Randy Cogill

Bio: Randy Cogill is an academic researcher from University of Virginia. The author has contributed to research in topics: Markov decision process & Optimization problem. The author has an hindex of 16, co-authored 48 publications receiving 798 citations. Previous affiliations of Randy Cogill include IBM & Stanford University.

A Constant Factor Approximation Algorithm for Event-Based Sampling

Abstract: We consider a control system in which sensor data is transmitted from the plant to a receiver over a communication channel, and the receiver uses the data to estimate the state of the plant. Using a feedback policy to choose when to transmit data, the goal is to schedule transmissions to balance a trade-off between communication rate and estimation error. Computing an optimal policy for this problem is generally computationally intensive. Here we provide a simple algorithm for computing a suboptimal policy for scheduling state transmissions which incurs a cost within a factor of six of the optimal achievable cost.

Event-based control using quadratic approximate value functions

Abstract: In this paper we consider several problems involving control with limited actuation and sampling rates. Event-based control has emerged as an attractive approach for addressing the problems of control system design under rate limitations. In event-based control, a system is actuated or a control signal is changed only when certain events occur. For example, a control signal might be applied only when some measure of deviation of the system state from equilibrium is exceeded. Thus, control action is only applied when it is needed, keeping control performance satisfactory while reducing the rate that the system must be sensed and actuated.

Convexity of optimal control over networks with delays and arbitrary topology

Abstract: We consider the design of optimal controllers for a networked system (or a spatio-temporal system), where the dynamics of each subsystem may affect those of other subsystems with some propagation delays, and the controllers may communicate with each other with some transmission delays. We show that if a simple condition holds, then the optimal control problem may be recast as a convex optimisation problem. This is shown to unify and broadly generalise the class of such systems amenable to convex synthesis. When we consider the special case of spatially invariant systems, this again broadly generalises the known class of tractable problems.

An approximate dynamic programming approach to decentralized control of stochastic systems

Abstract: We consider the problem of computing decentralized control policies for stochastic systems with finite state and action spaces. Synthesis of optimal decentralized policies for such problems is known to be NP-hard [1]. Here we focus on methods for efficiently computing meaningful suboptimal decentralized control policies. The algorithms we present here are based on approximation of optimal Q-functions. We show that the performance loss associated with choosing decentralized policies with respect to an approximate Q-function is related to the approximation error.

Model Reduction Methods for Rotor Dynamic Analysis: A Survey and Review

Abstract: The focus of this literature survey and review is model reduction methods and their application to rotor dynamic systems. Rotor dynamic systems require careful consideration in their dynamic models as they include unsymmetric stiffness, localized nonproportional damping, and frequency-dependent gyroscopic effects. The literature reviewed originates from both controls and mechanical systems analysis and has been previously applied to rotor systems. This survey discusses the previous literature reviews on model reduction, reduction methods applied to rotor systems, the current state of these reduction methods in rotor dynamics, and the ability of the literature to reduce the complexities of large order rotor dynamic systems but allow accurate solutions.

Fundamentals of Queueing Theory

Abstract: Praise for the Third Edition: "This is one of the best books available. Its excellent organizational structure allows quick reference to specific models and its clear presentation . . . solidifies the understanding of the concepts being presented."IIE Transactions on Operations EngineeringThoroughly revised and expanded to reflect the latest developments in the field, Fundamentals of Queueing Theory, Fourth Edition continues to present the basic statistical principles that are necessary to analyze the probabilistic nature of queues. Rather than presenting a narrow focus on the subject, this update illustrates the wide-reaching, fundamental concepts in queueing theory and its applications to diverse areas such as computer science, engineering, business, and operations research.This update takes a numerical approach to understanding and making probable estimations relating to queues, with a comprehensive outline of simple and more advanced queueing models. Newly featured topics of the Fourth Edition include:Retrial queuesApproximations for queueing networksNumerical inversion of transformsDetermining the appropriate number of servers to balance quality and cost of serviceEach chapter provides a self-contained presentation of key concepts and formulae, allowing readers to work with each section independently, while a summary table at the end of the book outlines the types of queues that have been discussed and their results. In addition, two new appendices have been added, discussing transforms and generating functions as well as the fundamentals of differential and difference equations. New examples are now included along with problems that incorporate QtsPlus software, which is freely available via the book's related Web site.With its accessible style and wealth of real-world examples, Fundamentals of Queueing Theory, Fourth Edition is an ideal book for courses on queueing theory at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners who analyze congestion in the fields of telecommunications, transportation, aviation, and management science.

An introduction to event-triggered and self-triggered control

Abstract: Recent developments in computer and communication technologies have led to a new type of large-scale resource-constrained wireless embedded control systems. It is desirable in these systems to limit the sensor and control computation and/or communication to instances when the system needs attention. However, classical sampled-data control is based on performing sensing and actuation periodically rather than when the system needs attention. This paper provides an introduction to event- and self-triggered control systems where sensing and actuation is performed when needed. Event-triggered control is reactive and generates sensor sampling and control actuation when, for instance, the plant state deviates more than a certain threshold from a desired value. Self-triggered control, on the other hand, is proactive and computes the next sampling or actuation instance ahead of time. The basics of these control strategies are introduced together with a discussion on the differences between state feedback and output feedback for event-triggered control. It is also shown how event- and self-triggered control can be implemented using existing wireless communication technology. Some applications to wireless control in process industry are discussed as well.

Applied Probability and Queues

Abstract: (1987). Applied Probability and Queues. Journal of the Operational Research Society: Vol. 38, No. 11, pp. 1095-1096.

Periodic event-triggered control for nonlinear systems

Abstract: Event-triggered control (ETC) is a control strategy that is especially suited for applications where communication resources are scarce. By updating and communicating sensor and actuator data only when needed for stability or performance purposes, ETC is capable of reducing the amount of communications, while still retaining a satisfactory closed-loop performance. In this paper, an ETC strategy is proposed by striking a balance between conventional periodic sampled-data control and ETC, leading to so-called periodic event-triggered control (PETC). In PETC, the event-triggering condition is verified periodically and at every sampling time it is decided whether or not to compute and to transmit new measurements and new control signals. The periodic character of the triggering conditions leads to various implementation benefits, including a minimum inter-event time of (at least) the sampling interval of the event-triggering condition. The PETC strategies developed in this paper apply to both static state-feedback and dynamical output-based controllers, as well as to both centralized and decentralized (periodic) event-triggering conditions. To analyze the stability and the L2-gain properties of the resulting PETC systems, three different approaches will be presented based on 1) impulsive systems, 2) piecewise linear systems, and 3) perturbed linear systems. Moreover, the advantages and disadvantages of each of the three approaches will be discussed and the developed theory will be illustrated using a numerical example.