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Traditional approaches to modeling microgrids include the behavior of each inverter operating in a particular network configuration and at a particular operating point. Such models quickly become computationally intensive for large systems. Similarly, traditional approaches to control do not use advanced methodologies and suffer from poor performance and limited operating

Traditional approaches to modeling microgrids include the behavior of each inverter operating in a particular network configuration and at a particular operating point. Such models quickly become computationally intensive for large systems. Similarly, traditional approaches to control do not use advanced methodologies and suffer from poor performance and limited operating range. In this document a linear model is derived for an inverter connected to the Thevenin equivalent of a microgrid. This model is then compared to a nonlinear simulation model and analyzed using the open and closed loop systems in both the time and frequency domains. The modeling error is quantified with emphasis on its use for controller design purposes. Control design examples are given using a Glover McFarlane controller, gain sched- uled Glover McFarlane controller, and bumpless transfer controller which are compared to the standard droop control approach. These examples serve as a guide to illustrate the use of multi-variable modeling techniques in the context of robust controller design and show that gain scheduled MIMO control techniques can extend the operating range of a microgrid. A hardware implementation is used to compare constant gain droop controllers with Glover McFarlane controllers and shows a clear advantage of the Glover McFarlane approach.
ContributorsSteenis, Joel (Author) / Ayyanar, Raja (Thesis advisor) / Mittelmann, Hans (Committee member) / Tsakalis, Konstantinos (Committee member) / Tylavsky, Daniel (Committee member) / Arizona State University (Publisher)
Created2013
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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

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.
ContributorsDeśapāṇḍe, Sunīla (Author) / Rivera, Daniel E. (Thesis advisor) / Si, Jennie (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2011
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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 loop shaping techniques. This dissertation analyzes the effect of the order of a fractional integrator which is used as a

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.
ContributorsSaleh, Khalid M (Author) / Tsakalis, Konstantinos (Thesis advisor) / Rodriguez, Armando (Committee member) / Si, Jennie (Committee member) / Artemiadis, Panagiotis (Committee member) / Arizona State University (Publisher)
Created2017
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Description
In recent years, wide bandgap (WBG) devices enable power converters with higher power density and higher efficiency. On the other hand, smart grid technologies are getting mature due to new battery technology and computer technology. In the near future, the two technologies will form the next generation of smart grid

In recent years, wide bandgap (WBG) devices enable power converters with higher power density and higher efficiency. On the other hand, smart grid technologies are getting mature due to new battery technology and computer technology. In the near future, the two technologies will form the next generation of smart grid enabled by WBG devices. This dissertation deals with two applications: silicon carbide (SiC) device used for medium voltage level interface (7.2 kV to 240 V) and gallium nitride (GaN) device used for low voltage level interface (240 V/120 V). A 20 kW solid state transformer (SST) is designed with 6 kHz switching frequency SiC rectifier. Then three robust control design methods are proposed for each of its smart grid operation modes. In grid connected mode, a new LCL filter design method is proposed considering grid voltage THD, grid current THD and current regulation loop robust stability with respect to the grid impedance change. In grid islanded mode, µ synthesis method combined with variable structure control is used to design a robust controller for grid voltage regulation. For grid emergency mode, multivariable controller designed using H infinity synthesis method is proposed for accurate power sharing. Controller-hardware-in-the-loop (CHIL) testbed considering 7-SST system is setup with Real Time Digital Simulator (RTDS). The real TMS320F28335 DSP and Spartan 6 FPGA control board is used to interface a switching model SST in RTDS. And the proposed control methods are tested. For low voltage level application, a 3.3 kW smart grid hardware is built with 3 GaN inverters. The inverters are designed with the GaN device characterized using the proposed multi-function double pulse tester. The inverter is controlled by onboard TMS320F28379D dual core DSP with 200 kHz sampling frequency. Each inverter is tested to process 2.2 kW power with overall efficiency of 96.5 % at room temperature. The smart grid monitor system and fault interrupt devices (FID) based on Arduino Mega2560 are built and tested. The smart grid cooperates with GaN inverters through CAN bus communication. At last, the three GaN inverters smart grid achieved the function of grid connected to islanded mode smooth transition
ContributorsYao, Tong (Author) / Ayyanar, Raja (Thesis advisor) / Karady, George G. (Committee member) / Qin, Jiangchao (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2017
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Description
The Inverted Pendulum on a Cart is a classical control theory problem that helps understand the importance of feedback control systems for a coupled plant. In this study, a custom built pendulum system is coupled with a linearly actuated cart and a control system is designed to show the stability

The Inverted Pendulum on a Cart is a classical control theory problem that helps understand the importance of feedback control systems for a coupled plant. In this study, a custom built pendulum system is coupled with a linearly actuated cart and a control system is designed to show the stability of the pendulum. The three major objectives of this control system are to swing up the pendulum, balance the pendulum in the inverted position (i.e. $180^\circ$), and maintain the position of the cart. The input to this system is the translational force applied to the cart using the rotation of the tires. The main objective of this thesis is to design a control system that will help in balancing the pendulum while maintaining the position of the cart and implement it in a robot. The pendulum is made free rotating with the help of ball bearings and the angle of the pendulum is measured using an Inertial Measurement Unit (IMU) sensor. The cart is actuated by two Direct Current (DC) motors and the position of the cart is measured using encoders that generate pulse signals based on the wheel rotation. The control is implemented in a cascade format where an inner loop controller is used to stabilize and balance the pendulum in the inverted position and an outer loop controller is used to control the position of the cart. Both the inner loop and outer loop controllers follow the Proportional-Integral-Derivative (PID) control scheme with some modifications for the inner loop. The system is first mathematically modeled using the Newton-Euler first principles method and based on this model, a controller is designed for specific closed-loop parameters. All of this is implemented on hardware with the help of an Arduino Due microcontroller which serves as the main processing unit for the system.
ContributorsNamasivayam, Vignesh (Author) / Tsakalis, Konstantinos (Thesis advisor) / Rodriguez, Armando (Committee member) / Si, Jennie (Committee member) / Shafique, Md. Ashfaque Bin (Committee member) / Arizona State University (Publisher)
Created2021