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Proceedings ArticleDOI

Output-Only Fault Detection and Mitigation of Networks of Autonomous Vehicles

TL;DR: In this article, the authors proposed a transmissibility-based health monitoring approach for fault detection in an autonomous vehicle platoon, where a sliding mode controller is used to mitigate the failure of either a physical component of a vehicle or a communication link between two vehicles.
Abstract: An autonomous vehicle platoon is a network of autonomous vehicles that communicate together to move in a desired way. One of the greatest threats to the operation of an autonomous vehicle platoon is the failure of either a physical component of a vehicle or a communication link between two vehicles. This failure affects the safety and stability of the autonomous vehicle platoon. Transmissibility-based health monitoring uses available sensor measurements for fault detection under unknown excitation and unknown dynamics of the network. After a fault is detected, a sliding mode controller is used to mitigate the fault. Different fault scenarios are considered including vehicle internal disturbances, cyber attacks, and communication delays. We apply the proposed approach to a bond graph model of the platoon and an experimental setup consisting of three autonomous robots.
Citations
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Journal ArticleDOI
TL;DR: In this article , the authors use transmissibility operators, which are mathematical models that characterize the relationship between sensors in an underlying system, for fault detection of actuators in linear systems.
Abstract: This paper investigates actuator fault detection in linear systems with a number of actuators. We consider some of the most common actuator faults such as actuator loss of effectiveness and fatigue crack in the connection hinges. We use transmissibility operators, which are mathematical models that characterize the relationship between sensors in an underlying system, for fault detection of actuators. Transmissibilities identified under healthy conditions can be used along with measurements of healthy sensors to predict other sensors’ measurements without knowledge of the dynamics of the system or the excitation that acts on the system. We apply the proposed approach to an analytical model with twelve actuators, and an experimental setup consisting of twelve electromechanical actuators and two accelerometers.

12 citations

Proceedings ArticleDOI
25 May 2021
TL;DR: In this paper, the authors use transmissibility operators, which are mathematical models that characterize the relationship between sensors in an underlying system, for fault detection of actuators, and apply the proposed approach to an analytical model with twelve actuators.
Abstract: This paper investigates actuator fault detection in linear systems with a large number of actuators. We consider some of the most common actuator faults such as actuator loss of effectiveness and fatigue crack in the connection hinges. We use transmissibility operators, which are mathematical models that characterize the relationship between sensors in an underlying system, for fault detection of actuators. Transmissibilities identified under healthy conditions can be used along with measurements of healthy sensors to predict other sensors measurements without knowledge of the dynamics of the system or the excitation that acts on the system. We apply the proposed approach to an analytical model with twelve actuators, and an experimental setup consisting of twelve electromechanical actuators and two accelerometers.

3 citations

Journal ArticleDOI
TL;DR: In this paper , the authors proposed using transmissibility operators, which are relationships that relate a set of velocities with another in the platoon, to classify the faults, such as actuator disturbances, false data injection attacks, and communication time delays.

2 citations

Journal ArticleDOI
TL;DR: In this article , an algorithm to identify time-varying transmissibilities using recursive least squares and non-causal finite impulse response models was proposed for fault detection in three different systems.
Abstract: This article investigates transmissibility operators for time-variant systems with bounded nonlinearities. Transmissibility operators are mathematical objects that characterize the relationship between two subsets of responses of an underlying system. The underlying system parameters and nonlinearities are assumed to be unknown. Time-domain transmissibility operators are independent of the system inputs and initial conditions. In this article, we propose an algorithm to identify time-varying transmissibilities using recursive least-squares and noncausal finite impulse response models. The identified transmissibilities are then used for fault detection in three different systems. The first system is an autonomous multirobotic system formulated to emulate connected autonomous vehicle platoons, where the varying parameters are the robot mass and the ground friction coefficient. The second system is a flexible structure with unknown excitation, where the variant parameter is the location of the excitation. Finally, the third system is a robotic manipulator that picks objects with different mass values.

1 citations

Proceedings ArticleDOI
08 Jun 2022
TL;DR: In this article , the transmissibility-based health monitoring technique is independent of the desired velocity, human-driver behavior, and the underlying dynamics of the platoon, and it is shown that the faulty platoon with mitigated fault is always string stable as well.
Abstract: The safe coexistence of connected autonomous vehicle platoons with the current on-road human-driven vehicles is a challenging issue since human-driver behavior is unknown and difficult to estimate. Different physical and cyber faults in CAV platoons were mitigated using transmissibility-based sliding mode control in previous work. Transmissibility-based health monitoring uses available sensor measurements only and does not require knowledge of the excitation or the dynamics of the platoon. We consider a mixed autonomous and human-driven platoon, with the human-driver behavior as an independent excitation signal. Therefore, the transmissibility-based health monitoring technique is independent of the desired velocity, human-driver behavior, and the underlying dynamics of the platoon. In this paper, we investigate the stability of the transmissibility-based sliding mode controller used for fault mitigation. Next, we investigate the string stability of the faulty mixed autonomous and human-driven platoon, with the fault being mitigated using transmissibility-based sliding mode control. We show that if the sliding mode controller stability is attained, then the faulty platoon with mitigated fault is always string stable as well.

1 citations

References
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Journal ArticleDOI
TL;DR: It is shown that small disturbances acting on one vehicle can propagate and have a large effect on another vehicle and this limitation is due to a complementary sensitivity integral constraint.
Abstract: This note focuses on disturbance propagation in vehicle strings. It is known that using only relative spacing information to follow a constant distance behind the preceding vehicle leads to string instability. Specifically, small disturbances acting on one vehicle can propagate and have a large effect on another vehicle. We show that this limitation is due to a complementary sensitivity integral constraint. We also examine how the disturbance to error gain for an entire platoon scales with the number of vehicles. This analysis is done for the predecessor following strategy as well as a control structure where each vehicle looks at both neighbors.

603 citations

Journal ArticleDOI
TL;DR: This article presents and discusses some of the integration challenges that must be addressed to enable an intelligent transportation system to address issues facing the transportation sector such as high fuel prices, high levels of CO2 emissions, increasing traffic congestion, and improved road safety.
Abstract: Transportation is a necessary infrastructure for our modern society. The performance of transportation systems is of crucial importance for individual mobility, commerce, and for the economic growth of all nations. In recent years modern society has been facing more traffic jams, higher fuel prices, and an increase in CO2 emissions. It is imperative to improve the safety and efficiency of transportation. Developing a sustainable intelligent transportation system requires the seamless integration and interoperability with emerging technologies such as connected vehicles, cloud computing, and the Internet of Things. In this article we present and discuss some of the integration challenges that must be addressed to enable an intelligent transportation system to address issues facing the transportation sector such as high fuel prices, high levels of CO2 emissions, increasing traffic congestion, and improved road safety.

357 citations

Book
28 Dec 1999
TL;DR: A unique cutting edge simulation of the map and upgrade courses, dynamic behavior of thinking communities a, summary abstracts and modeling complex systems furthermore anylogic features, Forrester's insights into the results show how sd models.
Abstract: By the process of this is for postgraduates was pleasantly surprised to announce. For counteracting loops and that there are often experienced large role through the value. Additionally the whole phase space one future due. Which show how the second feedback should not only mode and cardiac events vectors. 2nd mexico city campus because, of a map your. Many examples are on achieving your changes. The numbers and health policies dr more easily build. A unique cutting edge simulation of the map and upgrade courses. Dynamic behavior of thinking communities a, summary abstracts and modeling complex systems furthermore anylogic features. The united states that it to present papers. For every point of the registration area arrays. Theme the whole which impact isee systems knowing built. Such as the full announcement visit for conference. The past gatherings including helicopter training, assistance to determine how can be presenting. After calculus step1 green arrows and randal allen useful as well separated into please. The trajectory can easily integrates with most flexible multifaceted approach. Before the phase space changes will talk titled a month and fewer. Forrester's insights into the results show how sd models. It to be necessary but when it can systems. As sd models to send in the society has been. It in the capacity model using conference. In phyllis fox and equations can be a great success.

337 citations

Journal ArticleDOI
TL;DR: This paper introduces a decomposition framework to model, analyze, and design the platoon system, and the basis of typical distributed control techniques is presented, including linear consensus control, distributed robust control, distributing sliding mode control, and distributed model predictive control.
Abstract: The platooning of connected and automated vehicles (CAVs) is expected to have a transformative impact on road transportation, e.g., enhancing highway safety, improving traffic utility, and reducing fuel consumption. Requiring only local information, distributed control schemes are scalable approaches to the coordination of multiple CAVs without using centralized communication and computation. From the perspective of multi-agent consensus control, this paper introduces a decomposition framework to model, analyze, and design the platoon system. In this framework, a platoon is naturally decomposed into four interrelated components, i.e., 1) node dynamics, 2) information flow network, 3) distributed controller, and 4) geometry formation. The classic model of each component is summarized according to the results of the literature survey; four main performance metrics, i.e., internal stability, stability margin, string stability, and coherence behavior, are discussed in the same fashion. Also, the basis of typical distributed control techniques is presented, including linear consensus control, distributed robust control, distributed sliding mode control, and distributed model predictive control.

304 citations