Fault Detection and Isolation, Fault Tolerant Control for Autonomous and Transport Vehicles

A special issue of Actuators (ISSN 2076-0825). This special issue belongs to the section "Actuators for Land Transport".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 1806

Special Issue Editor


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Guest Editor
Energy and Control of Transportation Systems Laboratory, Graduate School of Aeronautical, Aerospace, Automobile, Railway Engineering (ESTACA), 53061 Laval, France
Interests: power electronics; renewable energies; nonlinear dynamics; fault diagnosis
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Special Issue Information

Dear Colleagues,

Autonomous vehicles have captured the attention of multi-disciplinary researchers for a number of years. Considering the safety, comfort and convenience of passengers, there is a pressing need to develop more advanced vehicle state estimation, motion control, and diagnosis technologies. Real-time estimation of the vehicle state is foundational to achieving effective vehicle control schemes. Most autonomous and transport vehicles are based on electric machines and their corresponding power electronics systems. Inverters and converters contain ever greater numbers of power electronics switches: this may subsequently affect their reliability. Therefore, fault detection and location are essential to improving AV reliability and ensuring continuous operation. With advanced tools and technologies, we can create a safer, more efficient, and more sustainable future for automobiles and humankind.

This Special Issue will focus on publishing novel approaches to detecting and localizing the faults of autonomous and transport vehicles. We also encourage submissions on advanced state estimation and vehicle dynamics-based control strategies for autonomous vehicles. Original and innovative research studies from both academic and industrial research teams are welcomed. Potential topics include, but are not limited to:

  • vehicle state estimation
  • chassis control
  • active suspension control
  • fault detection and isolation
  • fault diagnosis and fault-tolerant control
  • vehicle motion control
  • active and semiactive vibration control
  • smart materials and structures

Dr. Cristina Morel
Guest Editor

Manuscript Submission Information

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Keywords

  • detection and isolation faults
  • fault tolerant strategy
  • inverter, converter and machine
  • vehicle dynamics
  • vehicle control
  • integrated chassis control
  • suspension control

Published Papers (2 papers)

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Research

18 pages, 6690 KiB  
Article
Augmented Kalman Estimator and Equivalent Replacement Based Taylor Series-LQG Control for a Magnetorheological Semi-Active Suspension
by Juncheng Wang, Mingyao Zhou, Jiacheng Tong, Jinyu Liu and Shian Chen
Actuators 2024, 13(4), 138; https://doi.org/10.3390/act13040138 - 08 Apr 2024
Viewed by 292
Abstract
This research presents an augmented Kalman estimator and an equivalent replacement-based Taylor series (ERBTS)-linear quadratic Gaussian (LQG) control strategy to cope with the control accuracy and response delay of magnetorheological (MR) dampers for vehicle semi-active suspensions. The parameters in the MR model are [...] Read more.
This research presents an augmented Kalman estimator and an equivalent replacement-based Taylor series (ERBTS)-linear quadratic Gaussian (LQG) control strategy to cope with the control accuracy and response delay of magnetorheological (MR) dampers for vehicle semi-active suspensions. The parameters in the MR model are identified from experimental measurements. Then, two main sources of control error, namely, modelling error and real-time variety of the MR damper output force, are defined as an integrated compound real-time variety. Subsequently, they are written into a differential equation with characteristics of the minimum system to augment the state equation of the semi-active suspension system. The augmented Kalman estimator is constructed to estimate the abovementioned compound real-time variety. To calculate an acceptable time-delay compensation predictive control force, an equivalent operation is implemented beforehand in the suspension comprehensive performance index by replacing a part of the squared time-delay control force with the corresponding predictive control force. Simulation results verify the effectiveness of the proposed augmented Kalman estimator, and the newly developed ERBTS-LQG controller almost achieves control effectiveness of the ideal time delay free semi-active suspension. Full article
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14 pages, 2395 KiB  
Article
Fault Diagnosis of Mine Truck Hub Drive System Based on LMD Multi-Component Sample Entropy Fusion and LS-SVM
by Le Xu, Wei Li, Bo Zhang, Yubin Zhu and Chaonan Lang
Actuators 2023, 12(12), 468; https://doi.org/10.3390/act12120468 - 16 Dec 2023
Viewed by 1231
Abstract
As the main transportation equipment in ore mining, the wheel drive system of mining trucks plays a crucial role in the transportation capacity of mining trucks. The internal components of the hub drive system are mainly composed of bearings, gears, etc. The vibration [...] Read more.
As the main transportation equipment in ore mining, the wheel drive system of mining trucks plays a crucial role in the transportation capacity of mining trucks. The internal components of the hub drive system are mainly composed of bearings, gears, etc. The vibration signals caused during operation are nonlinear and nonstationary complex signals, and there may be more than one factor that causes faults, which causes certain difficulties for the fault diagnosis of the hub drive system. A fault diagnosis method based on local mean decomposition (LMD) multi-component sample entropy fusion and LS-SVM is proposed to address this issue. Firstly, the LMD method is used to decompose the vibration signals in different states to obtain a finite number of PF components. Then, based on the typical correlation analysis method, the distribution characteristics and correlation coefficients of vibration signals in the frequency domain under different states are calculated, and effective PF multi-component sample entropy features are constructed. Finally, the LS-SVM multi-fault classifier is used to train and test the extracted multi-component sample entropy features to verify the effectiveness of the method. The experimental results show that, even in small-sample data, the LMD multi-component sample entropy fusion and LS-SVM method can accurately extract fault features of vibration signals and complete classification, achieving fault diagnosis of wheel drive systems. Full article
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