Summers, Tyler H.

Permanent URI for this collectionhttps://hdl.handle.net/10735.1/6600

Tyler Summers is an Assistant Professor in the Department of Electrical and Computer Engineering. He also serves as the Principal Investigator of the Control Optimization and Networks Lab. In 2017 he was awarded a $350,000 grant from the U.S. Army's Young Investigator Program to study the best ways to connect sensors and actuators into networks. His research interests include:

  • Control and optimization
  • Power and energy networks
  • Distributed robotics

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Recent Submissions

Now showing 1 - 5 of 5
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    Algorithms for Joint Sensor and Control Nodes Selection in Dynamic Networks
    (Elsevier Ltd, 2019-05-17) Nugroho, S. A.; Taha, A. F.; Gatsis, N.; Summers, Tyler H.; Krishnan, R.; Summers, Tyler H.
    The problem of placing or selecting sensors and control nodes plays a pivotal role in the operation of dynamic networks. This paper proposes optimal algorithms and heuristics to solve the Simultaneous Sensor and Actuator Selection Problem (SSASP) in linear dynamic networks. In particular, a sufficiency condition of static output feedback stabilizability is used to obtain the minimal set of sensors and control nodes needed to stabilize an unstable network. We then show that SSASP can be written as a mixed-integer nonconvex problem. To solve this nonconvex combinatorial problem, three methods based on (i) mixed-integer nonlinear programming, (ii) binary search algorithms, and (iii) simple heuristics are proposed. The first method yields optimal solutions to SSASP—given that some constants are appropriately selected. The second method requires a database of binary sensor/actuator combinations, returns optimal solutions, and necessitates no tuning parameters. The third approach is a heuristic that yields suboptimal solutions but is computationally attractive. The theoretical properties of these methods are discussed and numerical tests on dynamic networks showcase the trade-off between optimality and computational time. ©2019 Elsevier Ltd. All Rights Reserved.
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    Performance Bounds for Optimal Feedback Control in Networks
    (Institute of Electrical and Electronics Engineers Inc.) Summers, Tyler H.; Ruths, Justin; Summers, Tyler H.; Ruths, Justin
    Many important complex networks, including critical infrastructure and emerging industrial automation systems, are becoming increasingly intricate webs of interacting feedback control loops. A fundamental concern is to quantify the control properties and performance limitations of the network as a function of its dynamical structure and control architecture. We study performance bounds for networks in terms of optimal feedback control costs. We provide a set of complementary bounds as a function of the system dynamics and actuator structure. For unstable network dynamics, we characterize a tradeoff between feedback control performance and the number of control inputs, in particular showing that optimal cost can increase exponentially with the size of the network. We also derive a bound on the performance of the worst-case actuator subset for stable networks, providing insight into dynamics properties that affect the potential efficacy of actuator selection. We illustrate our results with numerical experiments that analyze performance in regular and random networks. ©2018 AACC.
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    Simultaneous Sensor and Actuator Selection/Placement through Output Feedback Control
    (Institute of Electrical and Electronics Engineers Inc.) Nugroho, S.; Taha, A. F.; Summers, Tyler H.; Gatsis, N.; Summers, Tyler H.
    In most dynamic networks, it is impractical to measure all of the system states; instead, only a subset of the states are measured through sensors. Consequently, and unlike full state feedback controllers, output feedback control utilizes only the measured states to obtain a stable closed-loop performance. This paper explores the interplay between the selection of minimal number of sensors and actuators (SaA) that yield a stable closed-loop system performance. Through the formulation of the static output feedback control problem, we show that the simultaneous selection of minimal set of SaA is a combinatorial optimization problem with mixed-integer nonlinear matrix inequality constraints. To address the computational complexity, we develop two approaches: The first approach relies on integer/disjunctive programming principles, while the second approach is a simple algorithm that is akin to binary search routines. The optimality of the two approaches is also discussed. Numerical experiments are included showing the performance of the developed approaches. © 2018 AACC.
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    Stochastic Optimal Power Flow Based on Data-Driven Distributionally Robust Optimization
    (Institute of Electrical and Electronics Engineers Inc.) Guo, Yi; Baker, K.; Dall'Anese, E.; Hu, Z.; Summers, Tyler H.; Guo, Yi; Summers, Tyler H.
    We propose a data-driven method to solve a stochastic optimal power flow (OPF) problem based on limited information about forecast error distributions. The objective is to determine power schedules for controllable devices in a power network to balance operational cost and conditional value-at-risk (CVaR) of device and network constraint violations. These decisions include scheduled power output adjustments and reserve policies, which specify planned reactions to forecast errors in order to accommodate fluctuating renewable energy sources. Instead of assuming the uncertainties across the networks follow prescribed probability distributions, we assume the distributions are only observable through a finite training dataset. By utilizing the Wasserstein metric to quantify differences between the empirical data-based distribution and the real data-generating distribution, we formulate a distributionally robust optimization OPF problem to search for power schedules and reserve policies that are robust to sampling errors inherent in the dataset. A multi-stage closed-loop control strategy based on model predictive control (MPC) is also discussed. A simpIe numerical example illustrates inherent tradeoffs between operational cost and risk of constraint violation, and we show how our proposed method offers a data-driven framework to balance these objectives.
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    Concurrent Goal Assignment and Collision-Free Trajectory Generation for Multiple Aerial Robots
    (Elsevier B.V.) Gravell, Benjamin; Summers, Tyler H.; Gravell, Benjamin; Summers, Tyler H.
    We develop computationally tractable methods for concurrent goal assignment and planning of collision-free trajectories for multiple aerial robot systems. Our method first assigns robots to goals to minimize total time-in-motion, assuming straight-line maximum-speed trajectories. By coupling the assignment and trajectory generation, the initial motion plans tend to require only limited collision resolution. We then refine the plans by checking for potential collisions and resolving them using either start time delays or altitude assignment. Numerical experiments using both methods show significant reductions in the total time required for agents to arrive at goals with only modest additional computational effort in comparison to state-of-the-art prior work.

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