基于ID预测模型的明渠系统控制参数在线优化研究
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摘要
渠系控制算法在输水工程中的成功应用,大大提高了渠系的运行调度水平,但现有控制算法依然存在渠池间耦合和时间滞后、传统PID控制算法应对突变敏感性较高等问题。为进一步提高渠系的响应速度,减少水位波动,现将模型预测控制引入渠道自动化控制中,采用线性化的ID模型作为预测模型,通过在线滚动优化目标函数,以设计可实时调节反馈控制参数的PID控制器。以湖北省漳河灌区三干渠三分干为工程背景,建立单渠段控制模型并进行仿真。仿真结果表明,MPC控制方法具有实时在线滚动优化的特点,针对渠道中不断变化的需求情况,可以较好地预测未来系统状态并以此选择合适的控制器参数。
The successful application of canal system control algorithm in water conveyance project has greatly improved canal system operation, but the existing canal system control algorithms still have some drawbacks, such as the coupling between the canal and pool time lag, as well as traditional PID control algorithm dealing with high sensitivity mutation and so on. In order to further improve the response speed of the canal system and reduce the water level fluctuation, the model predictive control is introduced into the channel automation control. The linearized ID model is used as the forecasting model, and the objective function is optimized through on-line scrolling to design a PID Controller that can adjust feedback control parameters in real time. Based on the engineering background of three-trunk canals in Zhanghe Irrigation District of Hubei Province, a single canal control model is established and simulated. The simulation results show that MPC control method has the characteristics of a rolling real-time online optimization, demand for changing channels, and the future state of the system can better predicted and used to select the appropriate controller parameters.
The successful application of canal system control algorithm in water conveyance project has greatly improved canal system operation, but the existing canal system control algorithms still have some drawbacks, such as the coupling between the canal and pool time lag, as well as traditional PID control algorithm dealing with high sensitivity mutation and so on. In order to further improve the response speed of the canal system and reduce the water level fluctuation, the model predictive control is introduced into the channel automation control. The linearized ID model is used as the forecasting model, and the objective function is optimized through on-line scrolling to design a PID Controller that can adjust feedback control parameters in real time. Based on the engineering background of three-trunk canals in Zhanghe Irrigation District of Hubei Province, a single canal control model is established and simulated. The simulation results show that MPC control method has the characteristics of a rolling real-time online optimization, demand for changing channels, and the future state of the system can better predicted and used to select the appropriate controller parameters.
引文
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[2] 杨昱昺, 周光宇. 计算机集成综合自动化预测控制建模[J]. 机械设计与制造工程,2008,37(11):30-33.
[3] Brdys MA, Grochowski M, Giminski T, et al. Hierarchical predictive control of integrated wastewater treatment systems[J]. Control Engineering Practice, 2008,16(6):751-767.
[4] Igreja JM, Lemos JM, Cadete FM, et al. Control of a water delivery canal with cooperative distributed MPC[C]// 2012 American Control Conference. Proceedings of the American Control Conference, 2012:3 346-3 351.
[5] Klaudia Horvath. MPC control of water level in a navigation canal-The Cuinchy-Fontinettes case study[C]// Control Conference (ECC); European: IEEE, 2014:1 337-1 342.
[6] 席裕庚, 李德伟, 林姝. 模型预测控制——现状与挑战[J]. 自动化学报,2013,(3):222-236.
[7] 郭华. 渠系运行的预测控制及其仿真研究[D]. 武汉:武汉大学, 2005.
[8] 王长德, 郭华, 邹朝望, 等. 动态矩阵控制在渠道运行系统中的应用[J]. 武汉大学学报(工学版),2005,(3):6-18.
[9] Clemmens AJ, Bautista E, Wahlin BT, Strand RJ. Simulation of Automatic Canal Control Systems[J]. Journal of Irrigation and Drainage Engineering, 2005,131(4):324-335.
[10] Clemmens AJ, Schuurmans J. Simple Optimal Downstream Feedback Canal Controllers: Theory[J]. Journal of Irrigation and Drainage Engineering, 2004,130(1):26-34.
[11] Weyer E, Williamson RC, Mareels IMY. On the Relationship Between Behavioural and Standard Methods for System Identification[J]. Automatica, 1998,34(6):801-804.
[12] 美国内务部垦务局. 现代灌区自动化管理实用手册[M]. 北京: 中国水利电力出版社,2003.
[13] Campi MC, Weyer E. Global Non-Asymptotic Confidence sets for General Linear Models[J]. IFAC Proceedings Volumes, 2005,38(1):279-284.
[14] 崔巍, 王长德, 管光华, 等. 渠道运行自调整模糊控制系统设计与仿真[J]. 武汉大学学报(工学版),2005(01):104-116.
[15] Y. WW-G, G. AL, T. D. Central Arizona Project: Operation Model[J]. Journal of the Water Resources Planning and Management Division, 1980,106(2):521-540.
[16] 黄凯, 管光华, 刘大志, 等. 串联渠系PID改进积分与微分环节仿真研究[J]. 灌溉排水学报,2017,36(2):1-11.
[17] 陶永华. 新型PID控制及其应用[M].北京:机械工业出版社, 1998:1-17.
[18] Schuurmans J, Bosgra OH, Brouwer R. Open-channel flow model approximation for controller design[J]. Applied Mathematical Modelling, 1995,19(9):525-530.
[19] 输水渠道系统运行仿真与控制软件V1.0[P] 中国:2011SR034392. 2011-06-03.
[20] 刘向昕, 曹晓冬, 谭国俊, 等. 电励磁同步电机全速域自适应模型预测控制[J]. 电工技术学报, 2017,(4):112-122.