p downward facing camera, (3) Inertial Measurement Unit (IMU) for attitude control , (4) Ultrasonic sensor for estimating altitude, (5) On-board 1 GHz Processor for processing low level commands. The reliability of the vision algorithm is presented by flying the drone in indoor corridors.
In this thesis, vanishing points are the perspective cues used to control and navigate a quadrotor in an indoor corridor. Indoor corridors are an abundant source of parallel lines. As a consequence of perspective projection, parallel lines in the real world, that are not parallel to the plane of the camera, intersect at a point in the image. This point is called the vanishing point of the image. The vanishing point is sensitive to the lateral motion of the camera and hence the quadrotor. By tracking the position of the vanishing point in every image frame, the quadrotor can navigate along the center of the corridor.
Experiments are conducted using the Augmented Reality (AR) Drone 2.0. The drone is equipped with the following componenets: (1) 720p forward facing camera for vanishing point detection, (2) 240p downward facing camera, (3) Inertial Measurement Unit (IMU) for attitude control , (4) Ultrasonic sensor for estimating altitude, (5) On-board 1 GHz Processor for processing low level commands. The reliability of the vision algorithm is presented by flying the drone in indoor corridors.
ContributorsRavishankar, Nikhilesh (Author) / Rodriguez, Armando A (Thesis advisor) / Tsakalis, Konstantinos (Committee member) / Berman, Spring M (Committee member) / Arizona State University (Publisher)
Created2018
Description
In the past decade, real-world applications of Vertical Take-Off and Landing (VTOL) Unmanned Aerial Vehicles (UAV) have increased significantly. There has been growing interest in one of these types of UAVs, called a tail-sitter UAV, due to its VTOL and cruise capabilities. This thesis presents the fabrication of a spherical tail-sitter UAV and derives a nonlinear mathematical model of its dynamics. The singularity in the attitude kinematics of the vehicle is avoided using Modified Rodrigues Parameters (MRP). The model parameters of the fabricated vehicle are calculated using the bifilar pendulum method, a motor stand, and ANSYS simulation software. Then the trim conditions at hover are calculated for the nonlinear model, and the rotational dynamics of the model are linearized around the equilibrium state with the calculated trim conditions. Robust controllers are designed to stabilize the UAV in hover using the H2 control and H-infinity control methodologies. For H2 control design, Linear Quadratic Gaussian (LQG) control is used. For the H infinity control design, Linear Matrix Inequalities (LMI) with frequency-dependent weights are derived and solved using the MATLAB toolbox YALMIP. In addition, a nonlinear controller is designed using the Sum-of-Squares (SOS) method to implement large-angle maneuvers for transitions between horizontal flight and vertical flight. Finally, the linear controllers are implemented in the fabricated spherical tail-sitter UAV for experimental validation. The performance trade-offs and the response of the UAV with the linear and nonlinear controllers are discussed in detail.
ContributorsRamasubramaniyan, Sri Ram Prasath (Author) / Berman, Spring M (Thesis advisor) / Mignolet, Marc P (Committee member) / Tsakalis, Konstantinos S (Committee member) / Arizona State University (Publisher)
Created2018
Description
Robotic swarms can potentially perform complicated tasks such as exploration and mapping at large space and time scales in a parallel and robust fashion. This thesis presents strategies for mapping environmental features of interest – specifically obstacles, collision-free paths, generating a metric map and estimating scalar density fields– in an unknown domain using data obtained by a swarm of resource-constrained robots. First, an approach was developed for mapping a single obstacle using a swarm of point-mass robots with both directed and random motion. The swarm population dynamics are modeled by a set of advection-diffusion-reaction partial differential equations (PDEs) in which a spatially-dependent indicator function marks the presence or absence of the obstacle in the domain. The indicator function is estimated by solving an optimization problem with PDEs as constraints. Second, a methodology for constructing a topological map of an unknown environment was proposed, which indicates collision-free paths for navigation, from data collected by a swarm of finite-sized robots. As an initial step, the number of topological features in the domain was quantified by applying tools from algebraic topology, to a probability function over the explored region that indicates the presence of obstacles. A topological map of the domain is then generated using a graph-based wave propagation algorithm. This approach is further extended, enabling the technique to construct a metric map of an unknown domain with obstacles using uncertain position data collected by a swarm of resource-constrained robots, filtered using intensity measurements of an external signal. Next, a distributed method was developed to construct the occupancy grid map of an unknown environment using a swarm of inexpensive robots or mobile sensors with limited communication. In addition to this, an exploration strategy which combines information theoretic ideas with Levy walks was also proposed. Finally, the problem of reconstructing a two-dimensional scalar field using observations from a subset of a sensor network in which each node communicates its local measurements to its neighboring nodes was addressed. This problem reduces to estimating the initial condition of a large interconnected system with first-order linear dynamics, which can be solved as an optimization problem.
ContributorsRamachandran, Ragesh Kumar (Author) / Berman, Spring M (Thesis advisor) / Mignolet, Marc (Committee member) / Artemiadis, Panagiotis (Committee member) / Marvi, Hamid (Committee member) / Robinson, Michael (Committee member) / Arizona State University (Publisher)
Created2018
Description
Toward the ambitious long-term goal of developing a robotic circus, this thesis addresses key steps toward the development of a ground robot that can catch a ball. Toward this end, we examine nonlinear quadratic drag trajectories for a tossed ball. Relevant least square error fits are provided. It is also shown how a Kalman filter and Extended Kalman filter can be used to generate estimates for the ball trajectory.
Several simple ball intercept policies are examined. This includes open loop and closed loop policies. It is also shown how a low-cost differential-drive research grade robot can be built, modeled and controlled. Directions for developing more complex xy planar intercept policies are also briefly discussed. In short, the thesis establishes a foundation for future work on developing a practical ball catching robot.
Several simple ball intercept policies are examined. This includes open loop and closed loop policies. It is also shown how a low-cost differential-drive research grade robot can be built, modeled and controlled. Directions for developing more complex xy planar intercept policies are also briefly discussed. In short, the thesis establishes a foundation for future work on developing a practical ball catching robot.
ContributorsDAS, NIRANGKUSH (Author) / Rodriguez, Armando A (Thesis advisor) / Berman, Spring M (Thesis advisor) / Artemiadis, Panagiotis (Committee member) / Arizona State University (Publisher)
Created2018
Description
Robotic technology is advancing to the point where it will soon be feasible to deploy massive populations, or swarms, of low-cost autonomous robots to collectively perform tasks over large domains and time scales. Many of these tasks will require the robots to allocate themselves around the boundaries of regions or features of interest and achieve target objectives that derive from their resulting spatial configurations, such as forming a connected communication network or acquiring sensor data around the entire boundary. We refer to this spatial allocation problem as boundary coverage. Possible swarm tasks that will involve boundary coverage include cooperative load manipulation for applications in construction, manufacturing, and disaster response.
In this work, I address the challenges of controlling a swarm of resource-constrained robots to achieve boundary coverage, which I refer to as the problem of stochastic boundary coverage. I first examined an instance of this behavior in the biological phenomenon of group food retrieval by desert ants, and developed a hybrid dynamical system model of this process from experimental data. Subsequently, with the aid of collaborators, I used a continuum abstraction of swarm population dynamics, adapted from a modeling framework used in chemical kinetics, to derive stochastic robot control policies that drive a swarm to target steady-state allocations around multiple boundaries in a way that is robust to environmental variations.
Next, I determined the statistical properties of the random graph that is formed by a group of robots, each with the same capabilities, that have attached to a boundary at random locations. I also computed the probability density functions (pdfs) of the robot positions and inter-robot distances for this case.
I then extended this analysis to cases in which the robots have heterogeneous communication/sensing radii and attach to a boundary according to non-uniform, non-identical pdfs. I proved that these more general coverage strategies generate random graphs whose probability of connectivity is Sharp-P Hard to compute. Finally, I investigated possible approaches to validating our boundary coverage strategies in multi-robot simulations with realistic Wi-fi communication.
In this work, I address the challenges of controlling a swarm of resource-constrained robots to achieve boundary coverage, which I refer to as the problem of stochastic boundary coverage. I first examined an instance of this behavior in the biological phenomenon of group food retrieval by desert ants, and developed a hybrid dynamical system model of this process from experimental data. Subsequently, with the aid of collaborators, I used a continuum abstraction of swarm population dynamics, adapted from a modeling framework used in chemical kinetics, to derive stochastic robot control policies that drive a swarm to target steady-state allocations around multiple boundaries in a way that is robust to environmental variations.
Next, I determined the statistical properties of the random graph that is formed by a group of robots, each with the same capabilities, that have attached to a boundary at random locations. I also computed the probability density functions (pdfs) of the robot positions and inter-robot distances for this case.
I then extended this analysis to cases in which the robots have heterogeneous communication/sensing radii and attach to a boundary according to non-uniform, non-identical pdfs. I proved that these more general coverage strategies generate random graphs whose probability of connectivity is Sharp-P Hard to compute. Finally, I investigated possible approaches to validating our boundary coverage strategies in multi-robot simulations with realistic Wi-fi communication.
ContributorsPeruvemba Kumar, Ganesh (Author) / Berman, Spring M (Thesis advisor) / Fainekos, Georgios (Thesis advisor) / Bazzi, Rida (Committee member) / Syrotiuk, Violet (Committee member) / Taylor, Thomas (Committee member) / Arizona State University (Publisher)
Created2016
Description
With recent advances in missile and hypersonic vehicle technologies, the need for being able to accurately simulate missile-target engagements has never been greater. Within this research, we examine a fully integrated missile-target engagement environment. A MATLAB based application is developed with 3D animation capabilities to study missile-target engagement and visualize them. The high fidelity environment is used to validate miss distance analysis with the results presented in relevant GNC textbooks and to examine how the kill zone varies with critical engagement parameters; e.g. initial engagement altitude, missile Mach, and missile maximum acceleration. A ray-based binary search algorithm is used to estimate the kill zone region; i.e. the set of initial target starting conditions such that it will be "killed". The results show what is expected. The kill zone increases with larger initial missile Mach and maximum acceleration & decreases with higher engagement altitude and higher target Mach. The environment is based on (1) a 6DOF bank-to-turn (BTT) missile, (2) a full aerodynamic-stability derivative look up tables ranging over Mach number, angle of attack and sideslip angle (3) a standard atmosphere model, (4) actuator dynamics for each of the four cruciform fins, (5) seeker dynamics, (6) a nonlinear autopilot, (7) a guidance system with three guidance algorithms (i.e. PNG, optimal, differential game theory), (8) a 3DOF target model with three maneuverability models (i.e. constant speed, Shelton Turn & Climb, Riggs-Vergaz Turn & Dive). Each of the subsystems are described within the research. The environment contains linearization, model analysis and control design features. A gain scheduled nonlinear BTT missile autopilot is presented here. Autopilot got sluggish as missile altitude increased and got aggressive as missile mach increased. In short, the environment is shown to be a very powerful tool for conducting missile-target engagement research - a research that could address multiple missiles and advanced targets.
ContributorsRenganathan, Venkatraman (Author) / Rodriguez, Armando A (Thesis advisor) / Artemiadis, Panagiotis (Committee member) / Berman, Spring M (Committee member) / Arizona State University (Publisher)
Created2016
Description
Behavioral health problems such as physical inactivity are among the main causes of mortality around the world. Mobile and wireless health (mHealth) interventions offer the opportunity for applying control engineering concepts in behavioral change settings. Social Cognitive Theory (SCT) is among the most influential theories of health behavior and has been used as the conceptual basis of many behavioral interventions. This dissertation examines adaptive behavioral interventions for physical inactivity problems based on SCT using system identification and control engineering principles. First, a dynamical model of SCT using fluid analogies is developed. The model is used throughout the dissertation to evaluate system identification approaches and to develop control strategies based on Hybrid Model Predictive Control (HMPC). An initial system identification informative experiment is designed to obtain basic insights about the system. Based on the informative experimental results, a second optimized experiment is developed as the solution of a formal constrained optimization problem. The concept of Identification Test Monitoring (ITM) is developed for determining experimental duration and adjustments to the input signals in real time. ITM relies on deterministic signals, such as multisines, and uncertainty regions resulting from frequency domain transfer function estimation that is performed during experimental execution. ITM is motivated by practical considerations in behavioral interventions; however, a generalized approach is presented for broad-based multivariable application settings such as process control. Stopping criteria for the experimental test utilizing ITM are developed using both open-loop and robust control considerations.
A closed-loop intensively adaptive intervention for physical activity is proposed relying on a controller formulation based on HMPC. The discrete and logical features of HMPC naturally address the categorical nature of the intervention components that include behavioral goals and reward points. The intervention incorporates online controller reconfiguration to manage the transition between the behavioral initiation and maintenance training stages. Simulation results are presented to illustrate the performance of the system using a model for a hypothetical participant under realistic conditions that include uncertainty. The contributions of this dissertation can ultimately impact novel applications of cyberphysical system in medical applications.
A closed-loop intensively adaptive intervention for physical activity is proposed relying on a controller formulation based on HMPC. The discrete and logical features of HMPC naturally address the categorical nature of the intervention components that include behavioral goals and reward points. The intervention incorporates online controller reconfiguration to manage the transition between the behavioral initiation and maintenance training stages. Simulation results are presented to illustrate the performance of the system using a model for a hypothetical participant under realistic conditions that include uncertainty. The contributions of this dissertation can ultimately impact novel applications of cyberphysical system in medical applications.
ContributorsMartín Moreno, César Antonio (Author) / Rivera, Daniel E (Thesis advisor) / Hekler, Eric B. (Committee member) / Peet, Matthew M (Committee member) / Tsakalis, Konstantinos S (Committee member) / Arizona State University (Publisher)
Created2016