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Cyber-Physical Vehicle Systems: Methodology and Applications [Mīkstie vāki]

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This book studies the design optimization, state estimation, and advanced control methods for cyber-physical vehicle systems (CPVS) and their applications in real-world automotive systems.

First, in Chapter 1, key challenges and state-of-the-art of vehicle design and control in the context of cyber-physical systems are introduced. In Chapter 2, a cyber-physical system (CPS) based framework is proposed for high-level co-design optimization of the plant and controller parameters for CPVS, in view of vehicle's dynamic performance, drivability, and energy along with different driving styles. System description, requirements, constraints, optimization objectives, and methodology are investigated. In Chapter 3, an Artificial-Neural-Network-based estimation method is studied for accurate state estimation of CPVS. In Chapter 4, a high-precision controller is designed for a safety-critical CPVS. The detailed control synthesis and experimental validation are presented. The application results presented throughout the book validate the feasibility and effectiveness of the proposed theoretical methods of design, estimation, control, and optimization for cyber physical vehicle systems.

Preface ix
1 Introductions
1(4)
2 Co-Design Optimization for Cyber-Physical Vehicle System
5(18)
2.1 Problem Formulation
5(6)
2.1.1 Hierarchical Optimization Methodology
5(1)
2.1.2 System Description
5(2)
2.1.3 Driving Event
7(1)
2.1.4 D riving Style Recognition
7(2)
2.1.5 Requirements for the Design and Optimization of CPVS
9(1)
2.1.6 Constraints for Vehicle Design and Optimization
10(1)
2.2 System Modeling and Validation
11(2)
2.2.1 Electric Powertrain system
11(1)
2.2.2 Blended Brake System
12(1)
2.2.3 Dynamic Model of the Vehicle and Tyre
12(1)
2.2.4 Experimental Validation
13(1)
2.3 Controller Design for Different Driving Styles
13(3)
2.3.1 High-Level Controller Architecture
13(1)
2.3.2 Low-Level Controller for Different Driving Styles
14(2)
2.4 Driving-Style-Based Performance Exploration and Parameter Optimization
16(2)
2.4.1 Design Space Exploration
16(1)
2.4.2 Performance Exploration Methodology
16(1)
2.4.3 Driving-Style-Oriented Multi-Objective Optimization
16(2)
2.5 Optimization Results and Analysis
18(5)
2.5.1 Optimization Results for the Aggressive Driving Style
19(1)
2.5.2 Optimization Results of the Moderate Driving Style
19(2)
2.5.3 Optimization Results of the Conservative Driving Style
21(1)
2.5.4 Comparison and Discussion
21(2)
3 State Estimation of Cyber-Physical Vehicle Systems
23(20)
3.1 Multilayer Artificial Neural Networks Architecture
25(2)
3.1.1 System Architecture
25(1)
3.1.2 Multilayer Feed-Forward Neural Network
25(2)
3.2 Standard Backpropagation Algorithm
27(3)
3.3 Levenberg--Marquardt Backpropagation
30(3)
3.4 Experimental Testing and Data Collection
33(5)
3.4.1 Testing Vehicle and Scenario
33(2)
3.4.2 Data Collection and Processing
35(1)
3.4.3 Feature Selection and Model Training
35(3)
3.5 Experiment Results and Discussions
38(5)
3.5.1 Results of the ANN-Based Braking Pressure Estimation
38(2)
3.5.2 Importance Analysis of the Selected Features
40(1)
3.5.3 Comparison of Estimation Results with Different Learning Methods
40(3)
4 Controller Design of Cyber-Physical Vehicle Systems
43(18)
4.1 Description of the Newly Proposed BBW System
45(2)
4.2 Control Algorithm Design for Hydraulic Pump-Based Pressure Modulation
47(2)
4.3 Control Algorithm Design for Closed-Loop Pressure-Difference-Limiting Modulation
49(5)
4.3.1 Linear Modulation of On/Off Valve
49(4)
4.3.2 Closed-Loop Pressure-Difference-Limiting Control
53(1)
4.4 Hardware-in-the-Loop Test Results
54(7)
4.4.1 Comparison of HPBPM and CLPDL Control
56(3)
4.4.2 Brake Blending Algorithm Based on CLPDL Modulation
59(2)
5 Conclusions
61(2)
References 63(10)
Authors' Biographies 73