Parameter identification of ship motion model based on multi-innovation methods

Xie, Shuguo; Chu, Xiumin; Liu, Chenguang; Liu, Jialun; Mou, Junmin

doi:10.1007/s00773-019-00639-y

Cited by 43 publications

(21 citation statements)

References 20 publications

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“…In previous work, Xie et al [38] discussed the performance of KF with fading factors and raised that accumulative interference may occur if the effect of old measurements and new measurements both are equally weighted. According to the research of Liu et al [30], the influence of new measurement should be greater than that of old measurements.…”

Section: ) Multi-innovation Enhanced Ukfmentioning

confidence: 99%

Slip-Aware Motion Estimation for Off-Road Mobile Robots via Multi-Innovation Unscented Kalman Filter

Liu

Yuan

et al. 2020

IEEE Access

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Benefiting from high mobility and robust mechanical structure, ground mobile robots are widely adopted in the outdoor environment. The mobility of skid-steered mobile robots highly depends on the nonlinear and uncertain interaction between the tire and terrain. This paper introduces an approach to estimate the position, orientation, velocity, and wheel slip for the skid-steered mobile robots navigating on off-road terrains. More specifically, a Multi-Innovation Unscented Kalman Filter (MI-UKF) is developed to fusing different sensors' data. Historical innovations generated along the time sequence are merged into the update process of standard UKF to improve the accuracy of motion estimation. In the proposed estimator, an asymmetric ICR kinematic indicating wheel slip is taken into localization process. A four-wheeled prototype is introduced and three challenging test scenarios are designed. The improvements in orientation and velocity estimation are achieved according to results comparison. In the turning maneuver, the ICRs-based model operates more steady than the traditional wheel slip/skid model.

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Section: ) Multi-innovation Enhanced Ukfmentioning

confidence: 99%

Slip-Aware Motion Estimation for Off-Road Mobile Robots via Multi-Innovation Unscented Kalman Filter

Liu

Yuan

et al. 2020

IEEE Access

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“…In addition, the influence of new measurement should be greater than that of old measurement. From the discussion of KF with fading factors [38], the adverse effect of old measurement and new measurement both are equally weighted, which may result in accumulative interference. In this case, different weighting factors are introduced into different innovations to reduce correction effect of old data.…”

Section: Multi-innovation Based Mi-aekfmentioning

confidence: 99%

A Novel Open Circuit Voltage Based State of Charge Estimation for Lithium-Ion Battery by Multi-Innovation Kalman Filter

2019

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Accurate state of charge (SOC) estimation is a fundamental guarantee for effective development of lithium-ion power battery in electric vehicles. To improve the SOC estimation precision and robustness, a novel model-based estimation approach has been proposed. Fully giving consideration to the effect of measurement errors, the dynamic external electrical property of lithium-ion battery is approximated by a controlled auto-regressive and moving average (controlled ARMA)-based equivalent circuit model. An improved adaptive extended Kalman filter approach is developed for SOC estimation based on the multiinnovation principle. Meanwhile, the different weighting factor is added into each innovation to reduce cumulative influence of historical interference. Since the flat characteristic in OCV-SOC fitting curve enlarges the OCV-based SOC estimation error, a feedforward compensation method is introduced to reduce OCV identification error to improve OCV-based SOC estimation. The simulation and experimental results verify the validity of the proposed methodology over other estimation methods. Besides, simulated current noise is added to the condition data to prove the high precision and strong robustness of the proposed algorithm. INDEX TERMS Lithium-ion battery, state of charge, Kalman filter, multi-innovation, OCV compensation.

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“…The basic idea is to expand the scalar innovation into the innovation vector or the innovation vector into the innovation matrix, so as to achieve the purpose of reusing the data of the collected data at the previous time. [35][36][37] The multiinnovation identification method has been widely utilized in system identification these years. Cheng et al 38 investigated a multiinnovation fractional-order stochastic gradient algorithm to identify the Hammerstein nonlinear ARMAX systems.…”

Section: Introductionmentioning

confidence: 99%

Filtering‐based recursive least squares estimation approaches for multivariate equation‐error systems by using the multiinnovation theory

Wang

2021

Adaptive Control & Signal

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This article researches the filtering-based parameter estimation issues for a class of multivariate control systems with colored noise. A filtering-based recursive generalized extended least squares algorithm is derived, in which the data filtering technique is used for transforming the original system into two subidentification systems and the least squares principle is used for estimating parameters of these two subsystems. Furthermore, in order to improve the parameter estimation accuracy, the multiinnovation theory is added for deducing a filtering-based multiinnovation recursive generalized extended least squares algorithm.The numerical example confirms that these two proposed algorithms are effective.

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Parameter identification of ship motion model based on multi-innovation methods

Cited by 43 publications

References 20 publications

Slip-Aware Motion Estimation for Off-Road Mobile Robots via Multi-Innovation Unscented Kalman Filter

Slip-Aware Motion Estimation for Off-Road Mobile Robots via Multi-Innovation Unscented Kalman Filter

A Novel Open Circuit Voltage Based State of Charge Estimation for Lithium-Ion Battery by Multi-Innovation Kalman Filter

Filtering‐based recursive least squares estimation approaches for multivariate equation‐error systems by using the multiinnovation theory

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