Matching Items (7)

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Low-cost Image-assisted Inertial Navigation System for a Micro Air Vehicle

Description

The increasing civilian demand for autonomous aerial vehicle platforms in both hobby and professional markets has resulted in an abundance of inexpensive inertial navigation systems and hardware. Many of these

The increasing civilian demand for autonomous aerial vehicle platforms in both hobby and professional markets has resulted in an abundance of inexpensive inertial navigation systems and hardware. Many of these systems lack full autonomy, relying on the pilot's guidance with the assistance of inertial sensors for guidance. Autonomous systems depend heavily on the use of a global positioning satellite receiver which can be inhibited by satellite signal strength, low update rates and poor positioning accuracy. For precise navigation of a micro air vehicle in locations where GPS signals are unobtainable, such as indoors or throughout a dense urban environment, additional sensors must complement the inertial sensors to provide improved navigation state estimations without the use of a GPS. By creating a system that allows for the rapid development of experimental guidance, navigation and control algorithms on versatile, low-cost development platforms, improved navigation systems may be tested with relative ease and at reduced cost. Incorporating a downward-facing camera with this system may also be utilized to further improve vehicle autonomy in denied-GPS environments.

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Created

Date Created
  • 2014-12

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An Investigation of Human Error Correction in Anthropomorphic Robotic Armatures

Description

As robots become more prevalent, the need is growing for efficient yet stable control systems for applications with humans in the loop. As such, it is a challenge for scientists

As robots become more prevalent, the need is growing for efficient yet stable control systems for applications with humans in the loop. As such, it is a challenge for scientists and engineers to develop robust and agile systems that are capable of detecting instability in teleoperated systems. Despite how much research has been done to characterize the spatiotemporal parameters of human arm motions for reaching and gasping, not much has been done to characterize the behavior of human arm motion in response to control errors in a system. The scope of this investigation is to investigate human corrective actions in response to error in an anthropomorphic teleoperated robot limb. Characterizing human corrective actions contributes to the development of control strategies that are capable of mitigating potential instabilities inherent in human-machine control interfaces. Characterization of human corrective actions requires the simulation of a teleoperated anthropomorphic armature and the comparison of a human subject's arm kinematics, in response to error, against the human arm kinematics without error. This was achieved using OpenGL software to simulate a teleoperated robot arm and an NDI motion tracking system to acquire the subject's arm position and orientation. Error was intermittently and programmatically introduced to the virtual robot's joints as the subject attempted to reach for several targets located around the arm. The comparison of error free human arm kinematics to error prone human arm kinematics revealed an addition of a bell shaped velocity peak into the human subject's tangential velocity profile. The size, extent, and location of the additional velocity peak depended on target location and join angle error. Some joint angle and target location combinations do not produce an additional peak but simply maintain the end effector velocity at a low value until the target is reached. Additional joint angle error parameters and degrees of freedom are needed to continue this investigation.

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Created

Date Created
  • 2013-05

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Optimal input signal design for data-centric identification and control with applications to behavioral health and medicine

Description

Increasing interest in individualized treatment strategies for prevention and treatment of health disorders has created a new application domain for dynamic modeling and control. Standard population-level clinical trials, while useful,

Increasing interest in individualized treatment strategies for prevention and treatment of health disorders has created a new application domain for dynamic modeling and control. Standard population-level clinical trials, while useful, are not the most suitable vehicle for understanding the dynamics of dosage changes to patient response. A secondary analysis of intensive longitudinal data from a naltrexone intervention for fibromyalgia examined in this dissertation shows the promise of system identification and control. This includes datacentric identification methods such as Model-on-Demand, which are attractive techniques for estimating nonlinear dynamical systems from noisy data. These methods rely on generating a local function approximation using a database of regressors at the current operating point, with this process repeated at every new operating condition. This dissertation examines generating input signals for data-centric system identification by developing a novel framework of geometric distribution of regressors and time-indexed output points, in the finite dimensional space, to generate sufficient support for the estimator. The input signals are generated while imposing “patient-friendly” constraints on the design as a means to operationalize single-subject clinical trials. These optimization-based problem formulations are examined for linear time-invariant systems and block-structured Hammerstein systems, and the results are contrasted with alternative designs based on Weyl's criterion. Numerical solution to the resulting nonconvex optimization problems is proposed through semidefinite programming approaches for polynomial optimization and nonlinear programming methods. It is shown that useful bounds on the objective function can be calculated through relaxation procedures, and that the data-centric formulations are amenable to sparse polynomial optimization. In addition, input design formulations are formulated for achieving a desired output and specified input spectrum. Numerical examples illustrate the benefits of the input signal design formulations including an example of a hypothetical clinical trial using the drug gabapentin. In the final part of the dissertation, the mixed logical dynamical framework for hybrid model predictive control is extended to incorporate a switching time strategy, where decisions are made at some integer multiple of the sample time, and manipulation of only one input at a given sample time among multiple inputs. These are considerations important for clinical use of the algorithm.

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Created

Date Created
  • 2014

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Small Satellite Electromagnetic Docking System Modeling and Control

Description

There is a growing need for interplanetary travel technology development. There are hence plans to build deep space human habitats, communication relays, and fuel depots. These can be classified as

There is a growing need for interplanetary travel technology development. There are hence plans to build deep space human habitats, communication relays, and fuel depots. These can be classified as large space structures. To build large structures, it is essential that these are modular in nature. With modularization of structures, it becomes essential that interconnection of modules is developed. Docking systems enable interconnection of modules. The state-of-the-art technology in docking systems is the Power Data Grapple Fixture (PDGF), used on the International Space Station by the Canadarm2 robotic arm to grapple, latch onto and provide power to the object it has grappled. The PDGF is operated by highly skilled astronauts on the ISS and are prone to human errors. Therefore, there is a need for autonomous docking. Another issue with the PDGF is that it costs around 1 to 2 million US dollars to build the 26-inch diameter docking mechanism. Hence, there is a growing need to build a lower cost and scalable, smaller docking systems. Building scalable smaller docking systems will hence enable testing them on small satellites. With the increasing need for small, low cost, autonomous docking systems, this thesis has been proposed. This thesis focuses on modeling and autonomous control of an electromagnetic probe and cone docking mechanism. The electromagnetic docking system is known to be a highly nonlinear system. Hence, this work discusses various control strategies for this docking system using a levitation strategy.

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Created

Date Created
  • 2018

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Fractional Order PID Controller Tuning by Frequency Loop-Shaping: Analysis and Applications

Description

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

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.

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Created

Date Created
  • 2017

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Multivariable control of fixed wing aircrafts

Description

This thesis addresses control design for fixed-wing air-breathing aircraft. Four aircraft with distinct dynamical properties are considered: a scram-jet powered hypersonic (100foot long, X-43 like, wedge shaped) aircraft with

This thesis addresses control design for fixed-wing air-breathing aircraft. Four aircraft with distinct dynamical properties are considered: a scram-jet powered hypersonic (100foot long, X-43 like, wedge shaped) aircraft with flexible modes operating near Mach 8, 85k ft, a NASA HiMAT (Highly Maneuverable Aircraft Technology) F-18 aircraft,

a McDonnell Douglas AV-8A Harrier aircraft, and a Vought F-8 Crusader aircraft. A two-input two-output (TITO) longitudinal LTI (linear time invariant) dynamical model is used for each aircraft. Control design trade studies are conducted for each of the aircraft. Emphasis is placed on the hypersonic vehicle because of its complex nonlinear (unstable, non-minimum phase, flexible) dynamics and uncertainty associated with hypersonic flight (Mach $>$ 5, shocks and high temperatures on leading edges). Two plume models are used for the hypersonic vehicle – an old plume model and a new plume model. The old plume model is simple and assumes a typical decaying pressure distribution for aft nozzle. The new plume model uses Newtonian impact theory and a nonlinear solver to compute the aft nozzle pressure distribution. Multivariable controllers were generated using standard weighted $H_{\inf}$ mixed-sensitivity optimization as well as a new input disturbance weighted mixed-sensitivity framework that attempts to achieve good multivariable properties at both the controls (plant inputs) as well as the errors (plant outputs). Classical inner-outer (PD-PI) structures (partially centralized and decentralized) were also used. It is shown that while these classical (sometimes partially centralized PD-PI) structures could be used to generate comparable results to the multivariable controllers (e.g. for the hypersonic vehicle, Harrier, F-8), considerable tuning (iterative optimization) is often essential. This is especially true for the highly coupled hypersonic vehicle – thus justifying the need for a good multivariable control design tool. Fundamental control design tradeoffs for each aircraft are presented – comprehensively for the hypersonic aircraft. In short, the thesis attempts to shed light on when complex controllers are essential and when simple structures are sufficient for achieving control designs with good multivariable loop properties at both the errors (plant outputs) and the controls (plant inputs).

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Created

Date Created
  • 2015

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A control engineering approach for designing an optimized treatment plan for fibromyalgia

Description

There is increasing interest in the medical and behavioral health communities towards developing effective strategies for the treatment of chronic diseases. Among these lie adaptive interventions, which consider adjusting treatment

There is increasing interest in the medical and behavioral health communities towards developing effective strategies for the treatment of chronic diseases. Among these lie adaptive interventions, which consider adjusting treatment dosages over time based on participant response. Control engineering offers a broad-based solution framework for optimizing the effectiveness of such interventions. In this thesis, an approach is proposed to develop dynamical models and subsequently, hybrid model predictive control schemes for assigning optimal dosages of naltrexone, an opioid antagonist, as treatment for a chronic pain condition known as fibromyalgia. System identification techniques are employed to model the dynamics from the daily diary reports completed by participants of a blind naltrexone intervention trial. These self-reports include assessments of outcomes of interest (e.g., general pain symptoms, sleep quality) and additional external variables (disturbances) that affect these outcomes (e.g., stress, anxiety, and mood). Using prediction-error methods, a multi-input model describing the effect of drug, placebo and other disturbances on outcomes of interest is developed. This discrete time model is approximated by a continuous second order model with zero, which was found to be adequate to capture the dynamics of this intervention. Data from 40 participants in two clinical trials were analyzed and participants were classified as responders and non-responders based on the models obtained from system identification. The dynamical models can be used by a model predictive controller for automated dosage selection of naltrexone using feedback/feedforward control actions in the presence of external disturbances. The clinical requirement for categorical (i.e., discrete-valued) drug dosage levels creates a need for hybrid model predictive control (HMPC). The controller features a multiple degree-of-freedom formulation that enables the user to adjust the speed of setpoint tracking, measured disturbance rejection and unmeasured disturbance rejection independently in the closed loop system. The nominal and robust performance of the proposed control scheme is examined via simulation using system identification models from a representative participant in the naltrexone intervention trial. The controller evaluation described in this thesis gives credibility to the promise and applicability of control engineering principles for optimizing adaptive interventions.

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Created

Date Created
  • 2011