Trajectory Control Strategy and System Modeling of Load-Sensitive Hydraulic Excavator
Accurate control of excavator trajectory is the foundation for the intelligent and unmanned development of excavators. The excavator operation process requires multiple actuators to cooperate to complete the response action. However, the existing control methods to realize a single actuator of the e...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/11/1/10 |
| _version_ | 1797439542068772864 |
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| author | Haoju Song Guiqin Li Zhen Li Xin Xiong |
| author_facet | Haoju Song Guiqin Li Zhen Li Xin Xiong |
| author_sort | Haoju Song |
| collection | DOAJ |
| description | Accurate control of excavator trajectory is the foundation for the intelligent and unmanned development of excavators. The excavator operation process requires multiple actuators to cooperate to complete the response action. However, the existing control methods to realize a single actuator of the excavator can no longer meet the practical demand. Based on this, a hybrid adaptive quantum particle swarm optimization algorithm (HAQPSO) is proposed to tune the proportional integral derivative (PID) controller parameters for enhancing the trajectory control accuracy of excavator actuators. To increase particle randomization and search speed and avoid the local convergence of QPSO, the QPSO is combined with circle chaotic mapping, Gaussian mutation operators, and adaptive adjustment factors, while the linear transformation of the contraction-expansion coefficient (<i>CE</i>) is improved to the dynamic adjustment mode. Through the interface block, a co-simulation platform for the load-sensitive system excavator is constructed, and trajectory experiments of multiple actuator compound actions are carried out. The simulation results show that—compared with ZN-PID, PSO-PID, and QPSO-PID—the trajectory error accuracy of the boom is improved by 26.59%, 32.95%, and 9.44%, respectively, which proves the high control accuracy of HAQPSO-PID in controlling the trajectory of multiple actuators. |
| first_indexed | 2024-03-09T11:54:28Z |
| format | Article |
| id | doaj.art-60732abdf3894ca89ec5202d082da726 |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-09T11:54:28Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-60732abdf3894ca89ec5202d082da7262023-11-30T23:10:39ZengMDPI AGMachines2075-17022022-12-011111010.3390/machines11010010Trajectory Control Strategy and System Modeling of Load-Sensitive Hydraulic ExcavatorHaoju Song0Guiqin Li1Zhen Li2Xin Xiong3Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic and Automation Engineering, Shanghai University, Shanghai 200444, ChinaShanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic and Automation Engineering, Shanghai University, Shanghai 200444, ChinaShanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic and Automation Engineering, Shanghai University, Shanghai 200444, ChinaShanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic and Automation Engineering, Shanghai University, Shanghai 200444, ChinaAccurate control of excavator trajectory is the foundation for the intelligent and unmanned development of excavators. The excavator operation process requires multiple actuators to cooperate to complete the response action. However, the existing control methods to realize a single actuator of the excavator can no longer meet the practical demand. Based on this, a hybrid adaptive quantum particle swarm optimization algorithm (HAQPSO) is proposed to tune the proportional integral derivative (PID) controller parameters for enhancing the trajectory control accuracy of excavator actuators. To increase particle randomization and search speed and avoid the local convergence of QPSO, the QPSO is combined with circle chaotic mapping, Gaussian mutation operators, and adaptive adjustment factors, while the linear transformation of the contraction-expansion coefficient (<i>CE</i>) is improved to the dynamic adjustment mode. Through the interface block, a co-simulation platform for the load-sensitive system excavator is constructed, and trajectory experiments of multiple actuator compound actions are carried out. The simulation results show that—compared with ZN-PID, PSO-PID, and QPSO-PID—the trajectory error accuracy of the boom is improved by 26.59%, 32.95%, and 9.44%, respectively, which proves the high control accuracy of HAQPSO-PID in controlling the trajectory of multiple actuators.https://www.mdpi.com/2075-1702/11/1/10hydraulic excavatorload-sensitive systemhybrid adaptive quantum particle swarm optimization algorithmtrajectory controlco-simulation |
| spellingShingle | Haoju Song Guiqin Li Zhen Li Xin Xiong Trajectory Control Strategy and System Modeling of Load-Sensitive Hydraulic Excavator Machines hydraulic excavator load-sensitive system hybrid adaptive quantum particle swarm optimization algorithm trajectory control co-simulation |
| title | Trajectory Control Strategy and System Modeling of Load-Sensitive Hydraulic Excavator |
| title_full | Trajectory Control Strategy and System Modeling of Load-Sensitive Hydraulic Excavator |
| title_fullStr | Trajectory Control Strategy and System Modeling of Load-Sensitive Hydraulic Excavator |
| title_full_unstemmed | Trajectory Control Strategy and System Modeling of Load-Sensitive Hydraulic Excavator |
| title_short | Trajectory Control Strategy and System Modeling of Load-Sensitive Hydraulic Excavator |
| title_sort | trajectory control strategy and system modeling of load sensitive hydraulic excavator |
| topic | hydraulic excavator load-sensitive system hybrid adaptive quantum particle swarm optimization algorithm trajectory control co-simulation |
| url | https://www.mdpi.com/2075-1702/11/1/10 |
| work_keys_str_mv | AT haojusong trajectorycontrolstrategyandsystemmodelingofloadsensitivehydraulicexcavator AT guiqinli trajectorycontrolstrategyandsystemmodelingofloadsensitivehydraulicexcavator AT zhenli trajectorycontrolstrategyandsystemmodelingofloadsensitivehydraulicexcavator AT xinxiong trajectorycontrolstrategyandsystemmodelingofloadsensitivehydraulicexcavator |