Ebook: Real-time road profile identification and monitoring: theory and application

2. System modeling -- 2.1 Road profile modeling -- 2.1.1 Definition of road profile with the same PSD structure -- 2.1.2 Definition of road profile with different PSD structure -- 2.1.3 IRI and its correlation to ISO ondex -- 2.1.4 Road profile generation -- 2.2 Vehicle system modeling -- 2.2.1 Nonlinear Macpherson suspension model -- 2.2.2 Suspension system linearization -- 2.2.3 Controllable damper model -- 2.3 Summary.;Acknowledgments -- Nomenclature -- 1. Introduction -- 1.1 Motivation -- 1.2 Road estimation review -- 1.2.1 Direct measurements -- 1.2.2 Non-contact measurements -- 1.2.3 Response-based estimation -- 1.3 Summary.;6. Suspension predictive control based on road estimation -- 6.1 Hybrid model predictive control -- 6.1.1 Model predictive control -- 6.1.2 Hybrid system -- 6.1.3 Hybrid model predictive control -- 6.2 Optimal predictive control -- 6.2.1 Predictive control for quarter vehicle model -- 6.2.2 Predictive control for a half vehicle model -- 6.3 Simulations -- 6.3.1 Simulation settings -- 6.3.2 Simulation results for a quarter vehicle model -- 6.3.3 Simulation results for half vehicle model -- 6.4 Summary.;Ever stringent vehicle safety legislation and consumer expectations inspire the improvement of vehicle dynamic performance, which result in a rising number of control strategies for vehicle dynamics that rely on driving conditions. Road profiles, as the primary excitation source of vehicle systems, play a critical role in vehicle dynamics and also in public transportation. Knowledge of precise road conditions can thus be of great assistance for vehicle companies and government departments to develop proper dynamic control algorithms, and to fix roads in a timely manner and at the minimum cost, respectively. As a result, developing easy-to-use and accurate road estimation methods are of great importance in terms of reducing the cost related to vehicles and road maintenance as well as improving passenger comfort and handling capacity. A few books have already been published on road profile modeling and the influence of road unevenness on vehicle response. However, there is still room to discuss road assessment methods based on vehicle response and how road conditions can be used to improve vehicle dynamics. In this book, we use several generalized vehicle models to demonstrate the concepts, methods, and applications of vehicle response-based road estimation algorithms. In addition, necessary tools, algorithms, and methods are illustrated, and the benefits of the road estimation algorithms are evaluated. Furthermore, several case studies of controllable suspension systems to improve vehicle vertical dynamics are presented.;7. Conclusions -- References -- Authors' biographies.;4. Model-based road estimation algorithms -- 4.1 Transfer function-based road classification algorithms -- 4.1.1 Structure of speed independent road classification algorithm -- 4.1.2 Data processing procedure -- 4.1.3 Discussion on the input selection of RF classifier -- 4.1.4 Simulation results for varying velocity scenario -- 4.1.5 Simulation results for noisy measurement -- 4.1.6 Road classification when considering tire dynamics -- 4.1.7 Experimental validation -- 4.2 Observer-based road profile estimation -- 4.2.1 Observer structure -- 4.2.2 AKF-ASTO design -- 4.2.3 Simulation results -- 4.3 Summary.;5. Road adaptive hybrid suspension control -- 5.1 Correlation between suspension parameter and road excitation -- 5.1.1 Analytical Expressions of Vehicle Responses -- 5.1.2 Correlation Between Suspension System and Excitation Conditions -- 5.2 Multi-Objective Optimization Problem and Solution -- 5.2.1 MOOP -- 5.2.2 Solution of MOOP -- 5.3 Road Adaptive Hybrid Suspension Controller -- 5.3.1 Hybrid Control and its Analytical Expressions -- 5.3.2 Road Adaptive Hybrid Controller -- 5.4 Summary.;3. Data-driven road classification algorithms -- 3.1 The difference of system responses on various road levels -- 3.2 Features definition -- 3.3 Road classification algorithm -- 3.3.1 Overall structure -- 3.3.2 Signal pre-processing -- 3.3.3 Feature reduction -- 3.3.4 PNN classifier -- 3.4 Simulation settings and results -- 3.4.1 Simulation settings 3.4.2 Simulation results -- 3.4.3 Comparison with other methods -- 3.5 Summary.