Adaptive Kalman Filter for Navigation Sensor Fusion

Jwo, Dah‐Jing; Chung, Fong-Chi; Weng, Tsu-Pin

doi:10.5772/9957

Cited by 20 publications

(13 citation statements)

References 22 publications

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“…UGKF yöntemi, kestirilen durum vektörünün varyansının artırılmasına yönelik olarak kestirim hatasının kovaryans matrisine ölçekleme faktörü 'nın dahil edilmesiyle geleneksel GKF yönteminden farklılaşmaktadır [29]. UGKF yöntemine ait eşitlikler aşağıdaki gibi verilmiştir [26].…”

Section: Tam Dereceli Uyarlamalı Genişletilmiş Kalman Filtresi Algoriunclassified

Design of Adaptive Extended Kalman Filter-Based Speed-Sensorless Direct Torque Controlled Drive System with Constant Switching Frequency

İnan¹,

Zerdali²

2020

Pamukkale J Eng Sci

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Bu çalışmada, hız-algılayıcısız asenkron motor (ASM) kontrolü için uyarlamalı genişletilmiş Kalman filtresi (UGKF) tabanlı sabit anahtarlama frekanslı doğrudan moment kontrollü (SAF-DMK'lı) sürücü sisteminin tasarımı gerçekleştirilmektedir. Stator akısı tabanlı ASM modelini kullanan UGKF ile GKF'lerin kestirim başarımını doğrudan etkileyen ve geleneksel GKF'lerde sabit olarak kabul edilen sistem gürültüsü kovaryans matrisi çalışma koşullarına göre çevrimiçi olarak güncellenmektedir. UGKF'nin geleneksel DMK'lı sürücü yerine, SAF-DMK'lı sürücüye dahil edilmesinin nedeni geleneksel DMK kontrolündeki moment dalgalanmalarının ve değişken anahtarlama frekansından kaynaklanan anahtarlama kayıplarının azaltılmasıdır. SAF-DMK'lı sürücünün ihtiyaç duyduğu stator akısının stator duran eksen takımı bileşenlerine ve rotor mekanik açısal hızına ek olarak, stator akımının stator duran eksen takımı bileşenleri ve yük momenti önerilen UGKF tabanlı gözlemleyici ile kestirilmektedir. Yük momenti kestirimi ile önerilen hız-algılayıcısız sürücü sisteminin bozucu yük momenti değişimlerine karşı dayanıklı olması sağlanmaktadır. Son olarak, önerilen UGKF-tabanlı hız-algılayıcısız SAF-DMK'lı sürücü sistemi benzetim çalışmaları altında doğrulanmaktadır. In this study, the design of the adaptive extended Kalman filter (AEKF) based direct torque controlled drive system with constant switching frequency (DTCD-CSF) is performed for speed-sensorless control of induction motor (IM). The system noise covariance matrix, which directly affects the estimation performance of EKFs and is assumed as constant in conventional EKFs, is updated online according to the operating conditions by the AEKF, which uses stator flux-based IM model. The reason why the proposed algorithm is included on a DTCD-CSF, instead of a conventional DTCD, is to reduce the torque ripples in the traditional direct torque control and the switching losses caused by the variable switching frequency. The load torque and the stator stationary axis components of stator currents in addition to the stator stationary axis components of stator fluxes and the rotor mechanical speed required for the DTCD-CSF are estimated by the proposed AEKF-based observer. By the load torque estimation, it is ensured that the proposed speed-sensorless drive system is robust to changes in load torque. Finally, the proposed AEKF-based speedsensorless DTCD-CSF is verified under simulation studies.

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Section: Tam Dereceli Uyarlamalı Genişletilmiş Kalman Filtresi Algoriunclassified

Design of Adaptive Extended Kalman Filter-Based Speed-Sensorless Direct Torque Controlled Drive System with Constant Switching Frequency

İnan¹,

Zerdali²

2020

Pamukkale J Eng Sci

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“…(16) should be equal. So the ratio of the actual innovation covariance based on the sampled sequence to the theoretical innovation covariance will be employed for dynamically tuning filter parameters [6], as Eq. 11shows.…”

Section: Dual-frequency Tuning Strategymentioning

confidence: 99%

“…The innovation-based covariance matching technique is a popular method. It calculates the covariance estimation with limited sample in a window and attempts to make the filter theoretical covariance consistent with it [5,6].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Huber-Based Filter for Hypersonic Cruise Vehicle Navigation

Wang

Liu

Zhong

2014

Journal of Algorithms & Computational Technology

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The navigation for hypersonic cruise vehicle (HCV) is a challenging task because of the complex vehicle dynamic and sensor measurement noise it suffered. This paper proposes a kind of adaptive robust Kalman filter using Mahalanobis distance for HCV navigation. The innovation-based adaptive estimation is discussed first. Based on Mahalanobis distance theory, a kind of robust covariance matrix estimation method is used to modify the innovation-based adaptive Kalman filter. Considering that the vehicle maneuver characteristics and noise statistics parameters varies during different periods, dual-frequency tuning for unknown noise statistics is designed based on this. The algorithm proposed by this paper is applied to hypersonic cruise vehicle navigation. Simulation has been made to verify the performance of the new algorithm according to HCV flight profile and characteristics; both Gaussian and non-Gaussian simulation are included.

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“…Using the innovation-based covariance matching ideas discussed in Chapter 1, the proposed fuzzy adaptive update laws are based on the discrepancies between the actual observed Ąlter covariance and the theoretical covariance bounds suggested by the Ąlter. The Ąrst performance metric selected for adapting the EKF has been demonstrated by several other authors [59,60,118], and is coined the Degree of Mismatch (DOM). The DOM matrix η DOM ∈ R m×m is simply the element-by-element difference between the theoretical covariance of the residuals Σ k ∈ R m×m calculated within the EKF, and the experimental covariance of the residuals sampled from the Ąlter outputΣ k♣N ∈ R m×m , which have previously been presented in Eqs.…”

Section: Criteria For Innovation-based Covariance Matchingmentioning

confidence: 99%

“…Another parameter constructed to detect sub-optimal performance of the Ąlter is the Degree of Divergence (DOD) [118,119], which will be denoted by η DOD ∈ R. Evaluating the trace of the residual covariance matrix gives a scalar value ξ ∈ R that can be used to indicate overall divergence of the Ąlter, or outliers within the data. These traces can be identiĄed for both the theoretical RCM and the observed RCM, as denoted by ξ andξ, respectively:…”

Section: Criteria For Innovation-based Covariance Matchingmentioning

confidence: 99%

Adaptive Extended Kalman Filtering Strategies for Autonomous Relative Navigation of Formation Flying Spacecraft

Fraser¹

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This research addresses the relative navigation problem for spacecraft formation Ćying missions in near-Earth orbit. Technological and economic inĆuences have expedited the miniaturization of spacecraft systems, popularizing the notion of satellite teamwork due to the potential reductions in overall costs and complexity. It is now feasible to use a coordinated group of close-proximity satellites, known as a formation, to autonomously perform rendezvous, on-orbit servicing and science-based missions for a fraction of the resources a large-scale satellite would require. Despite the growing interest in formation Ćying spacecraft however, only a limited number of applications have been Ćight validated as a result of the difficulties associated with accurately determining the relative motion of the spacecraft within a formation. To enhance the capabilities of onboard autonomous guidance, navigation and control systems, this thesis presents the development of two adaptive extended Kalman Ąlter navigation algorithms for spacecraft formation Ćying. The proposed adaptive Ąlters are capable of updating the internal noise characteristics of the Kalman Ąlter in real time, and are viable in all orbit scenarios, including highly elliptical orbits in the presence of perturbations. The Ąrst Kalman Ąlter approach uses maximum likelihood estimation techniques to derive analytical adaptations laws for the Ąlter, which are then improved through the novel inclusion of an intrinsic smoothing routine. The second approach uses an embedded fuzzy logic system based on a covariancematching analysis of the Ąlter residuals, where the fuzzy system has been speciĄcally designed for the spacecraft navigation problem at hand. Numerical simulations of three spacecraft formations are used to demonstrate that the proposed adaptive navigation algorithms are appreciably more robust to Ąlter initialization errors, dynamics modelling deĄciencies, and measurement noise than the standard Kalman Ąlter. iii To Dr. Steve Ulrich, formally my supervisor, but more importantly my mentor, my professor, and my friend; your steadfast support and leadership have made my studies in Ottawa immensely fulĄlling. Thank you for providing this opportunity to continually learn from your expertise, and for instilling conĄdence and direction throughout the span of our collaboration. The dedication you have shown to me, and all of the members of our research group, is remarkable. It has been an honor to work with you, and a whole lot of fun too. To the professors who have guided me along this journey, including Bruce Burlton, Tarik Kaya, Alex Ellery, Victor Aitken, Howard Schwartz, and many others from Carleton University. The knowledge and wisdom you have shared has been invaluable to my growth as an engineer. And from the days at Dalhousie University, my thanks in particular to Professors Darrel Doman, Jeff Dahn, Timothy Little, and Guy Kember. You managed to imbue a fascination with space elevators, the laws of nature, the rules of mathematics, and the process of learning,...

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Adaptive Kalman Filter for Navigation Sensor Fusion

Cited by 20 publications

References 22 publications

Design of Adaptive Extended Kalman Filter-Based Speed-Sensorless Direct Torque Controlled Drive System with Constant Switching Frequency

Design of Adaptive Extended Kalman Filter-Based Speed-Sensorless Direct Torque Controlled Drive System with Constant Switching Frequency

Adaptive Huber-Based Filter for Hypersonic Cruise Vehicle Navigation

Adaptive Extended Kalman Filtering Strategies for Autonomous Relative Navigation of Formation Flying Spacecraft

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