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| DOI：10.13522/j.cnki.ggps.2023458 |
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| Adaptive control method for multi-gate canals under large water level variations |
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YANG Yixin, HUANG Cao, LIU Jinlong, LI Weiqi, CAO Jinsong
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1. School of Hydraulic and Environmental Engineering, Changsha University of Science & Technology, Changsha 410114, China;
2. Key Laboratory of Dongting Lake Aquatic Eco-Environmental Control and Restoration of Hunan Province,
Changsha University of Science & Technology, Changsha 410114, China;
3. Beijing General Municipal Engineering Design & Research Institute Co., Ltd, Beijing 100082, China
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| Abstract: |
| 【Objective】The aim of this study is to improve the accuracy of water level control, minimize water level oscillations, and improve the convergence efficiency of control algorithms for multi-stage gate-controlled canals operating under significant water level variations.【Method】We propose an adaptive predictive control algorithm (APC) which dynamically adjusts the parameters of the identification (ID) model based on real-time canal water level data and modifies the activation frequency of sluices. The performance of the APC algorithm is comprehensively evaluated and compared with other control algorithms under six distinct operational conditions.【Result】In comparison to the linear quadratic controller (LQR) and the model predictive controller (MPC) based on the ID model, the APC algorithm shows significant improvements in accuracy and reliability, reducing the regulation duration by 19% to 32% and 8% to 40% respectively, damping the cumulative fluctuation of the canal water level by 47% to 97% and 14% to 93% respectively, lowering the mean absolute deviation of canal water level by 24% to 77% and 5% to 59%, respectively.【Conclusion】The APC method can substantially improve the precision of water level control and the convergence for multi-stage gate-controlled canals. It is robust for various canal water level amplitudes, thereby providing a crucial technical support for advancing intelligent management of water conservancy infrastructures. |
| Key words: Sha-River; real-time control; integrator-delay model; model predictive control; adaptive method |
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