Combined input and parameter estimation with input observers and set-membership parameter bounding

Kolmanovsky, Ilya; Sivergina, I.; Sun, Jing

doi:10.1109/cdc.2004.1429632

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Because of the complexity of the propulsion-load torque model, a simplified model is proposed first, which is able to capture the key dynamics. The input observer (IO) presented in Kolmanovsky et al (2006) is used as an approach to estimate the ship load demand, and linear prediction (Vaidyanathan, 2007) is combined with IO to predict the future load demand (Hou et al, 2018). It has advantages in its simplicity and instantaneity, but it concentrates purely on the relationship between speed and power, ignoring fluctuations for ship working conditions.…”

Section: Introductionmentioning

confidence: 99%

A novel load prediction method for hybrid electric ship based on working condition classification

Gao

Jiang

Zhao

2020

Transactions of the Institute of Measurement and Control

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In order to effectively optimize the load distribution between power sources during the navigation of hybrid ships, a method for predicting ship load demand based on real-time classification according to different working conditions is proposed. The k-means clustering algorithm is used to quantify the voyage history data to classify the ship’s navigation conditions into fast-changing conditions and slow-changing conditions. Some characteristic parameters related to working conditions are selected as input. Then, input and the category of working conditions are put into least squares support vector machine to learn and train to get an online working condition classifier. The genetic algorithm is used to optimize the radial-based neural network to predict the load demand under fast-changing conditions, use the Markov chain model to predict the load demand under slow-changing conditions, so as to obtain the most accurate future load demand of the ship. The simulation results show that the proposed prediction models under different conditions have higher precision, which is an effective means of predicting the load demand for hybrid power ships.

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Section: Introductionmentioning

confidence: 99%

A novel load prediction method for hybrid electric ship based on working condition classification

Gao

Jiang

Zhao

2020

Transactions of the Institute of Measurement and Control

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“…Note, however, that system (1), (2) is not in the form treated in Reference [1]. Earlier conference papers [2,3] have described our approach and have included some of the results of this paper.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous input and parameter estimation with input observers and set‐membership parameter bounding: theory and an automotive application

Kolmanovsky

Sivergina

Sun

2006

Adaptive Control & Signal

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The paper addresses an on-line, simultaneous input and parameter estimation problem for a first-order system affected by measurement noise. This problem is motivated by practical applications in the area of engine control. Our approach combines an input observer for the unknown input with a set-membership algorithm to estimate the parameter. The set-membership algorithm takes advantage of a priori available information such as (i) known bounds on the unknown input, measurement noise and time rate of change of the unknown input; (ii) the form of the input observer in which the unknown parameter affects only the observer output; and (iii) the input observer error bounds for the case when the parameter is known exactly. The asymptotic properties of the algorithm as the observer gain increases are delineated. It is shown that for accurate estimation the unknown input needs to approach the known bounds a sufficient number of times (these time instants need not be known). Powertrain control applications are discussed and a simulation example based on application to engine control is reported. A generalization of the basic ideas to higher order systems is also elaborated.

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An Si Engine Load Torque Observer Based on Combined Input and Parameter Estimation

Deur

Kolmanovsky

Hrovat

2007

IFAC Proceedings Volumes

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Combined input and parameter estimation with input observers and set-membership parameter bounding

Cited by 3 publications

References 14 publications

A novel load prediction method for hybrid electric ship based on working condition classification

A novel load prediction method for hybrid electric ship based on working condition classification

Simultaneous input and parameter estimation with input observers and set‐membership parameter bounding: theory and an automotive application

An Si Engine Load Torque Observer Based on Combined Input and Parameter Estimation

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