Two-Level Fault Detection and Isolation Algorithm for Vehicle Platoon

To deal with the fault of the vehicle platoon, we have established a fault detection and isolation (FDI) system with two-level fault diagnosis architecture. For simplicity, we divide the FDI architecture into two kinds: system failure and component element failure. To detect these faults, we set up the FDI mathematical model of the fleet based on the vehicular spacing, and the sensor FDI model of a certain vehicle. Meanwhile, we construct the state space model of the fleet, and design the residual generator using the space geometry method for system failure. To design the residual generation model of the fleet for component element failure, we strengthen the structure analysis of both the fleet and a certain vehicle. What’s more, to elucidate the factors that cause the change of vehicle distance, the virtual force analysis is introduced. Using the adaptive threshold method, it can enhance both the sensitivity of the FDI system to the residual and the robustness to the disturbance. To promote the vehicle itself and the fleet’s information perception ability, all vehicles (Autonomous Mobile Robots) are equipped with infrared distance measuring sensors, odometers, a pair of incremental optical encoders, and so on. The experimental results show that the proposed method is reliable and efficient for FDI of fleet.

[1]  S. Longhi,et al.  Experimental Validation of a Real-Time Model-Based Sensor Fault Detection and Isolation System for Unmanned Ground Vehicles , 2006, 2006 14th Mediterranean Conference on Control and Automation.

[2]  Mohammad-Ali Massoumnia,et al.  A geometric approach to failure detection and identification in linear systems , 1986 .

[3]  Guo Ge,et al.  Event-triggered platoon control of vehicles with time-varying delay and probabilistic faults , 2017 .

[4]  Jaehoon Jeong,et al.  VCPS: Vehicular Cyber-physical Systems for Smart Road Services , 2014, 2014 28th International Conference on Advanced Information Networking and Applications Workshops.

[5]  Srdjan S. Stankovic,et al.  Decentralized overlapping control of a platoon of vehicles , 2000, IEEE Trans. Control. Syst. Technol..

[6]  R. J. Patton,et al.  Advances in fault diagnosis using analytical redundancy , 1993 .

[7]  Kostas J. Kyriakopoulos,et al.  Sensors fault diagnosis in autonomous mobile robots using observer — Based technique , 2015, 2015 International Conference on Control, Automation and Robotics.

[8]  Rochdi Merzouki,et al.  Model based tracking control using Jerky behavior in platoon of vehicles , 2013, 2013 European Control Conference (ECC).

[9]  Stéphane Ploix,et al.  Fault diagnosis and fault tolerant control , 2007 .

[10]  Tang Tianhao Multi-sensor fault detection and isolation algorithm , 2010 .

[11]  Yonggui Liu,et al.  Cooperative spacing control for Autonomous Vehicle platoon with input delays , 2016, 2016 Chinese Control and Decision Conference (CCDC).

[12]  Guizhen Yu,et al.  An improved cooperative adaptive cruise control (CACC) algorithm considering invalid communication , 2014 .

[13]  Tien-Ngo Manh,et al.  Tracking control for mobile robots with uncertain parameters based on Model Reference Adaptive Control , 2013, 2013 International Conference on Control, Automation and Information Sciences (ICCAIS).

[14]  Jie Xu,et al.  SEED: A Scalable Approach for Cyber-Physical System Simulation , 2016, IEEE Transactions on Services Computing.

[15]  F. Gustafsson The marginalized likelihood ratio test for detecting abrupt changes , 1996, IEEE Trans. Autom. Control..

[16]  Yandong Hou,et al.  Based on space geometry method for actuator fault detection and isolation with disturbances and noises , 2014, CCC 2014.

[17]  Bugong Xu,et al.  Cooperative braking control for a platoon of vehicles under constraint with target stopping positions , 2016, 2016 35th Chinese Control Conference (CCC).

[18]  Kostas J. Kyriakopoulos,et al.  Model based actuator fault diagnosis for a mobile robot , 2014, 2014 IEEE International Conference on Industrial Technology (ICIT).

[19]  Zhen Li,et al.  Efficient Change Verification of Member Cardinality in Cluster-Based VCPS , 2015, 2015 International Conference on Identification, Information, and Knowledge in the Internet of Things (IIKI).

[20]  Sauro Longhi,et al.  Model-Based Sensor Fault Detection and Isolation System for Unmanned Ground Vehicles: Experimental Validation (part II) , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[21]  Jeroen Ploeg,et al.  Co-Design of Controller and Communication Topology for Vehicular Platooning , 2017, IEEE Transactions on Intelligent Transportation Systems.

[22]  Chen Zhi-guo Designing Method of Residual Generator for Multi-sensor Fault Detection and Isolation , 2011 .