<span>Disciplines can no longer be isolated. Technology has rapidly evolved to the point that driverless vehicles have truly become a reality and are not something out of a futuristic exhibition from the 1950s. However, engineers and researchers working on the development of autonomous vehicles cann
Cyber-Physical Vehicle Systems: Methodology and Applications (Synthesis Lectures on Advances in Automotive Technology)
β Scribed by Chen Lv, Yang Xing, Junzhi Zhang
- Publisher
- MORGAN & CLAYPOOL
- Year
- 2020
- Tongue
- English
- Leaves
- 86
- Series
- Synthesis Lectures on Advances in Automotive Technologies
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
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.
β¦ Table of Contents
Preface
Introductions
Co-Design Optimization for Cyber-Physical Vehicle System
Problem Formulation
Hierarchical Optimization Methodology
System Description
Driving Event
Driving Style Recognition
Requirements for the Design and Optimization of CPVS
Constraints for Vehicle Design and Optimization
System Modeling and Validation
Electric Powertrain system
Blended Brake System
Dynamic Model of the Vehicle and Tyre
Experimental Validation
Controller Design for Different Driving Styles
High-Level Controller Architecture
Low-Level Controller for Different Driving Styles
Driving-Style-Based Performance Exploration and Parameter Optimization
Design Space Exploration
Performance Exploration Methodology
Driving-Style-Oriented Multi-Objective Optimization
Optimization Results and Analysis
Optimization Results for the Aggressive Driving Style
Optimization Results of the Moderate Driving Style
Optimization Results of the Conservative Driving Style
Comparison and Discussion
State Estimation of Cyber-Physical Vehicle Systems
Multilayer Artificial Neural Networks Architecture
System Architecture
Multilayer Feed-Forward Neural Network
Standard Backpropagation Algorithm
LevenbergβMarquardt Backpropagation
Experimental Testing and Data Collection
Testing Vehicle and Scenario
Data Collection and Processing
Feature Selection and Model Training
Experiment Results and Discussions
Results of the ANN-Based Braking Pressure Estimation
Importance Analysis of the Selected Features
Comparison of Estimation Results with Different Learning Methods
Controller Design of Cyber-Physical Vehicle Systems
Description of the Newly Proposed BBW System
Control Algorithm Design for Hydraulic Pump-Based Pressure Modulation
Control Algorithm Design for Closed-Loop Pressure-Difference-Limiting Modulation
Linear Modulation of On/Off Valve
Closed-Loop Pressure-Difference-Limiting Control
Hardware-in-the-Loop Test Results
Comparison of HPBPM and CLPDL Control
Brake Blending Algorithm Based on CLPDL Modulation
Conclusions
References
Authors' Biographies
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