Decision Fault Tree Learning and Differential Lyapunov Optimal Control for Path Tracking
This paper considers the main challenges for all components engaged in the driving task suggested by the automation of road vehicles or autonomous cars. Numerous autonomous vehicle developers often invest an important amount of time and effort in fine-tuning and measuring the route tracking to obtai...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/25/3/443 |
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| author | S. Subash Chandra Bose Badria Sulaiman Alfurhood Gururaj H L Francesco Flammini Rajesh Natarajan Sheela Shankarappa Jaya |
| author_facet | S. Subash Chandra Bose Badria Sulaiman Alfurhood Gururaj H L Francesco Flammini Rajesh Natarajan Sheela Shankarappa Jaya |
| author_sort | S. Subash Chandra Bose |
| collection | DOAJ |
| description | This paper considers the main challenges for all components engaged in the driving task suggested by the automation of road vehicles or autonomous cars. Numerous autonomous vehicle developers often invest an important amount of time and effort in fine-tuning and measuring the route tracking to obtain reliable tracking performance over a wide range of autonomous vehicle speed and road curvature diversities. However, a number of automated vehicles were not considered for fault-tolerant trajectory tracking methods. Motivated by this, the current research study of the Differential Lyapunov Stochastic and Decision Defect Tree Learning (DLS-DFTL) method is proposed to handle fault detection and course tracking for autonomous vehicle problems. Initially, Differential Lyapunov Stochastic Optimal Control (SOC) with customizable Z-matrices is to precisely design the path tracking for a particular target vehicle while successfully managing the noise and fault issues that arise from the localization and path planning. With the autonomous vehicle’s low ceilings, a recommendation trajectory generation model is created to support such a safety justification. Then, to detect an unexpected deviation caused by a fault, a fault detection technique known as Decision Fault Tree Learning (DFTL) is built. The DLS-DFTL method can be used to find and locate problems in expansive, intricate communication networks. We conducted various tests and showed the applicability of DFTL. By offering some analysis of the experimental outcomes, the suggested method produces significant accuracy. In addition to a thorough study that compares the results to state-of-the-art techniques, simulation was also used to quantify the rate and time of defect detection. The experimental result shows that the proposed DLS-DFTL enhances the fault detection rate (38%), reduces the loss rate (14%), and has a faster fault detection time (24%) than the state of art methods. |
| first_indexed | 2024-03-11T06:34:54Z |
| format | Article |
| id | doaj.art-5c7e29d5b35f4298a949a01241187c11 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-11T06:34:54Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-5c7e29d5b35f4298a949a01241187c112023-11-17T10:56:17ZengMDPI AGEntropy1099-43002023-03-0125344310.3390/e25030443Decision Fault Tree Learning and Differential Lyapunov Optimal Control for Path TrackingS. Subash Chandra Bose0Badria Sulaiman Alfurhood1Gururaj H L2Francesco Flammini3Rajesh Natarajan4Sheela Shankarappa Jaya5Department of Computer Science, Islamiah College (Autonomous), Vaniyambadi 635751, IndiaDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi ArabiaDepartment of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal 576104, IndiaIDSIA USI-SUPSI, University of Applied Sciences and Arts of Southern Switzerland, 6928 Manno, SwitzerlandInformation Technology Department, University of Technology and Applied Sciences-Shinas, Al-Aqr, Shinas 324, OmanDepartment of Electronics and Communication, SIT Siddaganga Institute of Technology, Tumkur 572103, IndiaThis paper considers the main challenges for all components engaged in the driving task suggested by the automation of road vehicles or autonomous cars. Numerous autonomous vehicle developers often invest an important amount of time and effort in fine-tuning and measuring the route tracking to obtain reliable tracking performance over a wide range of autonomous vehicle speed and road curvature diversities. However, a number of automated vehicles were not considered for fault-tolerant trajectory tracking methods. Motivated by this, the current research study of the Differential Lyapunov Stochastic and Decision Defect Tree Learning (DLS-DFTL) method is proposed to handle fault detection and course tracking for autonomous vehicle problems. Initially, Differential Lyapunov Stochastic Optimal Control (SOC) with customizable Z-matrices is to precisely design the path tracking for a particular target vehicle while successfully managing the noise and fault issues that arise from the localization and path planning. With the autonomous vehicle’s low ceilings, a recommendation trajectory generation model is created to support such a safety justification. Then, to detect an unexpected deviation caused by a fault, a fault detection technique known as Decision Fault Tree Learning (DFTL) is built. The DLS-DFTL method can be used to find and locate problems in expansive, intricate communication networks. We conducted various tests and showed the applicability of DFTL. By offering some analysis of the experimental outcomes, the suggested method produces significant accuracy. In addition to a thorough study that compares the results to state-of-the-art techniques, simulation was also used to quantify the rate and time of defect detection. The experimental result shows that the proposed DLS-DFTL enhances the fault detection rate (38%), reduces the loss rate (14%), and has a faster fault detection time (24%) than the state of art methods.https://www.mdpi.com/1099-4300/25/3/443optimal controldifferential Lyapunovfault detectionpath trackingautonomous vehiclesmachine learning |
| spellingShingle | S. Subash Chandra Bose Badria Sulaiman Alfurhood Gururaj H L Francesco Flammini Rajesh Natarajan Sheela Shankarappa Jaya Decision Fault Tree Learning and Differential Lyapunov Optimal Control for Path Tracking Entropy optimal control differential Lyapunov fault detection path tracking autonomous vehicles machine learning |
| title | Decision Fault Tree Learning and Differential Lyapunov Optimal Control for Path Tracking |
| title_full | Decision Fault Tree Learning and Differential Lyapunov Optimal Control for Path Tracking |
| title_fullStr | Decision Fault Tree Learning and Differential Lyapunov Optimal Control for Path Tracking |
| title_full_unstemmed | Decision Fault Tree Learning and Differential Lyapunov Optimal Control for Path Tracking |
| title_short | Decision Fault Tree Learning and Differential Lyapunov Optimal Control for Path Tracking |
| title_sort | decision fault tree learning and differential lyapunov optimal control for path tracking |
| topic | optimal control differential Lyapunov fault detection path tracking autonomous vehicles machine learning |
| url | https://www.mdpi.com/1099-4300/25/3/443 |
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