Configuration Path Control

Pankov, Sergey

doi:10.1007/s12555-021-0466-5

Cited by 2 publications

(2 citation statements)

References 25 publications

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“…To compute the conditional expectation of the loglikelihood function (19), it is necessary to evaluate the posterior probability and prediction of x N and corresponding covariance P N . Previous studies have employed a fixed-interval smoother to calculate the posterior of the hidden states in the Q-function.…”

Section: Predict and Update Of States And Covariancementioning

confidence: 99%

“…[16] In recent times, significant attention has been given to the challenge of parameter estimation for nonlinear functions and auxiliary models. [17][18][19] In addition, the field of agro-hydrology has seen previous efforts dedicated to parameter estimation. In the work of Bo et al, [20] a moving horizon estimation method is introduced to simultaneously estimate the parameters and states of agro-hydrological systems.…”

Section: Introductionmentioning

confidence: 99%

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State estimation for one‐dimensional agro‐hydrological processes with model mismatch

Liu,

Zhao

et al. 2023

Can J Chem Eng

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The importance of accurate soil moisture data for the development of modern closed‐loop irrigation systems cannot be overstated. Due to the diversity of soil, it is difficult to obtain an accurate model for the agro‐hydrological system. In this study, soil moisture estimation in one‐dimensional (1D) agro‐hydrological systems with model mismatch is the focus. To address the problem of model mismatch, a nonlinear state‐space model derived from the Richards equation is utilized, along with additive unknown inputs. The determination of the number of sensors required is achieved through sensitivity analysis and the orthogonalization projection method. To estimate states and unknown inputs in real‐time, a recursive expectation maximization (EM) algorithm derived from the conventional EM algorithm is employed. During the E‐step, the extended Kalman filter (EKF) is used to compute states and covariance in the recursive Q‐function, while in the M‐step, unknown inputs are updated by locally maximizing the recursive Q‐function. The estimation performance is evaluated using comprehensive simulations. Through this method, accurate soil moisture estimation can be obtained, even in the presence of model mismatch.

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Section: Predict and Update Of States And Covariancementioning

confidence: 99%