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E-raamat: Optimal and Robust Scheduling for Networked Control Systems [Taylor & Francis e-raamat]

(Jaguar Land Rover Limited, UK), (Cranfield University, UK), (University of Bristol, UK), (Beijing Institute of Technology, China)
  • Formaat: 280 pages, 9 Tables, black and white; 65 Illustrations, black and white
  • Sari: Automation and Control Engineering
  • Ilmumisaeg: 12-Oct-2017
  • Kirjastus: CRC Press
  • ISBN-13: 9781315215983
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Optimal and Robust Scheduling for Networked Control SystemsSuurem pilt
  • Formaat: 280 pages, 9 Tables, black and white; 65 Illustrations, black and white
  • Sari: Automation and Control Engineering
  • Ilmumisaeg: 12-Oct-2017
  • Kirjastus: CRC Press
  • ISBN-13: 9781315215983
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9781315215983

Optimal and Robust Scheduling for Networked Control Systems tackles the problem of integrating system components—controllers, sensors, and actuators—in a networked control system. It is common practice in industry to solve such problems heuristically, because the few theoretical results available are not comprehensive and cannot be readily applied by practitioners. This book offers a solution to the deterministic scheduling problem that is based on rigorous control theoretical tools but also addresses practical implementation issues. Helping to bridge the gap between control theory and computer science, it suggests that the consideration of communication constraints at the design stage will significantly improve the performance of the control system.

Technical Results, Design Techniques, and Practical Applications

The book brings together well-known measures for robust performance as well as fast stochastic algorithms to assist designers in selecting the best network configuration and guaranteeing the speed of offline optimization. The authors propose a unifying framework for modelling NCSs with time-triggered communication and present technical results. They also introduce design techniques, including for the codesign of a controller and communication sequence and for the robust design of a communication sequence for a given controller. Case studies explore the use of the FlexRay TDMA and time-triggered control area network (CAN) protocols in an automotive control system.

Practical Solutions to Your Time-Triggered Communication Problems

This unique book develops ready-to-use engineering tools for large-scale control system integration with a focus on robustness and performance. It emphasizes techniques that are directly applicable to time-triggered communication problems in the automotive industry and in avionics, robotics, and automated manufacturing.

List of Figures
List of Tables
List of Acronyms
Notation and Symbols
Preface
Author Biographies
1 Introduction
1.1 Overview
2(4)
1.1.1 Real-time control systems
2(1)
1.1.2 Networked control systems
2(3)
1.1.3 Limited communication systems
5(1)
1.2 Motivation
6(4)
2 Control of plants with limited communication
2.1 Introduction
10(2)
2.2 Practical considerations
12(4)
2.2.1 Real-time networks
13(1)
2.2.1.1 Contention-based paradigms
13(2)
2.2.1.2 Contention-free paradigms
15(1)
2.3 Models for NCSs
16(8)
2.3.1 Modeling the contention-based paradigm
16(2)
2.3.2 Modeling the contention-free paradigm
18(1)
2.3.2.1 The non-zero-order-hold case
18(2)
2.3.2.2 The zero-order-hold case
20(2)
2.3.2.3 The model-based case
22(2)
2.4 Control methods
24(4)
2.4.1 Stochastic and robust control
24(2)
2.4.2 Estimation
26(2)
2.5 Scheduling methods
28(4)
2.5.1 Open-loop scheduling
29(1)
2.5.2 Closed-loop scheduling
30(2)
2.6 Scheduling and controller codesign methods
32(6)
2.6.1 Offline scheduling and controller codesign
33(2)
2.6.2 Online scheduling and controller codesign
35(3)
2.7 Structural and stability analysis
38(7)
2.7.1 Stability with delays and packet dropout
38(1)
2.7.1.1 Constant delays
39(1)
2.7.1.2 Variable sampling and delays
39(1)
2.7.1.3 Model-based NCS
40(1)
2.7.1.4 Packet dropout
40(1)
2.7.1.5 Scheduling algorithms
41(2)
2.7.2 Structural properties and stability for the codesign problem
43(2)
2.8 Nonlinear NCSs
45(1)
2.9 Summary
46(1)
3 A general framework for NCS modeling
47(40)
3.1 Introduction
48(1)
3.2 Limited communication and schedulers
49(4)
3.2.1 A time-varying star graph
49(1)
3.2.2 The scheduler
50(3)
3.3 NCS modeling
53(9)
3.3.1 Augmented plant
58(2)
3.3.2 Augmented controller
60(1)
3.3.3 Augmented closed-loop system
61(1)
3.4 NCS without ZOH
62(1)
3.5 Periodicity and discrete-time lifting
63(7)
3.5.1 Elimination of periodicity via lifting
65(5)
3.6 Extension to multi-networks, subnetworks and task scheduling
70(7)
3.6.1 Multi-networks
70(2)
3.6.2 Subnetworks and task scheduling
72(5)
3.7 Multirate systems, a special case of NCSs
77(1)
3.8 NCSs, a special case of switched and delayed systems
78(3)
3.8.1 Switched systems
78(1)
3.8.2 Delayed systems
79(2)
3.9 Application to a vehicle brake-by-wire control system
81(5)
3.9.1 Modeling the bus and task scheduling
83(3)
3.10 Summary
86(1)
4 Controllability and observability
87(24)
4.1 Introduction
87(2)
4.2 NCSs with ZOH
89(11)
4.2.1 Controllability and stabilizability
90(10)
4.3 NCSs without ZOH
100(4)
4.3.1 Stabilizability and detectability
101(3)
4.4 Sampled-data case
104(1)
4.5 Examples
105(5)
4.6 Summary
110(1)
5 Communication sequence optimization
5.1 Introduction
111(1)
5.2 Optimization problem
112(6)
5.2.1 Analysis of properties
113(5)
5.3 Optimization algorithms
118(5)
5.3.1 Genetic Algorithm optimization
119(1)
5.3.2 Particle Swarm Optimization algorithm
120(1)
5.3.2.1 PSO1
120(1)
5.3.2.2 PSO2
121(1)
5.3.3 Discussion on algorithm performance
122(1)
5.4 Constraint handling
123(1)
5.5 Optimizing for Δ
124(1)
5.6 Optimization of NCSs which are multirate systems
124(2)
5.6.1 Bus occupancy as a constraint
125(1)
5.7 Applying the optimization to the vehicle brake-by-wire control system
126(1)
5.8 Summary
127(2)
6 Optimal controller and schedule codesign
6.1 Introduction
129(2)
6.2 Problem formulation
131(6)
6.2.1 NCS model
131(2)
6.2.2 Quadratic cost function
133(4)
6.3 Optimal codesign
137(1)
6.4 Examples
138(3)
6.5 Summary
141(2)
7 Optimal schedule design
7.1 Introduction
143(2)
7.2 Problem formulation
145(5)
7.2.1 NCS model
145(2)
7.2.2 Quadratic cost function
147(1)
7.2.3 A model reference approach for the performance matrix
148(2)
7.3 Optimal design
150(2)
7.4 Examples
152(2)
7.5 Summary
154(2)
8 Robust schedule design
8.1 Introduction
156(1)
8.2 Formulation of an Hoc-based cost for performance
157(5)
8.2.1 NCS model
157(2)
8.2.2 Cost function
159(3)
8.3 Formulation of a discrete Hoc-based cost for robustness and performance
162(2)
8.3.1 NCS model
162(1)
8.3.1.1 Discrete-time lifted controller
162(1)
8.3.1.2 Discrete-time lifted closed-loop system
163(1)
8.3.2 Cost function
164(1)
8.4 Formulation of a sampled-data H∞-based cost for robustness and performance
164(9)
8.4.1 Continuous-time lifting
165(5)
8.4.2 NCS model
170(2)
8.4.3 Cost function
172(1)
8.5 Optimal design with an example
173(2)
8.6 Summary
175(2)
9 Application to an automotive control system
9.1 Introduction
177(1)
9.2 Vehicle model and controller design
178(11)
9.2.1 A cruise-driveline-temperature automotive system
178(1)
9.2.1.1 Linearized longitudinal dynamics
178(1)
9.2.1.2 Driveline dynamics
179(3)
9.2.1.3 Air-conditioning system
182(3)
9.2.2 Design of an observer and an LQR controller with integral action
185(4)
9.3 HIL from TTE systems
189(4)
9.3.1 Modifications to the original HIL
191(2)
9.4 Experiments on the HIL
193(10)
9.4.1 Simulation results
194(2)
9.4.2 HIL quadratic performance results
196(2)
9.4.3 HIL robust performance results
198(5)
9.5 Experiments with FlexRay
203(5)
9.5.1 Brief overview of FlexRay and its development tools
203(2)
9.5.2 Optimal cycle scheduling for FlexRay
205(1)
9.5.3 Results for the FlexRay setup
206(2)
9.6 Summary
208(3)
10 Schedule design for nonlinear NCSs
10.1 Introduction
211(2)
10.2 Discretization of nonlinear affine systems
213(1)
10.3 Sampled-data model of nonlinear NCS
213(3)
10.3.1 Time discretization of a multi-input nonlinear affine system
214(1)
10.3.2 Scheduling of actuator information
215(1)
10.4 Quadratic cost function for NCS performance
216(3)
10.4.1 Cost function for the sampled-data system
216(1)
10.4.2 Removal of periodicity and cost for optimization
217(2)
10.5 Optimization problem
219(2)
10.5.1 Generic optimization problem
219(1)
10.5.2 Lyapunov function for the SOS-approach
220(1)
10.6 An SOS-framework for local cost computation
221(2)
10.7 Example
223(7)
10.8 Summary
230
Bibliography
Index
Dr. Stefano Longo is currently a lecturer in vehicles electrical and electronic systems in the Department of Automotive Engineering at Cranfield University, UK. His work and his research interests gravitate around the problem of implementing advanced control algorithms in hardware, where the controller design and the hardware implementation are not seen as two separate and decoupled problems, but as a single interconnected one.

Dr. Tingli Su is a researcher at the Beijing Institute of Technology. In 2009 she was invited by the University of Bristol as an exchange Ph.D. student, sponsored by the Chinese Scholarship Council during the first year and Jaguar & Land Rover Research the following year. Within the exchange period, she explored the field of nonlinear network control systems (NCS) with communication constraints.

Dr. Guido Herrmann is a reader in control and dynamics in the Department of Mechanical Engineering at the University of Bristol. His research considers the development and application of novel, robust, and nonlinear control systems. He has been collaborating with several institutions in Australia, China, Malaysia, Singapore, and the USA and has been working with companies such as Western Digital and Jaguar Land Rover.

Dr. Phil Barber, BSc Ph.D. CEng MIET FIMechE, is currently the technical specialist for chassis systems and vehicle capability research at Jaguar Land Rover. Current interests include vehicle dynamics, state estimation, distributed and networked systems for real-time vehicle control, and regenerative braking. A member of the Institution of Engineering and Technology, he serves on the executive team of their Automotive and Road Transport Systems Network.
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