LPV design of fault-tolerant control for road vehicles

Gáspár, Péter; Szabó, Zoltán; Bokor, József

doi:10.2478/v10006-012-0013-x

Cited by 20 publications

(6 citation statements)

References 25 publications

(21 reference statements)

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“…LPV control designs for lateral control are presented in [157]- [159]. LPV models are used in [160], [161] together with predictive control approaches for path and trajectory stabilization.…”

Section: Linear Parameter Varying Controllersmentioning

confidence: 99%

A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles

Paden

Ćáp

Yong

et al. 2016

IEEE Trans. Intell. Veh.

1,968

976

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Self-driving vehicles are a maturing technology with the potential to reshape mobility by enhancing the safety, accessibility, efficiency, and convenience of automotive transportation. Safety-critical tasks that must be executed by a self-driving vehicle include planning of motions through a dynamic environment shared with other vehicles and pedestrians, and their robust executions via feedback control. The objective of this paper is to survey the current state of the art on planning and control algorithms with particular regard to the urban setting. A selection of proposed techniques is reviewed along with a discussion of their effectiveness. The surveyed approaches differ in the vehicle mobility model used, in assumptions on the structure of the environment, and in computational requirements. The side-by-side comparison presented in this survey helps to gain insight into the strengths and limitations of the reviewed approaches and assists with system level design choices. VIConclusions 23 References 23 * The first two authors contributed equally to this work.

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Section: Linear Parameter Varying Controllersmentioning

confidence: 99%

A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles

Paden

Ćáp

Yong

et al. 2016

IEEE Trans. Intell. Veh.

1,968

976

View full text Add to dashboard Cite

show abstract

“…[9] presents a methodology based on the use of an ANFIS in order to do a fault diagnosis of a vehicle driveline system and shows how this model free methodologies can be applied expecting a high accuracy on the final diagnosis. In [10][11][12] authors propose approaches that are classified as model based methodologies, needing in this way a good and complete knowledge of the system being modeled in order to have a good accuracy when giving the final diagnosis. Now a new vehicle fault detection and diagnosis proposal on the history data using an AANN, FS and a CNN is proposed.…”

Section: State Of the Artmentioning

confidence: 99%

“…13. Set the b test R vector obtained in step (10) as the input of the competitive neural network previously trained. Give which fault is present and its location.…”

Section: Algorithm Of the Proposalmentioning

confidence: 99%

Fault Diagnosis of a Vehicle Based on a History Data Hybrid Approach

Gonzlez¹

2012

JMMT

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The main goals of a fault diagnosis system in a vehicle are to prevent dangerous situations for occupants. This domain is a complex system that turns the monitoring task a very challenging one. On one hand, there is an inherent uncertainty caused by noisy sensor measurements and unmodeled dynamics and in the other hand the existence of false alarms that appears in a natural way due to the high correlation between several variables. The present work is a variant of a proposal made by the author. This paper presents a new approach based on history data process that can manage the variable correlation and can carry out a complete fault diagnosis system. In the first phase, the approach learns behavior from normal operation of the system using an autoassociative neural network (AANN). On a second phase a fuzzy system (FS) is carried out in order to diminish the presence of false alarms that could be originated by the noise presence and then a competitive neural network (CNN) is used to give the final diagnosis. Results are shown for a ten variables vehicle monitoring.

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“…The relative technique for localization is called Simultaneous Localization and Mapping (SLAM) [1,2], which can obtain the map of the environment and get the robot poses simultaneously. In addition, the corresponding decision-making system which consists of planning [3][4][5][6][7] and control [8][9][10] would generate a safety trajectory and control the mobile robot to follow it until reaching the goal. The decision-making system plays an important role to connect the preceding localization stage and the following manipulation stage.…”

Section: Introductionmentioning

confidence: 99%

Mapless Collaborative Navigation for a Multi-Robot System Based on the Deep Reinforcement Learning

et al. 2019

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Compared with the single robot system, a multi-robot system has higher efficiency and fault tolerance. The multi-robot system has great potential in some application scenarios, such as the robot search, rescue and escort tasks, and so on. Deep reinforcement learning provides a potential framework for multi-robot formation and collaborative navigation. This paper mainly studies the collaborative formation and navigation of multi-robots by using the deep reinforcement learning algorithm. The proposed method improves the classical Deep Deterministic Policy Gradient (DDPG) to address the single robot mapless navigation task. We also extend the single-robot Deep Deterministic Policy Gradient algorithm to the multi-robot system, and obtain the Parallel Deep Deterministic Policy Gradient (PDDPG). By utilizing the 2D lidar sensor, the group of robots can accomplish the formation construction task and the collaborative formation navigation task. The experiment results in a Gazebo simulation platform illustrates that our method is capable of guiding mobile robots to construct the formation and keep the formation during group navigation, directly through raw lidar data inputs.

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LPV design of fault-tolerant control for road vehicles

Cited by 20 publications

References 25 publications

A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles

A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles

Fault Diagnosis of a Vehicle Based on a History Data Hybrid Approach

Mapless Collaborative Navigation for a Multi-Robot System Based on the Deep Reinforcement Learning

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