A consensus-based approach for parameter and state estimation of agro-hydrological system

TL;DR

This paper presents a robust, consensus-based estimation method combining distributed Kalman and horizon filtering to accurately estimate soil moisture and model parameters in agro-hydrological systems, even with poor initial guesses.

Contribution

It introduces a novel combined distributed estimation approach for simultaneous parameter and state estimation in nonlinear agro-hydrological models.

Findings

Accurate soil moisture estimation achieved with poor initial guesses.

The proposed method outperforms existing algorithms in robustness and accuracy.

Simulations confirm the effectiveness of the consensus-based approach.

Abstract

The development of advanced closed-loop irrigation systems requires accurate soil moisture information. In this work, we address the problem of soil moisture estimation for the agro-hydrological systems in a robust and reliable manner. A nonlinear state-space model is established based on the discretization of the Richards equation to describe the dynamics of agro-hydrological systems. We consider that model parameters are unknown and need to be estimated together with the states simultaneously. We propose a consensus-based estimation mechanism, which comprises two main parts: 1) a distributed extended Kalman filtering algorithm used to estimate several model parameters; and 2) a distributed moving horizon estimation algorithm used to estimate the state variables and one remaining model parameter. Extensive simulations are conducted, and comparisons with existing methods are made to demonstrate the effectiveness and superiority of the proposed approach. In particular, the proposed approach can provide accurate soil moisture estimate even when poor initial guesses of the parameters and the states are used, which can be challenging to be handled using existing algorithms.