Efficient Model Predictive Control for Large-scale Urban Traffic Networks PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Efficient Model Predictive Control for Large-scale Urban Traffic Networks PDF full book. Access full book title Efficient Model Predictive Control for Large-scale Urban Traffic Networks by Shu Lin. Download full books in PDF and EPUB format.
Author: Shu Lin
Publisher:
ISBN: 9789055841363
Category : Predictive control
Languages : en
Pages : 161
Get Book Here
Book Description
Author: Shu Lin
Publisher:
ISBN: 9789055841363
Category : Predictive control
Languages : en
Pages : 161
Get Book Here
Book Description
Author: Yong Shi
Publisher: Springer
ISBN: 3319936980
Category : Computers
Languages : en
Pages : 761
Get Book Here
Book Description
The three-volume set LNCS 10860, 10861 + 10862 constitutes the proceedings of the 18th International Conference on Computational Science, ICCS 2018, held in Wuxi, China, in June 2018. The total of 155 full and 66 short papers presented in this book set was carefully reviewed and selected from 404 submissions. The papers were organized in topical sections named: Part I: ICCS Main Track Part II: Track of Advances in High-Performance Computational Earth Sciences: Applications and Frameworks; Track of Agent-Based Simulations, Adaptive Algorithms and Solvers; Track of Applications of Matrix Methods in Artificial Intelligence and Machine Learning; Track of Architecture, Languages, Compilation and Hardware Support for Emerging ManYcore Systems; Track of Biomedical and Bioinformatics Challenges for Computer Science; Track of Computational Finance and Business Intelligence; Track of Computational Optimization, Modelling and Simulation; Track of Data, Modeling, and Computation in IoT and Smart Systems; Track of Data-Driven Computational Sciences; Track of Mathematical-Methods-and-Algorithms for Extreme Scale; Track of Multiscale Modelling and Simulation Part III: Track of Simulations of Flow and Transport: Modeling, Algorithms and Computation; Track of Solving Problems with Uncertainties; Track of Teaching Computational Science; Poster Papers
Author: Yugeng Xi
Publisher: John Wiley & Sons
ISBN: 1119119588
Category : Technology & Engineering
Languages : en
Pages : 392
Get Book Here
Book Description
This book is a comprehensive introduction to model predictive control (MPC), including its basic principles and algorithms, system analysis and design methods, strategy developments and practical applications. The main contents of the book include an overview of the development trajectory and basic principles of MPC, typical MPC algorithms, quantitative analysis of classical MPC systems, design and tuning methods for MPC parameters, constrained multivariable MPC algorithms and online optimization decomposition methods. Readers will then progress to more advanced topics such as nonlinear MPC and its related algorithms, the diversification development of MPC with respect to control structures and optimization strategies, and robust MPC. Finally, applications of MPC and its generalization to optimization-based dynamic problems other than control will be discussed. Systematically introduces fundamental concepts, basic algorithms, and applications of MPC Includes a comprehensive overview of MPC development, emphasizing recent advances and modern approaches Features numerous MPC models and structures, based on rigorous research Based on the best-selling Chinese edition, which is a key text in China Predictive Control: Fundamentals and Developments is written for advanced undergraduate and graduate students and researchers specializing in control technologies. It is also a useful reference for industry professionals, engineers, and technicians specializing in advanced optimization control technology.
Author: Kjell Hausken
Publisher: Springer
ISBN: 3319116746
Category : Technology & Engineering
Languages : en
Pages : 226
Get Book Here
Book Description
Maximizing reader insights into the interactions between game theory, excessive crowding and safety and security elements, this book establishes a new research angle by illustrating linkages between different research approaches and through laying the foundations for subsequent analysis. Congestion (excessive crowding) is defined in this work as all kinds of flows; e.g., road/sea/air traffic, people, data, information, water, electricity, and organisms. Analysing systems where congestion occurs – which may be in parallel, series, interlinked, or interdependent, with flows one way or both ways – this book puts forward new congestion models, breaking new ground by introducing game theory and safety/security into proceedings. Addressing the multiple actors who may hold different concerns regarding system reliability; e.g. one or several terrorists, a government, various local or regional government agencies, or others with stakes for or against system reliability, this book describes how governments and authorities may have the tools to handle congestion, but that these tools need to be improved whilst additionally ensuring safety and security against various threats. This game-theoretic analysis sets this book apart from the current congestion literature and ensures that the book will be of use to postgraduates, researchers, 3rd/4th-year undergraduates, policy makers, and practitioners.
Author: Marek Pawelczyk
Publisher: Springer Nature
ISBN: 3031351703
Category : Technology & Engineering
Languages : en
Pages : 412
Get Book Here
Book Description
This book introduces the reader to the hottest topics in current control sciences and robotics, as seen by scientists from Poland and other European countries. Volume 1 comprises 37 chapters, which specifically address topics connected to modeling, identification, and analysis of automation systems, to design of control systems, and to fault diagnosis and fault-tolerant control. The contributions were presented during XXI Polish Control Conference, held in Gliwice, Poland, from June 26 to 29, 2023. This book is extremely useful to all persons who want to know the latest trends in automation and robotics.
Author: Juan Andrade Cetto
Publisher: Springer Science & Business Media
ISBN: 3642197302
Category : Technology & Engineering
Languages : en
Pages : 346
Get Book Here
Book Description
The present book includes a set of selected papers from the fourth “International Conference on Informatics in Control Automation and Robotics” (ICINCO 2009), held in Milan, Italy, from 2 to 5 July 2009. The conference was organized in three simultaneous tracks: “Intelligent Control Systems and Optimization”, “Robotics and Automation” and “Systems Modeling, Signal Processing and Control”. The book is based on the same structure. ICINCO received 365 paper submissions, not including those of workshops, from 55 countries, in all continents. After a double blind paper review performed by the Program Committee only 34 submissions were accepted as full papers and thus selected for oral presentation, leading to a full paper acceptance ratio of 9%. Additional papers were accepted as short papers and posters. A further refinement was made after the conference, based also on the assessment of presentation quality, so that this book includes the extended and revised versions of the very best papers of ICINCO 2009. Commitment to high quality standards is a major concern of ICINCO that will be maintained in the next editions of this conference, including not only the stringent paper acceptance ratios but also the quality of the program committee, keynote lectures, workshops and logistics.
Author: Sarah Muraoka Koehler
Publisher:
ISBN:
Category :
Languages : en
Pages : 126
Get Book Here
Book Description
Industrial large-scale control problems present an interesting algorithmic design challenge. A number of controllers must cooperate in real-time on a network of embedded hardware with limited computing power in order to maximize system efficiency while respecting constraints and despite communication delays. Model predictive control (MPC) can automatically synthesize a centralized controller which optimizes an objective function subject to a system model, constraints, and predictions of disturbance. Unfortunately, the computations required by model predictive controllers for large-scale systems often limit its industrial implementation only to medium-scale slow processes. Distributed model predictive control (DMPC) enters the picture as a way to decentralize a large-scale model predictive control problem. The main idea of DMPC is to split the computations required by the MPC problem amongst distributed processors that can compute in parallel and communicate iteratively to find a solution. Some popularly proposed solutions are distributed optimization algorithms such as dual decomposition and the alternating direction method of multipliers (ADMM). However, these algorithms ignore two practical challenges: substantial communication delays present in control systems and also problem non-convexity. This thesis presents two novel and practically effective DMPC algorithms. The first DMPC algorithm is based on a primal-dual active-set method which achieves fast convergence, making it suitable for large-scale control applications which have a large communication delay across its communication network. In particular, this algorithm is suited for MPC problems with a quadratic cost, linear dynamics, forecasted demand, and box constraints. We measure the performance of this algorithm and show that it significantly outperforms both dual decomposition and ADMM in the presence of communication delay. The second DMPC algorithm is based on an inexact interior point method which is suited for nonlinear optimization problems. The parallel computation of the algorithm exploits iterative linear algebra methods for the main linear algebra computations in the algorithm. We show that the splitting of the algorithm is flexible and can thus be applied to various distributed platform configurations. The two proposed algorithms are applied to two main energy and transportation control problems. The first application is energy efficient building control. Buildings represent 40% of energy consumption in the United States. Thus, it is significant to improve the energy efficiency of buildings. The goal is to minimize energy consumption subject to the physics of the building (e.g. heat transfer laws), the constraints of the actuators as well as the desired operating constraints (thermal comfort of the occupants), and heat load on the system. In this thesis, we describe the control systems of forced air building systems in practice. We discuss the "Trim and Respond" algorithm which is a distributed control algorithm that is used in practice, and show that it performs similarly to a one-step explicit DMPC algorithm. Then, we apply the novel distributed primal-dual active-set method and provide extensive numerical results for the building MPC problem. The second main application is the control of ramp metering signals to optimize traffic flow through a freeway system. This application is particularly important since urban congestion has more than doubled in the past few decades. The ramp metering problem is to maximize freeway throughput subject to freeway dynamics (derived from mass conservation), actuation constraints, freeway capacity constraints, and predicted traffic demand. In this thesis, we develop a hybrid model predictive controller for ramp metering that is guaranteed to be persistently feasible and stable. This contrasts to previous work on MPC for ramp metering where such guarantees are absent. We apply a smoothing method to the hybrid model predictive controller and apply the inexact interior point method to this nonlinear non-convex ramp metering problem.
Author: Vladimir Jordan
Publisher: Springer Nature
ISBN: 3031237447
Category : Computers
Languages : en
Pages : 326
Get Book Here
Book Description
This book constitutes the refereed proceedings of the 12th International Conference on High-Performance Computing Systems and Technologies in Scientific Research, Automation of Control and Production, HPCST 2022, held in Barnaul, Russia, during May 20–21, 2022. The 23 full papers included in this book were carefully reviewed and selected from 116 submissions. They were organized in topical sections as follows: hardware for high-performance computing and signal processing; information technologies and computer simulation of physical phenomena; computing technologies in data analysis and decision making; and computing technologies in information security applications.
Author: Linsen Chong
Publisher:
ISBN:
Category :
Languages : en
Pages : 151
Get Book Here
Book Description
In this thesis, we propose novel computationally efficient optimization algorithms that derive effective traffic management strategies to reduce congestion and improve the efficiency of urban transportation systems. The proposed algorithms enable the use of high-resolution yet computationally inefficient urban traffic simulators to address large-scale urban transportation optimization problems in a computationally efficient manner. The first and the second part of this thesis focus on large-scale offline transportation optimization problems with stochastic simulation-based objective functions, analytical differentiable constraints and high-dimensional decision variables. We propose two optimization algorithms to solve these problems. In the first part, we propose a simulation-based metamodel algorithm that combines the use of an analytical stationary traffic network model and a dynamic microscopic traffic simulator. In the second part, we propose a metamodel algorithm that combines the use of an analytical transient traffic network model and the microscopic simulator. In the first part, we use the first metamodel algorithm to solve a large-scale fixed-time traffic signal control problem of the Swiss city of Lausanne with limited simulation runs, showing that the proposed algorithm can derive signal plans that outperform traditional simulation-based optimization algorithms and a commercial traffic signal optimization software. In the second part, we use both algorithms to solve a time-dependent traffic signal control problem of Lausanne, showing that the metamodel with the transient analytical traffic model outperforms that with the stationary traffic model. The third part of this thesis focuses on large-scale online transportation problems, which need to be solved with limited computational time. We propose a new optimization framework that combines the use of a problem-specific model-driven method, i.e., the method proposed in the first part, with a generic data-driven supervised machine learning method. We use this framework to address a traffic responsive control problem of Lausanne. We compare the performance of the proposed framework with the performance of an optimization framework with only the model-driven method and an optimization framework with only the data-driven method, showing that the proposed framework is able to derive signal plans that outperform the signal plans derived by the other two frameworks in most cases.
Author: R. Sathiyaraj
Publisher: CRC Press
ISBN: 1000555909
Category : Computers
Languages : en
Pages : 145
Get Book Here
Book Description
The book emphasizes the predictive models of Big Data, Genetic Algorithm, and IoT with a case study. The book illustrates the predictive models with integrated fuel consumption models for smart and safe traveling. The text is a coordinated amalgamation of research contributions and industrial applications in the field of Intelligent Transportation Systems. The advanced predictive models and research results were achieved with the case studies, deployed in real transportation environments. Features: Provides a smart traffic congestion avoidance system with an integrated fuel consumption model. Predicts traffic in short-term and regular. This is illustrated with a case study. Efficient Traffic light controller and deviation system in accordance with the traffic scenario. IoT based Intelligent Transport Systems in a Global perspective. Intelligent Traffic Light Control System and Ambulance Control System. Provides a predictive framework that can handle the traffic on abnormal days, such as weekends, festival holidays. Bunch of solutions and ideas for smart traffic development in smart cities. This book focuses on advanced predictive models along with offering an efficient solution for smart traffic management system. This book will give a brief idea of the available algorithms/techniques of big data, IoT, and genetic algorithm and guides in developing a solution for smart city applications. This book will be a complete framework for ITS domain with the advanced concepts of Big Data Analytics, Genetic Algorithm and IoT. This book is primarily aimed at IT professionals. Undergraduates, graduates and researchers in the area of computer science and information technology will also find this book useful.
