Enhancing stability and safety: A novel multi‐constraint model predictive control approach for forklift trajectory
Abstract The advancements in intelligent manufacturing have made high‐precision trajectory tracking technology crucial for improving the efficiency and safety of in‐factory cargo transportation. This study addresses the limitations of current forklift navigation systems in trajectory control accurac...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-12-01
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| Series: | IET Cyber-systems and Robotics |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/csy2.70004 |
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| _version_ | 1846099238668206080 |
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| author | Yizhen Sun Junyou Yang Donghui Zhao Moses Chukwuka Okonkwo Jianmin Zhang Shuoyu Wang Yang Liu |
| author_facet | Yizhen Sun Junyou Yang Donghui Zhao Moses Chukwuka Okonkwo Jianmin Zhang Shuoyu Wang Yang Liu |
| author_sort | Yizhen Sun |
| collection | DOAJ |
| description | Abstract The advancements in intelligent manufacturing have made high‐precision trajectory tracking technology crucial for improving the efficiency and safety of in‐factory cargo transportation. This study addresses the limitations of current forklift navigation systems in trajectory control accuracy and stability by proposing the Enhanced Stability and Safety Model Predictive Control (ESS‐MPC) method. This approach includes a multi‐constraint strategy for improved stability and safety. The kinematic model for a single front steering‐wheel forklift vehicle is constructed with all known state quantities, including the steering angle, resulting in a more accurate model description and trajectory prediction. To ensure vehicle safety, the spatial safety boundary obtained from the trajectory planning module is established as a hard constraint for ESS‐MPC tracking. The optimisation constraints are also updated with the key kinematic and dynamic parameters of the forklift. The ESS‐MPC method improved the position and pose accuracy and stability by 57.93%, 37.83%, and 57.51%, respectively, as demonstrated through experimental validation using simulation and real‐world environments. This study provides significant support for the development of autonomous navigation systems for industrial forklifts. |
| format | Article |
| id | doaj-art-3924803abbe5452fbff982f1d0abfea5 |
| institution | Kabale University |
| issn | 2631-6315 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Cyber-systems and Robotics |
| spelling | doaj-art-3924803abbe5452fbff982f1d0abfea52024-12-31T19:42:28ZengWileyIET Cyber-systems and Robotics2631-63152024-12-0164n/an/a10.1049/csy2.70004Enhancing stability and safety: A novel multi‐constraint model predictive control approach for forklift trajectoryYizhen Sun0Junyou Yang1Donghui Zhao2Moses Chukwuka Okonkwo3Jianmin Zhang4Shuoyu Wang5Yang Liu6School of Electrical Engineering Shenyang University of Technology Shenyang ChinaSchool of Electrical Engineering Shenyang University of Technology Shenyang ChinaSchool of Electrical Engineering Shenyang University of Technology Shenyang ChinaSchool of Electrical Engineering Shenyang University of Technology Shenyang ChinaSchool of Information Engineering Shenyang University Shenyang ChinaDepartment of Intelligent Mechanical Systems Engineering Kochi University of Technology Kochi JapanIntelligent Robot Laboratory Shenyang Open University Shenyang ChinaAbstract The advancements in intelligent manufacturing have made high‐precision trajectory tracking technology crucial for improving the efficiency and safety of in‐factory cargo transportation. This study addresses the limitations of current forklift navigation systems in trajectory control accuracy and stability by proposing the Enhanced Stability and Safety Model Predictive Control (ESS‐MPC) method. This approach includes a multi‐constraint strategy for improved stability and safety. The kinematic model for a single front steering‐wheel forklift vehicle is constructed with all known state quantities, including the steering angle, resulting in a more accurate model description and trajectory prediction. To ensure vehicle safety, the spatial safety boundary obtained from the trajectory planning module is established as a hard constraint for ESS‐MPC tracking. The optimisation constraints are also updated with the key kinematic and dynamic parameters of the forklift. The ESS‐MPC method improved the position and pose accuracy and stability by 57.93%, 37.83%, and 57.51%, respectively, as demonstrated through experimental validation using simulation and real‐world environments. This study provides significant support for the development of autonomous navigation systems for industrial forklifts.https://doi.org/10.1049/csy2.70004automatic guided vehiclesmobile robotsrobot manipulatortracking control |
| spellingShingle | Yizhen Sun Junyou Yang Donghui Zhao Moses Chukwuka Okonkwo Jianmin Zhang Shuoyu Wang Yang Liu Enhancing stability and safety: A novel multi‐constraint model predictive control approach for forklift trajectory IET Cyber-systems and Robotics automatic guided vehicles mobile robots robot manipulator tracking control |
| title | Enhancing stability and safety: A novel multi‐constraint model predictive control approach for forklift trajectory |
| title_full | Enhancing stability and safety: A novel multi‐constraint model predictive control approach for forklift trajectory |
| title_fullStr | Enhancing stability and safety: A novel multi‐constraint model predictive control approach for forklift trajectory |
| title_full_unstemmed | Enhancing stability and safety: A novel multi‐constraint model predictive control approach for forklift trajectory |
| title_short | Enhancing stability and safety: A novel multi‐constraint model predictive control approach for forklift trajectory |
| title_sort | enhancing stability and safety a novel multi constraint model predictive control approach for forklift trajectory |
| topic | automatic guided vehicles mobile robots robot manipulator tracking control |
| url | https://doi.org/10.1049/csy2.70004 |
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