Observer-Based Remote Conductivity Variable-Parameter Sliding Mode Control for Water–Fertilizer Integration Machines Using Recursive Least Squares Adaptive Estimation
In remote conductivity control for water–fertilizer integration systems, challenges such as long-distance nonlinearities and variable parameters can lead to fertilization inaccuracies, including over-irrigation and uneven distribution, affecting both productivity and environmental sustainability. To...
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MDPI AG
2025-04-01
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| author | Peng Zhang Zhigang Li Xue Hu Lixin Zhang |
| author_facet | Peng Zhang Zhigang Li Xue Hu Lixin Zhang |
| author_sort | Peng Zhang |
| collection | DOAJ |
| description | In remote conductivity control for water–fertilizer integration systems, challenges such as long-distance nonlinearities and variable parameters can lead to fertilization inaccuracies, including over-irrigation and uneven distribution, affecting both productivity and environmental sustainability. To mitigate these issues, this study proposes a variable-parameter sliding mode control (VSMC) strategy, combined with an adaptive observer based on Recursive Least Squares (RLS) to estimate system inertia and load torque in real time. This allows for dynamic adjustment of the sliding surface parameters, ensuring robust control even under varying operating conditions. Two parameter derivation approaches—analytical modeling and data-driven fitting—are evaluated. Field tests demonstrate that VSMC outperforms the Proportional–Integral (PI) and conventional sliding mode control (SMC) methods in maintaining target electrical conductivity (EC) levels. Specifically, for a target EC of 1.4 mS/cm, VSMC stabilizes the system to within 1.18–1.60 mS/cm in 95 s, with a 14.3% overshoot, well within agronomic tolerance. In regional irrigation trials, VSMC significantly improves fertilizer uniformity, reducing the standard deviation of potassium nitrate distribution from 2.14 (PI) to 0.59. The simulation and experimental results validate the effectiveness and robustness of the proposed method, highlighting its potential to enhance agronomic efficiency and reduce environmental impact. |
| format | Article |
| id | doaj-art-ebea72d4cbb246d390ff254f89b57e70 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-ebea72d4cbb246d390ff254f89b57e702025-08-20T01:49:20ZengMDPI AGApplied Sciences2076-34172025-04-01159499310.3390/app15094993Observer-Based Remote Conductivity Variable-Parameter Sliding Mode Control for Water–Fertilizer Integration Machines Using Recursive Least Squares Adaptive EstimationPeng Zhang0Zhigang Li1Xue Hu2Lixin Zhang3College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, ChinaCollege of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, ChinaCollege of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, ChinaCollege of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, ChinaIn remote conductivity control for water–fertilizer integration systems, challenges such as long-distance nonlinearities and variable parameters can lead to fertilization inaccuracies, including over-irrigation and uneven distribution, affecting both productivity and environmental sustainability. To mitigate these issues, this study proposes a variable-parameter sliding mode control (VSMC) strategy, combined with an adaptive observer based on Recursive Least Squares (RLS) to estimate system inertia and load torque in real time. This allows for dynamic adjustment of the sliding surface parameters, ensuring robust control even under varying operating conditions. Two parameter derivation approaches—analytical modeling and data-driven fitting—are evaluated. Field tests demonstrate that VSMC outperforms the Proportional–Integral (PI) and conventional sliding mode control (SMC) methods in maintaining target electrical conductivity (EC) levels. Specifically, for a target EC of 1.4 mS/cm, VSMC stabilizes the system to within 1.18–1.60 mS/cm in 95 s, with a 14.3% overshoot, well within agronomic tolerance. In regional irrigation trials, VSMC significantly improves fertilizer uniformity, reducing the standard deviation of potassium nitrate distribution from 2.14 (PI) to 0.59. The simulation and experimental results validate the effectiveness and robustness of the proposed method, highlighting its potential to enhance agronomic efficiency and reduce environmental impact.https://www.mdpi.com/2076-3417/15/9/4993recursive least squares observerPMSM servo systemsliding mode controlparameter adaptation |
| spellingShingle | Peng Zhang Zhigang Li Xue Hu Lixin Zhang Observer-Based Remote Conductivity Variable-Parameter Sliding Mode Control for Water–Fertilizer Integration Machines Using Recursive Least Squares Adaptive Estimation Applied Sciences recursive least squares observer PMSM servo system sliding mode control parameter adaptation |
| title | Observer-Based Remote Conductivity Variable-Parameter Sliding Mode Control for Water–Fertilizer Integration Machines Using Recursive Least Squares Adaptive Estimation |
| title_full | Observer-Based Remote Conductivity Variable-Parameter Sliding Mode Control for Water–Fertilizer Integration Machines Using Recursive Least Squares Adaptive Estimation |
| title_fullStr | Observer-Based Remote Conductivity Variable-Parameter Sliding Mode Control for Water–Fertilizer Integration Machines Using Recursive Least Squares Adaptive Estimation |
| title_full_unstemmed | Observer-Based Remote Conductivity Variable-Parameter Sliding Mode Control for Water–Fertilizer Integration Machines Using Recursive Least Squares Adaptive Estimation |
| title_short | Observer-Based Remote Conductivity Variable-Parameter Sliding Mode Control for Water–Fertilizer Integration Machines Using Recursive Least Squares Adaptive Estimation |
| title_sort | observer based remote conductivity variable parameter sliding mode control for water fertilizer integration machines using recursive least squares adaptive estimation |
| topic | recursive least squares observer PMSM servo system sliding mode control parameter adaptation |
| url | https://www.mdpi.com/2076-3417/15/9/4993 |
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