Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Optimal Control (2)
- Computational Fluid Dynamics (1)
- Computational Methods (1)
- Computational neuroscience (1)
- Demand response (1)
-
- Distributed dynamical systems (1)
- Dynamical Systems (1)
- Ensemble Control (1)
- Ensemble control (1)
- Infinite-Dimensional System (1)
- Large-Scale Systems (1)
- Linear Systems (1)
- Model Predictive Control (1)
- Model Reduction (1)
- Neural dynamics (1)
- Neural networks (1)
- Neurocontrol (1)
- Normative modeling (1)
- PDE Optimization (1)
- Pattern formation (1)
- Pinning Control (1)
- Smart grid (1)
- Thermal Comfort (1)
Articles 1 - 5 of 5
Full-Text Articles in Electrical and Electronics
Algebraic, Computational, And Data-Driven Methods For Control-Theoretic Analysis And Learning Of Ensemble Systems, Wei Miao
McKelvey School of Engineering Theses & Dissertations
In this thesis, we study a class of problems involving a population of dynamical systems under a common control signal, namely, ensemble systems, through both control-theoretic and data-driven perspectives. These problems are stemmed from the growing need to understand and manipulate large collections of dynamical systems in emerging scientific areas such as quantum control, neuroscience, and magnetic resonance imaging. We examine fundamental control-theoretic properties such as ensemble controllability of ensemble systems and ensemble reachability of ensemble states, and propose ensemble control design approaches to devise control signals that steer ensemble systems to desired profiles. We show that these control-theoretic properties …
Elucidating And Leveraging Dynamics-Function Relationships In Neural Circuits Through Modeling And Optimal Control, Sruti Mallik
Elucidating And Leveraging Dynamics-Function Relationships In Neural Circuits Through Modeling And Optimal Control, Sruti Mallik
McKelvey School of Engineering Theses & Dissertations
A fundamental research question in neuroscience pertains to understanding how neural networks through their activity encode and decode information. In this research, we build on methods from theoretical domains such as control theory, dynamical systems analysis and reinforcement learning to investigate such questions. Our objective is two-fold: first, to use methods from engineering to identify specific objectives that neural circuits might be optimizing through their spatiotemporal activity patterns, and second, to draw motivation from neuroscience to formulate new engineering principles such as synthesis of dynamical networks for decentralized control applications. We specifically take a top-down, optimization driven approach in our …
Robust Engineering Of Dynamic Structures In Complex Networks, Walter Botongo Bomela
Robust Engineering Of Dynamic Structures In Complex Networks, Walter Botongo Bomela
McKelvey School of Engineering Theses & Dissertations
Populations of nearly identical dynamical systems are ubiquitous in natural and engineered systems, in which each unit plays a crucial role in determining the functioning of the ensemble. Robust and optimal control of such large collections of dynamical units remains a grand challenge, especially, when these units interact and form a complex network. Motivated by compelling practical problems in power systems, neural engineering and quantum control, where individual units often have to work in tandem to achieve a desired dynamic behavior, e.g., maintaining synchronization of generators in a power grid or conveying information in a neuronal network; in this dissertation, …
Extrinsic And Intrinsic Control Of Integrative Processes In Neural Systems, Anirban Nandi
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 …