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＜電子ブック＞
Traffic Congestion Control by PDE Backstepping / by Huan Yu, Miroslav Krstic
(Systems & Control: Foundations & Applications. ISSN:23249757)

版	1st ed. 2022.
出版者	(Cham : Springer International Publishing : Imprint: Birkhäuser)
出版年	2022
本文言語	英語
大きさ	XVII, 356 p : online resource
著者標目	*Yu, Huan author Krstic, Miroslav author SpringerLink (Online service)
件　名	LCSH:System theory LCSH:Control theory LCSH:Differential equations LCSH:Control engineering FREE:Systems Theory, Control FREE:Differential Equations FREE:Control and Systems Theory
一般注記	Introduction -- Backstepping for Coupled Hyperbolic PDEs -- Part I: Basic Backstepping Control of Freeway Traffic -- Stabilization of ARZ Model with Known Parameters and Fundamental Diagram -- Observer Validation on Freeway Data -- Adaptive Control of ARZ Traffic Model -- Event-Triggered Control of ARZ Model -- Comparison of Backstepping with Reinforcement Learning -- Part II: Advanced Backstepping for Traffic Flows -- Two-Lane Traffic Control -- Two-Class Traffic Control -- Control of Two-Cascaded Freeway Segments -- Estimation of Freeway Diverge Flows -- Control under Routing-Induced Instability -- Bilateral Regulation of Moving Shock Position -- Extremum Seeking of Downstream Bottleneck This monograph explores the design of controllers that suppress oscillations and instabilities in congested traffic flow using PDE backstepping methods. The first part of the text is concerned with basic backstepping control of freeway traffic using the Aw-Rascle-Zhang (ARZ) second-order PDE model. It begins by illustrating a basic control problem – suppressing traffic with stop-and-go oscillations downstream of ramp metering – before turning to the more challenging case for traffic upstream of ramp metering. The authors demonstrate how to design state observers for the purpose of stabilization using output-feedback control. Experimental traffic data are then used to calibrate the ARZ model and validate the boundary observer design. Because large uncertainties may arise in traffic models, adaptive control and reinforcement learning methods are also explored in detail. Part II then extends the conventional ARZ model utilized until this point in orderto address more complex traffic conditions: multi-lane traffic, multi-class traffic, networks of freeway segments, and driver use of routing apps. The final chapters demonstrate the use of the Lighthill-Whitham-Richards (LWR) first-order PDE model to regulate congestion in traffic flows and to optimize flow through a bottleneck. In order to make the text self-contained, an introduction to the PDE backstepping method for systems of coupled first-order hyperbolic PDEs is included. Traffic Congestion Control by PDE Backstepping is ideal for control theorists working on control of systems modeled by PDEs and for traffic engineers and applied scientists working on unsteady traffic flows. It will also be a valuable resource for researchers interested in boundary control of coupled systems of first-order hyperbolic PDEs HTTP:URL=https://doi.org/10.1007/978-3-031-19346-0

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