Multiple window moving horizon estimation

Al-Matouq, Ali; Vincent, Tyrone L.

doi:10.1016/j.automatica.2014.12.002

Cited by 19 publications

(12 citation statements)

References 27 publications

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“…The full information estimator is an offline post-processing tool. This is where the Moving Horizon Estimator comes into play; it is a full information estimator applied only to a window when a certain number of measurements have been logged [19]- [21]. This enables the problem to be solved online at the cost of losing some accuracy due to not utilizing the full trajectory of the state.…”

Section: )Moving Horizon Estimator (Mhe)mentioning

confidence: 99%

Identification of the Uncertainty Structure to Estimate the Acoustic Release of Chemotherapeutics From Polymeric Micelles

Wadi

Abdel–Hafez

Husseini

2017

IEEE Trans.on Nanobioscience

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This paper estimates the acoustic drug release from micelles after accurately identifying the underlying statistical noise characteristics in experimental data. The drug release is measured as a change in fluorescence as ultrasound is applied. First, the noise structure affecting the process dynamics and the measurement process is identified in terms of statistical covariance of the aforementioned quantities. Then, the identified covariance magnitudes are utilized to estimate the dynamics of drug release. The performance of different filters is investigated. The identified a priori knowledge is used to implement an optimal Kalman filter, a multi-hypothesis Kalman filter, and a variant of the full information estimator (moving horizon estimator) to the problem at hand. The proposed algorithms are initially deployed in a simulation environment, and then the experimental data sets are fed into the algorithms to validate their performance. Experiments span a number of ultrasonic power densities for both non-targeted and targeted polymeric micelles (the targeting being accomplished using the folate moiety). The results suggest that the proposed algorithm, the optimal Kalman filter, performs better than the other two in all tests performed.

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Section: )Moving Horizon Estimator (Mhe)mentioning

confidence: 99%

Identification of the Uncertainty Structure to Estimate the Acoustic Release of Chemotherapeutics From Polymeric Micelles

Wadi

Abdel–Hafez

Husseini

2017

IEEE Trans.on Nanobioscience

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“…The first results on MHE date back to [1], where "limited memory" estimation was proposed as an alternative to the Kalman filter. Nowadays, a vast literature on MHE exists, with a number of results for linear and nonlinear systems [2]- [6], large-scale systems [7]- [9], switching systems [10], [11], descriptor systems [12], and systems affected by uncertainties [13]- [16]. The extent of the literature certifies the effectiveness of the MHE methodology and still motivates the investigation of further developments.…”

Section: Introductionmentioning

confidence: 99%

Fast Moving Horizon State Estimation for Discrete-Time Systems Using Single and Multi Iteration Descent Methods

Alessandri

Gaggero

2017

IEEE Trans. Automat. Contr.

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Descent algorithms based on the gradient, conjugate gradient, and Newton methods are investigated to perform optimization in moving horizon state estimation for discrete-time linear and nonlinear systems. Conditions that ensure the stability of the estimation error are established for single and multi iteration schemes with a least-squares cost function that takes into account only a batch of most recent information. Simulation results show the effectiveness of the proposed approaches also in comparison with techniques based on the Kalman filter.

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“…In more recent works, as in [16], the authors proposed a moving horizon estimation algorithm based on multiple estimation windows. e major advantage of this algorithm is that it reduces the size of the optimization problem while maintaining the performance properties of the estimator and its stability by taking advantage of the inactivity of the constraints.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of the Moving Horizon Estimation Performance Based on an Adaptive Estimation Algorithm

Ouambo

Boum

Imano

et al. 2021

Journal of Control Science and Engineering

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Although moving horizon estimation (MHE) is a very efficient technique for estimating parameters and states of constrained dynamical systems, however, the approximation of the arrival cost remains a major challenge and therefore a popular research topic. The importance of the arrival cost is such that it allows information from past measurements to be introduced into current estimates. In this paper, using an adaptive estimation algorithm, we approximate and update the parameters of the arrival cost of the moving horizon estimator. The proposed method is based on the least-squares algorithm but includes a variable forgetting factor which is based on the constant information principle and a dead zone which ensures robustness. We show by this method that a fairly good approximation of the arrival cost guarantees the convergence and stability of estimates. Some simulations are made to show and demonstrate the effectiveness of the proposed method and to compare it with the classical MHE.

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Multiple window moving horizon estimation

Cited by 19 publications

References 27 publications

Identification of the Uncertainty Structure to Estimate the Acoustic Release of Chemotherapeutics From Polymeric Micelles

Identification of the Uncertainty Structure to Estimate the Acoustic Release of Chemotherapeutics From Polymeric Micelles

Fast Moving Horizon State Estimation for Discrete-Time Systems Using Single and Multi Iteration Descent Methods

Enhancement of the Moving Horizon Estimation Performance Based on an Adaptive Estimation Algorithm

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