Autonomous mobile robot (AMR) integrated chassis control considering active-fault tolerant control
This report presents a comprehensive study of control strategies for Autonomous Mobile Robots (AMRs). Specifically, the report focuses on the implementation of two control strategies, Model Predictive Control (MPC) and Proportional Integral Derivative (PID), for collision avoidance in the AMR system...
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2023
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sg-ntu-dr.10356-1670652023-05-27T16:50:41Z Autonomous mobile robot (AMR) integrated chassis control considering active-fault tolerant control Muhammad Bin Shamsudin Lyu Chen School of Mechanical and Aerospace Engineering Schaeffler Hub for Advanced REsearch (SHARE) Lab lyuchen@ntu.edu.sg Engineering::Mechanical engineering::Robots This report presents a comprehensive study of control strategies for Autonomous Mobile Robots (AMRs). Specifically, the report focuses on the implementation of two control strategies, Model Predictive Control (MPC) and Proportional Integral Derivative (PID), for collision avoidance in the AMR system. Additionally, the report discusses the implementation of four Active Fault Tolerant Control (AFTC) algorithms for the AMR's actuators. The results of simulations demonstrate that both MPC and PID controllers are effective in controlling the AMR system and avoiding collisions. Moreover, all four AFTC algorithms were effective in ensuring the AMR can move linearly in the horizontal direction when there is a fault in the driving actuator(s). The study highlights the importance of effective control strategies for AMR systems and can be useful for further research on AMR systems and their applications in various industries. Bachelor of Engineering (Mechanical Engineering) 2023-05-21T12:32:14Z 2023-05-21T12:32:14Z 2023 Final Year Project (FYP) Muhammad Bin Shamsudin (2023). Autonomous mobile robot (AMR) integrated chassis control considering active-fault tolerant control. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/167065 https://hdl.handle.net/10356/167065 en C079 application/pdf Nanyang Technological University |
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Engineering::Mechanical engineering::Robots Muhammad Bin Shamsudin Autonomous mobile robot (AMR) integrated chassis control considering active-fault tolerant control |
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This report presents a comprehensive study of control strategies for Autonomous Mobile Robots (AMRs). Specifically, the report focuses on the implementation of two control strategies, Model Predictive Control (MPC) and Proportional Integral Derivative (PID), for collision avoidance in the AMR system. Additionally, the report discusses the implementation of four Active Fault Tolerant Control (AFTC) algorithms for the AMR's actuators. The results of simulations demonstrate that both MPC and PID controllers are effective in controlling the AMR system and avoiding collisions. Moreover, all four AFTC algorithms were effective in ensuring the AMR can move linearly in the horizontal direction when there is a fault in the driving actuator(s). The study highlights the importance of effective control strategies for AMR systems and can be useful for further research on AMR systems and their applications in various industries. |
author2 |
Lyu Chen |
author_facet |
Lyu Chen Muhammad Bin Shamsudin |
format |
Final Year Project |
author |
Muhammad Bin Shamsudin |
author_sort |
Muhammad Bin Shamsudin |
title |
Autonomous mobile robot (AMR) integrated chassis control considering active-fault tolerant control |
title_short |
Autonomous mobile robot (AMR) integrated chassis control considering active-fault tolerant control |
title_full |
Autonomous mobile robot (AMR) integrated chassis control considering active-fault tolerant control |
title_fullStr |
Autonomous mobile robot (AMR) integrated chassis control considering active-fault tolerant control |
title_full_unstemmed |
Autonomous mobile robot (AMR) integrated chassis control considering active-fault tolerant control |
title_sort |
autonomous mobile robot (amr) integrated chassis control considering active-fault tolerant control |
publisher |
Nanyang Technological University |
publishDate |
2023 |
url |
https://hdl.handle.net/10356/167065 |
_version_ |
1772827109968838656 |