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Academic literature on the topic 'Unscented Kalman observer'

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Published: 25 May 2024

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Journal articles on the topic "Unscented Kalman observer"

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Ongkosutjahjo, Martin, and Victor M. Becerra. "INTEGRATING THE UTKIN OBSERVER WITH THE UNSCENTED KALMAN FILTER." IFAC Proceedings Volumes 41, no. 2 (2008): 12534–39. http://dx.doi.org/10.3182/20080706-5-kr-1001.02121.

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Wan, Wenkang, Jingan Feng, Bao Song, and Xinxin Li. "Huber-Based Robust Unscented Kalman Filter Distributed Drive Electric Vehicle State Observation." Energies 14, no. 3 (February 1, 2021): 750. http://dx.doi.org/10.3390/en14030750.

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Accurate and real-time acquisition of vehicle state parameters is key to improving the performance of vehicle control systems. To improve the accuracy of state parameter estimation for distributed drive electric vehicles, an unscented Kalman filter (UKF) algorithm combined with the Huber method is proposed. In this paper, we introduce the nonlinear modified Dugoff tire model, build a nonlinear three-degrees-of-freedom time-varying parametric vehicle dynamics model, and extend the vehicle mass, the height of the center of gravity, and the yaw moment of inertia, which are significantly influenced by the driving state, into the vehicle state vector. The vehicle state parameter observer was designed using an unscented Kalman filter framework. The Huber cost function was introduced to correct the measured noise and state covariance in real-time to improve the robustness of the observer. The simulation verification of a double-lane change and straight-line driving conditions at constant speed was carried out using the Simulink/Carsim platform. The results show that observation using the Huber-based robust unscented Kalman filter (HRUKF) more realistically reflects the vehicle state in real-time, effectively suppresses the influence of abnormal error and noise, and obtains high observation accuracy.
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Yang, Rong Jun, and Yao Ye. "Drag Coefficient Identification from Flight Data via Optimal Observer." Applied Mechanics and Materials 687-691 (November 2014): 787–90. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.787.

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. For effectively using flight test data to extract drag coefficient, an optimal observer based on parameter estimation technique is proposed. The point mass dynamic equation is used to form the Unscented Kalman Filter (UKF) and the smoother (URTSS) for the estimation of a projectile’s flight states. The projectile flight states are then solved and utilized to extract the drag coefficient information using the observer techniques. The simulation verifies the feasibility of the method: with measurement noise, the accurate drag coefficient is obtained by using the smoother.
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Chen, Jian Feng, Xiao Dong Sun, Long Chen, and Hao Bin Jiang. "Load Torque Observer Design of PMSMs for EVs Based on Square-Root Unscented Kalman Filtering." Applied Mechanics and Materials 668-669 (October 2014): 615–18. http://dx.doi.org/10.4028/www.scientific.net/amm.668-669.615.

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Varying load torque is an important influence factor in speed tracking of PMSM for EVs. This paper presents a PMSM control strategy using load torque observer. After introducing the entire structure of PMSM control system, a state observer is described based on square-root unscented Kalman filtering. Simulation tests are carried out to examine the speed tracking performance of PMSM compensated with the state observer. The results demonstrate that the proposed method is superior to another one using SMC in control accuracy and regulatory time.
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Chen, Yong, Hao Yan, and Yuecheng Li. "Vehicle State Estimation Based on Sage–Husa Adaptive Unscented Kalman Filtering." World Electric Vehicle Journal 14, no. 7 (June 25, 2023): 167. http://dx.doi.org/10.3390/wevj14070167.

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To combat the impacts of uncertain noise on the estimation of vehicle state parameters and the high cost of sensors, a state-observer design with an adaptive unscented Kalman filter (AUKF) is developed. The design equation of the state observer is derived by establishing the vehicle’s three degrees-of-freedom (DOF) model. On this basis, the Sage–Husa algorithm and unscented Kalman filter (UKF) are combined to form the AUKF algorithm to adaptively update the statistical feature estimation of measurement noise. Finally, a co-simulation using Carsim and Matlab/Simulink confirms the algorithm is effective and reasonable. The simulation results demonstrate that the proposed algorithm, compared with the UKF algorithm, increases estimation accuracy by 19.13%, 32.8%, and 39.46% in yaw rate, side-slip angle, and longitudinal velocity, respectively. This is because the proposed algorithm adaptively adjusts the measurement noise covariance matrix, which can estimate the state parameters of the vehicle more accurately.
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Qiu, Li Bo, Hui Yi Su, Hao Hu, Sheng Lin Huang, Jie Wang, and Ting Jun Li. "Application of UKF Algorithm in Airborne Single Observer Passive Location." Advanced Materials Research 267 (June 2011): 356–62. http://dx.doi.org/10.4028/www.scientific.net/amr.267.356.

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Airborne Single Observer Passive Location have the characteristics of mobility and a wide range impaction, while location method based on the rate change of phase difference also has the characteristic of getting the position quickly and having a high precision. Studied the Unscented Kalman Filter (UKF) apply in Airborne Single Observer Passive Location. It gave out the principle of the position method based on the rate change of phase difference. And it introduced the filtering principle and the filtering process of the UKF algorithm. The simulation results show that, UKF algorithm used in Airborne Single Observer Passive Location have an accurately positioning and rapid convergence.
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Li, Zhao, Yu, and Wei. "Underwater Bearing-only and Bearing-Doppler Target Tracking Based on Square Root Unscented Kalman Filter." Entropy 21, no. 8 (July 28, 2019): 740. http://dx.doi.org/10.3390/e21080740.

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Underwater target tracking system can be kept covert using the bearing-only or the bearing-Doppler measurements (passive measurements), which will reduce the risk of been detected. According to the characteristics of underwater target tracking, the square root unscented Kalman filter (SRUKF) algorithm, which is based on the Bayesian theory, was applied to the underwater bearing-only and bearing-Doppler non-maneuverable target tracking problem. Aiming at the shortcomings of the unscented Kalman filter (UKF), the SRUKF uses the QR decomposition and the Cholesky factor updating, in order to avoid that the process noise covariance matrix loses its positive definiteness during the target tracking period. The SRUKF uses sigma sampling to avoid the linearization of the nonlinear bearing-only and the bearing-Doppler measurements. To ensure the target state observability in underwater target tracking, the paper uses single maneuvering observer to track the single non-maneuverable target. The simulation results show that the SRUKF has better tracking performance than the extended Kalman filter (EKF) and the UKF in tracking accuracy and stability, and the computational complexity of the SRUKF algorithm is low.
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Rayyam, Marouane, Malika Zazi, and Youssef Barradi. "A new metaheuristic unscented Kalman filter for state vector estimation of the induction motor based on Ant Lion optimizer." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 37, no. 3 (May 8, 2018): 1054–68. http://dx.doi.org/10.1108/compel-06-2017-0239.

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PurposeTo improve sensorless control of induction motor using Kalman filtering family, this paper aims to introduce a new metaheuristic optimizer algorithm for online rotor speed and flux estimation.Design/methodology/approachThe main problem with unscented Kalman filter (UKF) observer is its sensibility to the initial values of Q and R. To solve the optimal solution of these matrices, a novel alternative called ant lion optimization (ALO)-UKF is introduced. It is based on the combination of the classical UKF observer and a nature-inspired metaheuristic algorithm, ALO.FindingsSynthesized ALO-UKF has given good results over the famous extended Kalman filter and the classical UKF observer in terms of accuracy and dynamic performance. A comparison between ALO and particle swarm optimization (PSO) was established. Simulations illustrate that ALO recovers rapidly and accurately while PSO has a slower convergence.Originality/valueUsing the proposed approach, tuning the design matrices Q and R in Kalman filtering becomes an easy task with a high degree of accuracy and the constraints of time cost are surmounted. Also, ALO-UKF is an efficient tool to improve estimation performance of states and parameters’ uncertainties of the induction motor. Related optimization technique can be extended to faults monitoring by online identification of their corresponding signatures.
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Wang, Taipeng, Sizhong Chen, Hongbin Ren, and Yuzhuang Zhao. "Model-based unscented Kalman filter observer design for lithium-ion battery state of charge estimation." International Journal of Energy Research 42, no. 4 (December 12, 2017): 1603–14. http://dx.doi.org/10.1002/er.3954.

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Mei, Mingming, Shuo Cheng, Liang Li, Hongyuan Mu, and Yuxuan Pei. "UKF-Based Observer Design for the Electric Brake Booster in Situations of Disturbance." Actuators 12, no. 3 (February 22, 2023): 94. http://dx.doi.org/10.3390/act12030094.

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The motor-driven electric brake booster (E-Booster) can replace the traditional vacuum booster to realize the braking power assistance and active braking. Independent of extra sensors, this paper proposes a full-state observer for E-Booster based on Unscented Kalman Filter (UKF) in the presence of a driver’s input force disturbance. The electro-hydraulic system is first modeled, which includes a nonlinear hydraulic model and the reaction disk’s rubber model. The pre-compression is designed to produce linear power assistance based on the properties of rubber material. With the existence of the disturbance, the linear quadratic regulator (LQR) algorithm is used to track the pre-compression of the reaction disk so that E-Booster is developed into a closed-loop system to achieve power assistance. The proposed UKF observer can online estimate the states considering the controller input and disturbance input. To reduce the process error, the hydraulic p-V characteristic is fitted using the recursive least squares (RLS) method before observation. Furthermore, the simulation test and vehicle tests are performed to validate the observation effect. In the closed-loop test, UKF decreases residual error by 16% when compared to the typical Extended Kalman Filter (EKF). The simulation results remain consistent with the experimental results, demonstrating the effectiveness of the proposed method.
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Dissertations / Theses on the topic "Unscented Kalman observer"

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Daid, Assia. "Sur la convergence d’unscented Kalman filter." Electronic Thesis or Diss., Toulon, 2021. http://www.theses.fr/2021TOUL0013.

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Notre travail consiste à étudier les propriétés du filtre unscented Kalman filter "UKF" et son adaptation en tant qu'observateur non linéaire. Il a été développé mais, bien que donnant en pratique de meilleurs résultats, aucune preuve de convergence n’existe qui garantit son utilisation.Un résultat négatif a été obtenu (sa non-convergence). Ce qui nous a conduit à proposer une nouvelle version : "unscented Kalman observer" pour les systèmes à temps continu dans un cadre déterministe. Nous avons montré sa convergence, lorsque les erreurs d'estimations initiales sont suffisamment petites. Cette démonstration repose essentiellement sur l'existence de bornes de la solution de l'équation de Riccati qui est perturbé par un terme non Linéaire.On a aussi développer une version grand gain de l'observateur UKF, qui est un compromis entre le filtre de Kalman étendu grand gain (EKF) et le unscented Kalman filter grand grand (HG-UKF). Toutes ces propriétés sont illustrées sur l'exemple de la colonne de distillation binaire et sur un exemple de géolocalisation
The present thesis is a study of the convergence of the unscented Kalman filter. A convergence analysis of the modified unscented Kalman filter ( used as an observer for a class of nonlinear deterministic continuous time systems, is presented. Under certain conditions, the extended Kalman filter ( is an exponential observer for non linear systems, i.e., the dynamics of the estimation error is exponentially stable. It is shown that unlike the EKF, the UKF is not an expo nentially converging observer. A modification of the UKF the unscented Kalman observer ( is proposed, which is a better candidate for an observer, we proved the exponentialconvergence of the UKO and also shown that the high gain UKF observer as a compromise between the high gain extended Kalman filter (HG EKF) and the high gain unscented Kalman filter (HG UKF). All these properties are illustrated on the example of the binary distillation column and on an example of geolocalization
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Ongkosutjahjo, Martin. "Development of variable structure observers and their integration with the unscented kalman filter." Thesis, University of Reading, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.515883.

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Exposito, Garcia Adrian. "Investigation on Model Based Observers for SpaceStructure Load Characterization." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-60732.

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The experimental determination of dynamic characteristics of elastic structures, in particularof space flight related structures typically is performed by experimental modal analysis(EMA) or output-only modal analysis (OMA). This document is focused on the OMA methodsand state-space modelling, the motivation for this approach is the possibility to monitorthe real loading of a structure in order to provide a loading history which may be used foran assessment of safe remaining life once the dynamic characteristics has been determined. Previous work has demonstrated that Extened Kalman Filter is not sufficient in thecase when the forces are unkown and the only resource available are the responses of thestructure. In this research a new method called Unscented Kalman Filter is investigated andimplemented, proving its capability to obtain a better approximation of the elastic structurebehaviour and a correction of the modal parameters.
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Idrissi, Imane. "Contribution au Diagnotic des Défauts de la Machine Asynchrone Doublement Alimentée de l'Eolienne à Vitesse Variable." Thesis, Normandie, 2019. http://www.theses.fr/2019NORMR033/document.

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Actuellement, les machines Asynchrones à Double Alimentation (MADA) sont omniprésentes dans le secteur éolien, grâce à leur simplicité de construction, leur faible coût d’achat et leur robustesse mécanique ainsi que le nombre faible d’interventions pour la maintenance. Cependant, comme toute autre machine électrique, ces génératrices sont sujettes aux défauts de différent ordre (électrique, mécanique, électromagnétique…) ou de différents types (capteur, actionneur ou composants du système). C’est pourquoi, il est primordial de concevoir une approche de diagnostic permettant de manière anticipée, de détecter, localiser et identifier tout défaut ou anomalie pouvant altérer le fonctionnement sain de ce type de machine. Motivés par les points forts des méthodes de diagnostic de défauts à base d’observateurs, nous proposons d’une part, dans cette thèse, une approche de détection, localisation et identification des défauts de la MADA d’une éolienne à vitesse variable, à base des observateurs de Kalman, performants et largement utilisés. Les erreurs d’estimation d’état du filtre de Kalman linéaire et de ses variantes non-linéaires, à noter : le Filtre de Kalman Etendu (EKF) et le Filtre de Kalman sans-Parfum (UKF), sont utilisés comme résidus sensibles aux défauts. En vue d’éviter les fausses alarmes et de découpler les défauts des perturbations et des bruits, l’analyse des résidus générés est réalisée par des tests statistiques tels que : Test de Page Hinkley (PH) et Test DCS (Dynamic Cumulative Sum). Pour la localisation des défauts multiples et simultanés, la Structure d’Observateurs Dédiés (DOS) et la Structure d’Observateurs Généralisés (GOS) sont appliquées. De plus, l’amplitude du défaut est déterminée dans l’étape d’identification de défaut. Les défauts capteurs, actionneurs et composants de la MADA, sont traités dans ce travail de recherche. D’autre part, une étude comparative entre les différents observateurs de Kalman, est élaborée. La comparaison porte sur les critères suivants : le temps de calcul, la précision et la vitesse de convergence des estimations
Actually, the Doubly Fed Induction Generators (DFIG) are omnipresent in the wind power market, owing to their construction simplicity, their low purchase cost and their mechanical robustness. However, as any other electrical machine, these generators are subject to defects of different order (electrical, mechanical, electromagnetic ...) or of different type (sensor, actuator or system). That’s why, it is important to design an effective diagnostic approach, able to early detect, locate and identify any defect or abnormal behavior, which could undermine the healthy operation of this machine On the one hand, motivated by the observer-based fault diagnosis methods strengths, we proposed, in this thesis, a diagnostic approach for the faults detection, localization and identification of the DFIG used in variable speed wind turbine. This approach is based on the use of the efficient and widely used Kalman observers. The state estimation errors of the linear Kalman filter and the non-linear Kalman filters, named: The Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF) are used as faults sensitive residuals. In order to avoid false alarms and to decouple faults from disturbances and noises, the faults detection is carried out by the analysis of the residuals generated, by the mean of statistical tests such as: Hinkley Page Test (PH) and DCS Test (Dynamic) Cumulative Sum). For the localization step in case of multiple and simultaneous faults, the Dedicated Observer scheme (DOS) and the Generalized Observer scheme (GOS) are applied. In addition, the fault level is determined in the fault identification step. Sensor faults, actuator and system faults of DFIG, are treated in this research work. On the other hand, a comparative study between the three Kalman observers proposed is performed. The comparison was done in terms of (1) the computation time, (2) the estimation accuracy, and (3) the convergence speed
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Bolandhemmat, Hamidreza. "Distributed Sensing and Observer Design for Vehicles State Estimation." Thesis, 2009. http://hdl.handle.net/10012/4471.

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A solution to the vehicle state estimation problem is given using the Kalman filtering and the Particle filtering theories. Vehicle states are necessary for an active or a semi-active suspension control system, which is intended to enhance ride comfort, road handling and stability of the vehicle. Due to a lack of information on road disturbances, conventional estimation techniques fail to provide accurate estimates of all the required states. The proposed estimation algorithm, named Supervisory Kalman Filter (SKF), consists of a Kalman filter with an extra update step which is inspired by the particle filtering technique. The extra step, called a supervisory layer, operates on the portion of the state vector that cannot be estimated by the Kalman filter. First, it produces N randomly generated state vectors, the particles, which are distributed based on the Kalman filter’s last updated estimate. Then, a resampling stage is implemented to collect the particles with higher probability. The effectiveness of the SKF is demonstrated by comparing its estimation results with that of the Kalman filter and the particle filter when a test vehicle is passing over a bump. The estimation results confirm that the SKF precisely estimates those states of the vehicle that cannot be estimated by either the Kalman filter or the particle filter, without any direct measurement of the road disturbance inputs. Once the vehicle states are provided, a suspension control law, the Skyhook strategy, processes the current states and adjusts the damping forces accordingly to provide a better and safer ride for the vehicle passengers. This thesis presents a novel systematic and practical methodology for the design and implementation of the Skyhook control strategy for vehicle’s semi-active suspension systems. Typically, the semi-active control strategies (including the Skyhook strategy) have switching natures. This makes the design process difficult and highly dependent on extensive trial and error. The proposed methodology maps the discontinuous control system model to a continuous linear region, where all the time/frequency design techniques, established in the conventional control system theory, can be applied. If the semiactive control law is designed to satisfy ride and stability requirements, an inverse mapping offers the ultimate control law. The effectiveness of the proposed methodology in the design of a semi-active suspension control system for a Cadillac SRX 2005 is demonstrated by real-time road tests. The road tests results verify that the use of the newly developed systematic design methodology reduces the required time and effort in real industrial problems.
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Conference papers on the topic "Unscented Kalman observer"

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Daid, Assia, Eric Busvelle, and Mohamed Aidene. "Unscented Kalman Observer*." In 2019 8th International Conference on Systems and Control (ICSC). IEEE, 2019. http://dx.doi.org/10.1109/icsc47195.2019.8950505.

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Nikoofard, Amirhossein, Tor Arne Johansen, and Glenn-Ole Kaasa. "Nonlinear Moving Horizon Observer for Estimation of States and Parameters in Under-Balanced Drilling Operations." In ASME 2014 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/dscc2014-6074.

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It is not possible to directly measure the total mass of gas and liquid in the annulus and geological properties of the reservoir during petroleum exploration and production drilling. Therefore, these parameters and states must be estimated by online estimators with proper measurements. This paper describes a nonlinear Moving Horizon Observer to estimate the annular mass of gas and liquid, and production constants of gas and liquid from the reservoir into the well during Under-Balanced Drilling with measuring the choke pressure and the bottom-hole pressure. This observer algorithm based on a low-order lumped model is evaluated against Joint Unscented Kalman filter for two different simulations with low and high measurement noise covariance. The results show that both algorithms are capable of identifying the production constants of gas and liquid from the reservoir into the well, while the nonlinear Moving Horizon Observer achieves better performance than the Unscented Kalman filter.
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Ahmad, Aftab, Kjell Andersson, and Ulf Sellgren. "A Comparative Study of Friction Estimation and Compensation Using Extended, Iterated, Hybrid, and Unscented Kalman Filters." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-12997.

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Transparency is a key performance evaluation criterion for haptic devices, which describes how realistically the haptic force/torque feedback is mimicked from a virtual environment or in case of master-slave haptic device. Transparency in haptic devices is affected by disturbance forces like friction between moving parts. An accurate estimate of friction forces for observer based compensation requires estimation techniques, which are computationally efficient and gives reduced error between measured and estimated friction. In this work different estimation techniques based on Kalman filter, such as Extended Kalman filter (EKF), Iterated Extended Kalman filter (IEKF), Hybrid extended Kalman filter (HEKF) and Unscented Kalman filter (UKF) are investigated with the purpose to find which estimation technique that gives the most efficient and realistic compensation using online estimation. The friction observer is based on a newly developed friction smooth generalized Maxwell slip model (S-GMS). Each studied estimation technique is demonstrated by numerical and experimental simulation of sinusoidal position tracking experiments. The performances of the system are quantified with the normalized root mean-square error (NRMSE) and the computation time. The results from comparative analyses suggest that friction estimation and compensation based on Iterated Extended Kalman filter both gives a reduced tracking error and computational advantages compared to EKF, HEKF, UKF, as well as with no friction compensation.
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Chen, Zixuan, Yupeng Duan, and Yunqing Zhang. "Automated Vehicle Path Planning and Trajectory Tracking Control Based on Unscented Kalman Filter Vehicle State Observer." In SAE WCX Digital Summit. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2021. http://dx.doi.org/10.4271/2021-01-0337.

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Cheng, Qi, Alessandro Correa-Victorino, and Ali Charara. "A new nonlinear observer of sideslip angle with unknown vehicle parameter using the dual unscented Kalman filter." In 2012 15th International IEEE Conference on Intelligent Transportation Systems - (ITSC 2012). IEEE, 2012. http://dx.doi.org/10.1109/itsc.2012.6338813.

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Cheng, Qi, Alessandro Correa-Victorino, and Ali Charara. "A new nonlinear observer using unscented Kalman filter to estimate sideslip angle, lateral tire road forces and tire road friction coefficient." In 2011 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2011. http://dx.doi.org/10.1109/ivs.2011.5940501.

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Yu, Chao, Chuan Wang, Xin Deng, XueLiang Zhang, HaiFang Sun, WeiMing Peng, and YuPeng Liu. "State Estimation and Slug Control of the Subsea Multiphase Pipeline." In ASME 2021 40th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/omae2021-62392.

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Abstract The simulation and control of the severe slugging flow in the subsea multiphase pipeline is the focus of research in the production and exploitation of oil companies. Severe slug flow results in severe fluctuations of pressure and flow rate at both the wells end and the receiving host processing facilities, causing safety and shutdown risks. To prevent the severe slugging flow regime in multiphase transport pipelines, an Ordinary Differential Equation (ODE) model is established by using the mass conservation law for individual phases in the pipeline and the riser sections. Then, the proposed model is compared to the results from the OLGA simulation. A comparative study of different slugging flow control solutions is conducted. Unscented Kalman Filter (UKF), Wavelet Neural Network (WNN) and UKF&WNN are used for state estimation and combined with PI controller. The UKF and WNN are good nonlinear filters. However, when the nominal choke opening is increased, they work unsatisfying. The UKF&WNN observer shows slightly better results than UKF and WNN when the system has high input disturbance.
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Dey, Satadru, Beshah Ayalew, and Pierluigi Pisu. "Estimation of Lithium-Ion Concentrations in Both Electrodes of a Lithium-Ion Battery Cell." In ASME 2015 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/dscc2015-9693.

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For control and estimation tasks in battery management systems, the benchmark Li-ion cell electrochemical pseudo-two-dimensional (P2D) model is often reduced to the Single Particle Model (SPM). The original SPM consists of two electrodes approximated as spherical particles with spatially distributed Li-ion concentration. However, the Li-ion concentration states in these two-electrode models are known to be weakly observable from the voltage output. This has led to the prevalent use of reduced models in literature that generally approximate Li-ion concentration states in one electrode as an algebraic function of that in the other electrode. In this paper, we remove such approximations and show that the addition of the thermal model to the electrochemical SPM essentially leads to observability of the Li-ion concentration states in both electrodes from voltage and temperature measurements. Then, we propose an estimation scheme based on this SPM coupled with lumped thermal dynamics that estimates the Li-ion concentrations in both electrodes. Moreover, these Li-ion concentration estimates also enable the estimation of the cell capacity. The estimation scheme consists of a sliding mode observer cascaded with an Unscented Kalman filter (UKF). Simulation studies are included to show the effectiveness of the proposed scheme.
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Heidfeld, Hannes, and Martin Schünemann. "Optimization based design of an UKF vehicle state estimator." In FISITA World Congress 2021. FISITA, 2021. http://dx.doi.org/10.46720/f2020-vdc-090.

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For years the trend has been towards automated and assisted driving, for which vehicle dynamic control is needed. Effective vehicle dynamic control requires exact knowledge of the vehicle motion state and the road conditions, which usually comes from state estimators. As vehicle dynamics models often are nonlinear, the design of those estimators can be a difficult task. The Unscented Kalman Filter is a powerful tool for nonlinear state estimation. Besides the selection of an appropriate model, the choice of covariance matrices is main task in the design phase. For this, optimization-based tuning has become a state of the art method. Often minimization of the estimation error is done, for example with global optimization methods [1]. However, this approach does not fully take into account the stochastic properties of the estimated variables, which can result in implausible covariance estimations. Addressing this problem, different approaches for optimization-based tuning arepresented in this paper using the example of an UKF state observer for a four-wheel drive electric vehicle [2], [3]. First, details on the used vehicle model as well as the estimator implementation are given. Then optimization and validation are carried out using data from driving tests in a vehicle simulation including various severe maneuvers and varying road conditions. Last, the results are compared and discussed.
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Samokhin, Sergey, and Kai Zenger. "Unscented MPC Design for Turbocharged EGR System in Diesel Engines." In ASME 2016 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/dscc2016-9880.

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Exhaust gas recirculation (EGR) has become an integral part of the NOx emission reduction mechanisms utilized in modern combustion engines. However, capabilities to recirculate the processed gas are oftentimes compromised by the inability to surmount the pressure differential between the intake and exhaust manifolds. The issue is dealt with successfully by the turbocharged EGR system discussed in this article. The increased complexity of such an EGR system requires a multivariable control system in order to achieve the EGR set-point tracking across the desired operating range. In this work, model predictive control is used to naturally incorporate the information about system internal couplings and constraints via the prediction model. The states of the partially observed EGR system required for the feedback control are recovered by using the unscented Kalman filter. Finally, the designed unscented MPC (UMPC) system is validated by numerical simulations.
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