4.7 Article

Hybrid physics-data-driven online modelling: Framework, methodology and application to electric vehicles

Related references

Note: Only part of the references are listed.
Article Engineering, Civil

Data-Driven Based Cruise Control of Connected and Automated Vehicles Under Cyber-Physical System Framework

Tao Zhang et al.

Summary: This article introduces the application of cyber-physical systems in intelligent transportation and presents a distributed CPS application for the safety-following driving control of connected and automated vehicles. By building vehicle behavior prediction models and dynamic driving system models using historical data, as well as proposing a new range strategy, the safety of vehicles can be improved.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2021)

Article Automation & Control Systems

On Prediction Model Fidelity in Explicit Nonlinear Model Predictive Vehicle Stability Control

Mathias Metzler et al.

Summary: This study discusses vehicle stability control using explicit nonlinear model predictive control (NMPC) and investigates how prediction model accuracy affects controller performance. The results show that features such as tire forces, load transfers, and tire force coupling have a significant impact on controller performance, while tire model peak and stiffness factors have negligible effects.

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY (2021)

Article Engineering, Mechanical

Modelling longitudinal vehicle dynamics with neural networks

Mauro Da Lio et al.

VEHICLE SYSTEM DYNAMICS (2020)

Article Engineering, Electrical & Electronic

Cyber-Physical Scheduling for Predictable Reliability of Inter-Vehicle Communications

Chuan Li et al.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2020)

Article Automation & Control Systems

Real-time neural observer-based controller for unknown nonlinear discrete delayed systems

Jorge D. Rios et al.

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL (2020)

Article Automation & Control Systems

Fault Tolerant Consensus for Vehicle State Estimation: A Cyber-Physical Approach

Ehsan Hashemi et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2019)

Article Automation & Control Systems

On Model-Free Adaptive Control and Its Stability Analysis

Zhongsheng Hou et al.

IEEE TRANSACTIONS ON AUTOMATIC CONTROL (2019)

Article Engineering, Electrical & Electronic

Dynamic State Estimation With Model Uncertainties Using H-infinity Extended Kalman Filter

Junbo Zhao

IEEE TRANSACTIONS ON POWER SYSTEMS (2018)

Proceedings Paper Automation & Control Systems

Neural Sliding Mode Control for Induction Motors Using Rapid Control Prototyping

Eduardo Quintero-Manriquez et al.

IFAC PAPERSONLINE (2017)

Article Transportation Science & Technology

A method of vehicle motion prediction and collision risk assessment with a simulated vehicular cyber physical system

Chaozhong Wu et al.

TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2014)

Article Computer Science, Information Systems

From model-based control to data-driven control: Survey, classification and perspective

Zhong-Sheng Hou et al.

INFORMATION SCIENCES (2013)

Article Automation & Control Systems

A Novel Data-Driven Control Approach for a Class of Discrete-Time Nonlinear Systems

Zhongsheng Hou et al.

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY (2011)

Article Computer Science, Artificial Intelligence

Data-Driven Model-Free Adaptive Control for a Class of MIMO Nonlinear Discrete-Time Systems

Zhongsheng Hou et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS (2011)

Article Engineering, Mechanical

Modelling nonlinear vehicle dynamics with neural networks

Simon J. Rutherford et al.

INTERNATIONAL JOURNAL OF VEHICLE DESIGN (2010)

Article Engineering, Electrical & Electronic

H-infinity filtering for a class of nonlinear discrete-time systems based on unscented transform

Wenling Li et al.

SIGNAL PROCESSING (2010)

Article Computer Science, Artificial Intelligence

Discrete-time adaptive backstepping nonlinear control via high-order neural networks

Alma Y. Alanis et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS (2007)