Abstract
Canal systems are complex nonlinear, distributed parameter systems with changing parameters according to the operating point. In this paper, a linear parameter‐varying (LPV) state‐space canal control model is obtained by identification in a local way using a multimodel approach. This LPV identification procedure is based on subspace methods for different operating points of an irrigation canal covering the full operation range. Different subspace algorithms have been used and compared. The model that best represents the canal behavior in a precise manner has been chosen, and it has been validated by error functions and analysis correlation of residuals in a laboratory multireach pilot canal providing satisfactory results.
Highlights
Water is one of the most used resources by industrial and agricultural sectors, and obviously by population
The LPV identification method used for the experimental modeling of our pilot two-pool canal presented is a two-step procedure where 1 linear state-space models are identified at several different operating points by subspace identification methods over the full range of operation; 2 a global state-space multi-model is obtained at the end interpolating the local state-space models using polynomials 21
It is known that open-flow canals present large delays that change with the operating point in this case, the pump operation, i.e., the upstream level of each canal pool 26
Summary
Water is one of the most used resources by industrial and agricultural sectors, and obviously by population. LTI control models widely used are Hayami model , Muskingum model 3 , IDZ model 4, , or black-box models identified using parameter estimation by classical identification methods 1, 12, These systems are not completely amenable using conventional linear modeling approaches due to the lack of precise, formal knowledge about the system; strongly nonlinear behavior; high degree of uncertainty; time varying characteristics; dynamic parameters changing over the operating point and coupling between pools. Subspace-based system identification methods are a branch that has been recently developed in system identification attracting much attention thanks to their computational simplicity and effectiveness in identifying dynamic state-space linear multivariable systems These algorithms are numerically robust and do not involve nonlinear optimization techniques, being fast and accurate.
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