Linear constrained moving horizon estimator with pre-estimating observer

Sui, Dan; Johansen, Tor Arne

doi:10.1016/j.sysconle.2014.02.003

Cited by 30 publications

(44 citation statements)

References 12 publications

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“…, x t , on the basis of the a priori estimatex t−N,t and I t . The MHE problem proposed by Sui and Johansen (2014) is formulated, as follows,…”

Section: Moving Horizon Estimatormentioning

confidence: 99%

“…Theorem 1 (Sui and Johansen, 2014) Suppose that Assumptions (A1)-(A3) hold. There always exist weight matrices Π > 0 and M > 0 such that the error e t is input-to-state stable (ISS).…”

Section: Moving Horizon Estimatormentioning

confidence: 99%

“…., e t is exponentially stable. Proposition 1 (Sui and Johansen, 2014) Suppose that Assumptions (A1)-(A3) hold. If the weight matrices M, Π satisfy…”

Section: Moving Horizon Estimatormentioning

confidence: 99%

“…Some post-analyses possibly provide good prediction compared with historical data, but models have less ability to look ahead to future ROP. In this paper, an MHE method proposed by Sui and Johansen (2014) for the ROP estimation is employed. The reason of using MHE observer is that it can provide a high degree of robustness in the presence of modeling uncertainties since it is based on a batch of the most recent information/measurements.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the constraints of states and parameters can be naturally considered in the MHE problem, which may lead to the more accurate ROP estimation. Sui and Johansen (2014) proposed a novel MHE observer where the states are estimated by a forward simulation with a pre-estimating observer. Compared with standard MHE approaches, it has additional degrees of freedom to optimize the noise and disturbance filtering through the pre-estimator, see more discussions in Sui and Johansen (2014).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Rate of Penetration Optimization using Moving Horizon Estimation

Sui¹,

Aadnøy²

2016

MIC

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Increase of drilling safety and reduction of drilling operation costs, especially improvement of drilling efficiency, are two important considerations in the oil and gas industry. The rate of penetration (ROP, alternatively called as drilling speed) is a critical drilling parameter to evaluate and improve drilling safety and efficiency. ROP estimation has an important role in drilling optimization as well as interpretation of all stages of the well life cycle. In this paper, we use a moving horizon estimation (MHE) method to estimate ROP as well as other drilling parameters. In the MHE formulation the states are estimated by a forward simulation with a pre-estimating observer. Moreover, it considers the constraints of states/outputs in the MHE problem. It is shown that the estimation error is with input-to-state stability. Furthermore, the ROP optimization (to achieve minimum drilling cost/drilling energy) concerning with the efficient hole cleaning condition and downhole environmental stability is presented. The performance of the methodology is demonstrated by one case study.

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“…, x t , on the basis of the a priori estimatex t−N,t and I t . The MHE problem proposed by Sui and Johansen (2014) is formulated, as follows,…”

Section: Moving Horizon Estimatormentioning

confidence: 99%

“…Theorem 1 (Sui and Johansen, 2014) Suppose that Assumptions (A1)-(A3) hold. There always exist weight matrices Π > 0 and M > 0 such that the error e t is input-to-state stable (ISS).…”

Section: Moving Horizon Estimatormentioning

confidence: 99%

“…., e t is exponentially stable. Proposition 1 (Sui and Johansen, 2014) Suppose that Assumptions (A1)-(A3) hold. If the weight matrices M, Π satisfy…”

Section: Moving Horizon Estimatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Rate of Penetration Optimization using Moving Horizon Estimation

Sui¹,

Aadnøy²

2016

MIC

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Fast moving horizon state estimation for discrete‐time systems with linear constraints

Alessandri

Gaggero

2019

Adaptive Control & Signal

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Summary Fast moving horizon state estimation for nonlinear discrete‐time systems affected by disturbances is addressed by means of imperfect optimization at each time instant based on few iterations of the gradient, conjugate gradient, and Newton algorithms. Linear constraints on the state vector are taken into account through a projection on the subspace associated with such constraints. The stability of the estimation error for the resulting scheme is proved under suitable conditions. The effectiveness of the proposed approach is showcased via simulation results in comparison with moving horizon estimation based on complete optimization and extended Kalman filtering.

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A proximity moving horizon estimator for a class of nonlinear systems

Gharbi

Ebenbauer

2020

Adaptive Control & Signal

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Summary In this article, we present a proximity‐based formulation for moving horizon estimation (MHE) of a class of constrained discrete‐time nonlinear systems. The cost function of the proposed estimator includes a convex stage cost as well as a Bregman distance for the a priori estimate. This rather general formulation of the cost function allows for a flexible design of the estimator depending on the setting of the problem at hand. We derive sufficient conditions for the global exponential stability of the underlying estimation error. We also investigate the robustness properties of the proposed MHE scheme applied for linear systems subject to unknown process and measurement disturbances in terms of an input‐to‐state stability analysis. The estimator performance is illustrated by means of simulation examples.

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Linear constrained moving horizon estimator with pre-estimating observer

Cited by 30 publications

References 12 publications

Rate of Penetration Optimization using Moving Horizon Estimation

Rate of Penetration Optimization using Moving Horizon Estimation

Fast moving horizon state estimation for discrete‐time systems with linear constraints

A proximity moving horizon estimator for a class of nonlinear systems

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