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Journal articles on the topic 'Unscented Kalman observer'

Author: Grafiati

Published: 25 May 2024

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1

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 (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 influence

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3

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 (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 e

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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 Ai

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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 (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

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8

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 (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-U

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9

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 (2017): 1603–14. http://dx.doi.org/10.1002/er.3954.

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10

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 (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

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11

Zheng, Hong Yu, and Chang Fu Zong. "Information Fusion for State Estimation of Power Battery in Electric Vehicle Based on Unscented Kalman Filter." Applied Mechanics and Materials 303-306 (February 2013): 975–78. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.975.

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The power battery state of charge (SOC) in electric vehicles is not easy to measure accurately or apply a sensor but the expense is increased. However the variable of SOC is great importance to control of electric vehicles. A power battery model is built by the Partnership for a New Generation of Vehicles (PNGV) model to estimate the state of SOC. In order to make a high accurate estimate for SOC value, an information fusion algorithm based on unscented kalman filter (UKF) is introduced to design an observer. The test results show that the observer based information fusion and UKF are effectiv

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Wang, Zheng Jun, Jun Zheng Wang, Hao Wang, and Jiang Bo Zhao. "Model Parameter Adaptive Sliding Mode Model-Following Position Control of PMSM." Advanced Materials Research 466-467 (February 2012): 1089–94. http://dx.doi.org/10.4028/www.scientific.net/amr.466-467.1089.

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This paper aims at improving the robustness of the PMSM position control system with the parameter variation and load disturbance. A novel control strategy utilizing sliding mode model-following control (SMMFC) with the adaptive parameters observed by dual unscented Kalman filter (DUKF) observer is proposed. The switching gain of sliding mode is designed including the observed states of the system to suppress the chattering. The experimental results show that the robustness has been improved by sliding mode control, and the chattering has been well suppressed by switching gain adaptation depen

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13

Song, Chuanxue, Feng Xiao, Shixin Song, Shaokun Li, and Jianhua Li. "Design of a Novel Nonlinear Observer to Estimate Sideslip Angle and Tire Forces for Distributed Electric Vehicle." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/134615.

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For four-wheel independently driven (4WD) distributed electric vehicle (DEV), vehicle dynamics control systems such as direct yaw moment control (DYC) can be easily achieved. Accurate estimation of vehicle state variables and uncertain parameters can improve the robustness of vehicle dynamics control system. Various sensors are generally equipped to the acquisition of the vehicle dynamics. For both technical and economic reasons, some fundamental vehicle parameters, such as the sideslip angle and tire-road forces, can hardly be obtained through sensors directly. Therefore, this paper presented

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14

Novi, Tommaso, Renzo Capitani, and Claudio Annicchiarico. "An integrated artificial neural network–unscented Kalman filter vehicle sideslip angle estimation based on inertial measurement unit measurements." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 233, no. 7 (2018): 1864–78. http://dx.doi.org/10.1177/0954407018790646.

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Vehicle dynamics stability control systems rely on the amount of so-called sideslip angle and yaw rate. As the sideslip angle can be measured directly only with very expensive sensors, its estimation has been widely studied in the literature. Because of the large non-linearities and uncertainties in the dynamics, model-based methods are not a good solution to estimate the sideslip angle. On the contrary, machine learning techniques require large datasets that cover the entire working range for a correct estimation. In this paper, we propose an integrated artificial neural network and unscented

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15

Urbanski, Konrad, and Dariusz Janiszewski. "Sensorless Control of the Permanent Magnet Synchronous Motor." Sensors 19, no. 16 (2019): 3546. http://dx.doi.org/10.3390/s19163546.

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This paper describes the study and experimental verification of sensorless control of permanent magnet synchronous motors with a high precision drive using two novel estimation methods. All the studies of the modified Luenberger observer, reference model, and unscented Kalman filter are presented with algorithm details. The main part determines trials with a full range of reference speeds with a special near-zero speed area taken into account. In order to compare the estimation performances of the observers, both are designed for the same motor and control system and run in the same environmen

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16

Montani, M., S. Amirante, C. Annicchiarico, and R. Capitani. "Vehicle localization combining non-linear state observer with artificial neural network." IOP Conference Series: Materials Science and Engineering 1214, no. 1 (2022): 012040. http://dx.doi.org/10.1088/1757-899x/1214/1/012040.

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Abstract The developing of autonomous drive is needed to make people life more comfortable and safer, and one of the important skills to make possible the reliability of the all control system is a good localization of the vehicle. In this study, a no-linear state observer was developed using the Unscented Kalman Filter (UKF) algorithm, to estimate the global position, global orientation, and local speeds of a car inside a known path. A characterization of the sensors input measures was made and the measures of longitudinal and lateral vehicle speed were added using an Artificial Neural Networ

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17

Zhu, Huichao, Jun Tu, Chen Cai, Zhiyang Deng, Qiao Wu, and Xiaochun Song. "A Fast Signal-Processing Method for Electromagnetic Ultrasonic Thickness Measurement of Pipelines Based on UKF and SMO." Energies 15, no. 18 (2022): 6554. http://dx.doi.org/10.3390/en15186554.

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Electromagnetic ultrasonic testing technology has advantages in measuring the thickness of pipelines in service. However, the ultrasonic signal is susceptible to corrosions on the internal and external surfaces of the pipeline. Since the electromagnetic ultrasonic signal is nonlinear, and a dynamic model is difficult to establish accurately, in this paper, a new unscented Kalman filter (UKF) method based on a sliding mode observer (SMO) is proposed. The experiments, conducted on five different testing samples, validate that the proposed method can effectively process the signals drowned in noi

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18

Riva, Mauro H., Matthias Dagen, and Tobias Ortmaier. "Adaptive High-Gain observer for joint state and parameter estimation: A comparison to Extended and Unscented Kalman filter." IFAC Proceedings Volumes 47, no. 3 (2014): 8558–63. http://dx.doi.org/10.3182/20140824-6-za-1003.01609.

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19

Wang, Yaxiong, Feng Kang, Taipeng Wang, and Hongbin Ren. "A Robust Control Method for Lateral Stability Control of In-Wheel Motored Electric Vehicle Based on Sideslip Angle Observer." Shock and Vibration 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/8197941.

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In-wheel motored powertrain on electric vehicles has more potential in maneuverability and active safety control. This paper investigates the longitudinal and lateral integrated control through the active front steering and yaw moment control systems considering the saturation characteristics of tire forces. To obtain the vehicle sideslip angle of mass center, the virtual lateral tire force sensors are designed based on the unscented Kalman filtering (UKF). And the sideslip angle is estimated by using the dynamics-based approaches. Moreover, based on the estimated vehicle state information, an

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20

Benômar, Yassine, Julien Croonen, Björn Verrelst, Joeri Van Mierlo, and Omar Hegazy. "Model-Based Control System Design of Brushless Doubly Fed Reluctance Machines Using an Unscented Kalman Filter." Energies 14, no. 24 (2021): 8222. http://dx.doi.org/10.3390/en14248222.

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The Brushless Doubly Fed Reluctance Machine (BDFRM) is an emerging alternative for variable speed drive systems, providing a significant downsizing of the power electronics converter. This paper proposes a new view on the machine equations, allowing the reuse of the standard control system design for conventional synchronous and asynchronous machines: a cascade control system with an inner current control- and outer speed control loop. The assumptions and simplifications made on the machine model allow for a simple, model-based approach to set the controller gains in a brushless doubly fed mac

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21

Hu, Jie, Shijie Zheng, Xingyu Liu, Menghua Wang, Jiamei Deng, and Fuwu Yan. "Optimizing the fault diagnosis and fault-tolerant control of selective catalytic reduction hydrothermal aging using the Unscented Kalman Filter observer." Fuel 288 (March 2021): 119827. http://dx.doi.org/10.1016/j.fuel.2020.119827.

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22

Luo, Zeyuan, Zanhao Fu, and Qiwei Xu. "An Adaptive Multi-Dimensional Vehicle Driving State Observer Based on Modified Sage–Husa UKF Algorithm." Sensors 20, no. 23 (2020): 6889. http://dx.doi.org/10.3390/s20236889.

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An accurate vehicle driving state observer is a necessary condition for a safe automotive electronic control system. Vehicle driving state observer is challenged by unknown measurement noise and transient disturbances caused by complex working conditions and sensor failure. For the classical adaptive unscented Kalman filter (AUKF) algorithm, transient disturbances will cause the failure of state estimation and affect the subsequent process. This paper proposes an AUKF based on a modified Sage–Husa filter and divergence calculation technique for multi-dimensional vehicle driving state observati

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23

Das, Bikramaditya, Bidyadhar Subudhi, and Bibhuti Bhusan Pati. "Employing nonlinear observer for formation control of AUVs under communication constraints." International Journal of Intelligent Unmanned Systems 3, no. 2/3 (2015): 122–55. http://dx.doi.org/10.1108/ijius-04-2015-0004.

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Purpose – The purpose of this paper is to propose development of a formation control algorithm by employing a nonlinear observer for compensating the delay in the sensor signal transmission to the controller arising due to packet dropout in acoustic medium. Design/methodology/approach – A robust control law is developed using the sliding mode approach integrated with a communication consensus algorithm for achieving cooperative motion of acoustic underwater vehicles in a group ensuring the transfer of information among the AUVs. In acoustic medium, inter-vehicle communication is challenging fo

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Lv, Mingbo, Xiaopeng Li, Yunhua Li, Wei Zhang, and Rui Guo. "UKF-Based State Estimation for Electrolytic Oxygen Generation System of Space Station." Applied Sciences 11, no. 5 (2021): 2021. http://dx.doi.org/10.3390/app11052021.

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Electrolytic oxygen generation system (EOGS) is the only system that can provide oxygen for astronauts in a physicochemical regenerative way in a long-term manned spacecraft. In order to ensure that the astronauts in the cabin can obtain a continuous and enough oxygen supply, it is necessary to carry out real-time condition monitoring and fault diagnosis of the EOGS. This paper deals with condition monitoring and fault diagnosis of the EOGS. Firstly, the dynamic model of the system is established based on the principle electrolysis for actual oxygen production system and the state observer of

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Zhang, Xing, Shihua Yuan, Xufeng Yin, Xueyuan Li, Xinyi Qu, and Qi Liu. "Estimation of Skid-Steered Wheeled Vehicle States Using STUKF with Adaptive Noise Adjustment." Applied Sciences 11, no. 21 (2021): 10391. http://dx.doi.org/10.3390/app112110391.

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Skid-steered wheeled vehicles are commonly adopted in outdoor environments with the benefits of mobility and flexible structure. However, different from Ackerman turning vehicles, skid-steered vehicles do not possess geometric constraint but only dynamic constraint when steered, which leads to motion control and state estimation problems for skid-steered vehicles. The controlling accuracy of a skid-steered vehicle depends largely on feedback state information from sensors and an observer. In this study, a 3-DOF dynamic model using a Brush nonlinear tire model is built, first, to model a 6 × 6

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Han, Chunxiao, Yaru Yang, Tingting Yang, Yingmei Qin, and Yanqiu Che. "Parameter estimation of slow potassium dynamics in a neuron model for seizure-like activity via adaptive lag synchronization and unscented Kalman filter." International Journal of Modern Physics B 33, no. 15 (2019): 1950159. http://dx.doi.org/10.1142/s0217979219501595.

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We introduce a method that combines the unscented Kalman filter (UKF) and the adaptive lag synchronization (ALS) to estimate the unknown parameters of a neuron model with seizure-like activity using only the heavily noise-corrupted time series of membrane potentials. Although both UKF and ALS are able to estimate the parameters, UKF performs worse when the number of unknown parameters increases, while ALS requires system states that cannot be measured in practice. Therefore, we incorporate UKF as an observer of the unmeasured states into ALS method to estimate multiple parameters. The effectiv

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Al Kouzbary, Mouaz, Noor Azuan Abu Osman, and Ahmad Khairi Abdul Wahab. "Sensorless control system for assistive robotic ankle-foot." International Journal of Advanced Robotic Systems 15, no. 3 (2018): 172988141877585. http://dx.doi.org/10.1177/1729881418775854.

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This article presents a novel sensorless control system of assistive robotic ankle-foot prosthesis, two estimation algorithms were developed and an analogy between them has been made. The system actuator’s motor is a permanent magnet synchronous motor, unlike other powered ankle-foot, where the brushless DC motor and DC motor were used. Utilizing the permanent magnet synchronous motor will reduce the torque ripples and increase system ability to be overloaded compared to systems which utilize the brushless DC motor. Moreover, the ability of the machine to operate in all speed range makes this

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Ma, Jianfei, Kai Ding, Bing Yan, and Wen Dong. "Initial Tracking Parameter Estimation of Magnetic Ship Based on PSO." Mathematical Problems in Engineering 2020 (July 11, 2020): 1–7. http://dx.doi.org/10.1155/2020/7560474.

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We consider the problem of tracking a surface magnetic ship as it travels in a straight line path with the exertion of a magnetometer located at the seabed. Note that the initial filter parameters are prior information and the tracking performance depends on the initial filter parameters, and traditional estimation of initial filter parameters is to apply the filter bank algorithm, but there are several obvious defects in this method. In this paper, a novel algorithm based on the particle swarm optimization (PSO) algorithm is proposed to estimate initial parameters of the filter, and the model

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29

Wang, Yipeng, Lin Zhao, Jianhua Cheng, Junfeng Zhou, and Shuo Wang. "A State of Charge Estimation Method of Lithium-Ion Battery Based on Fused Open Circuit Voltage Curve." Applied Sciences 10, no. 4 (2020): 1264. http://dx.doi.org/10.3390/app10041264.

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The open circuit voltage (OCV) and model parameters are critical reference variables for a lithium-ion battery management system estimating the state of charge (SOC) accurately. However, the polarization effect reduces the accuracy of the OCV test, and the model parameters coupled to the polarization voltage increase the non-linearity of the cell model, all challenging SOC estimation. This paper presents an OCV curve fusion method based on the incremental and low-current test. Fusing the incremental test results without polarization effect and the low current test results with non-linear chara

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30

Sun, Jinwei, and Kai Zhao. "Adaptive neural network sliding mode control for active suspension systems with electrohydraulic actuator dynamics." International Journal of Advanced Robotic Systems 17, no. 4 (2020): 172988142094198. http://dx.doi.org/10.1177/1729881420941986.

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The object of this article is to design an observer-based adaptive neural network sliding mode controller for active suspension systems. A general nonlinear suspension model is established, and the electrohydraulic actuator dynamics are considered. The proposed controller is decomposed into two loops. Since the dynamics of the actuator is assumed highly nonlinear with uncertainties, the adaptive neural network is presented in the inner loop to ensure the control system robustness against uncertainties, and the self-tuning weighting vector is adjusted online according to the updated law obtaine

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Cui, Qingjia, Rongjun Ding, Bing Zhou, and Xiaojian Wu. "Path-tracking of an autonomous vehicle via model predictive control and nonlinear filtering." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 232, no. 9 (2017): 1237–52. http://dx.doi.org/10.1177/0954407017728199.

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To improve the stability of the autonomous vehicle for high speed tracking, a vehicle estimator scheme integrated into a path-tracking system has been proposed in this paper. Vehicle stability is related to road condition (low road adhesion, high road adhesion, and changing road adhesion) and vehicle state, thus a state observer has been preferred in this paper to estimate vehicle state and tire-road friction as a means of judging vehicle stabilization. For the approach to the estimation, an unscented Kalman filter (UKF) employing a three degrees-of-freedom vehicle model combined with a Magic

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32

Ibrahim, A., A. Azouz, and A. Abosekeen. "A land vehicle’s INS/GNSS integrated navigation system using left invariant extended kalman filter." Journal of Physics: Conference Series 2616, no. 1 (2023): 012023. http://dx.doi.org/10.1088/1742-6596/2616/1/012023.

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Abstract Land vehicles need high-precision navigational systems in which multi-sensor integration may be provided. Moreover, land vehicles regularly use Global Navigation Satellite Systems (GNSS) to estimate their position. Unfortunately, several locations, such as tunnels and inside parking garages, where GNSS signals cannot be detected. Several types of research have been conducted to improve positioning information using multi-sensor integration. Then, the vehicle needs another system for finding its location in GNSS-denied conditions, such as Inertial Navigation System (INS). Despite the a

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Hu, Juqi, Subhash Rakheja, and Youmin Zhang. "Tire–road friction coefficient estimation based on designed braking pressure pulse." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 235, no. 7 (2021): 1876–91. http://dx.doi.org/10.1177/0954407020983580.

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Knowledge of tire–road friction coefficient (TRFC) is valuable for autonomous vehicle control and design of active safety systems. This paper investigates TRFC estimation on the basis of longitudinal vehicle dynamics. A two-stage TRFC estimation scheme is proposed that limits the disturbances to the vehicle motion. A sequence of braking pressure pulses is designed in the first stage to identify desired minimal pulse pressure for reliable estimation of TRFC with minimal interference with the vehicle motion. This stage also provides a qualitative estimate of TRFC. In the second stage, tire norma

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34

Singh, Kanwar Bharat. "Virtual sensor for real-time estimation of the vehicle sideslip angle." Sensor Review 40, no. 2 (2019): 255–72. http://dx.doi.org/10.1108/sr-11-2018-0300.

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Purpose The vehicle sideslip angle is an important state of vehicle lateral dynamics and its knowledge is crucial for the successful implementation of advanced driver-assistance systems. Measuring the vehicle sideslip angle on a production vehicle is challenging because of the exorbitant price of a physical sensor. This paper aims to present a novel framework for virtually sensing/estimating the vehicle sideslip angle. The desired level of accuracy for the estimator is to be within +/− 0.2 degree of the actual sideslip angle of the vehicle. This will make the precision of the proposed estimato

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Chen, Xiao, Chun Zhang, Ju-Cai Chang, et al. "Longitudinal vibration estimation of a mine hoist using a hybrid signal fusion method combining UKF, ND and improved DE." Measurement Science and Technology 35, no. 4 (2024): 045108. http://dx.doi.org/10.1088/1361-6501/ad1b9d.

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Abstract The monitoring of cage longitudinal vibration can directly indicate the operational status of mine hoists. However, it is always challenging to collect the sensor signals of moving cages with high dynamic characteristics in real time from complex working environments using traditional monitoring methods. In this study, a more practical hybrid signal fusion approach is proposed to realize estimation of cage longitudinal vibration from a low sampling rate acceleration acquisition signal and a low cost encoder signal for state estimation. A nonlinear differentiator is applied to extract

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36

Wang, Yanbo, Fasheng Wang, Jianjun He, and Fuming Sun. "Iterative Truncated Unscented Particle Filter." Information 11, no. 4 (2020): 214. http://dx.doi.org/10.3390/info11040214.

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The particle filter method is a basic tool for inference on nonlinear partially observed Markov process models. Recently, it has been applied to solve constrained nonlinear filtering problems. Incorporating constraints could improve the state estimation performance compared to unconstrained state estimation. This paper introduces an iterative truncated unscented particle filter, which provides a state estimation method with inequality constraints. In this method, the proposal distribution is generated by an iterative unscented Kalman filter that is supplemented with a designed truncation metho

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37

Vepa, Ranjan. "Nonlinear Filtering of Oscillatory Measurements in Cardiovascular Applications." Mathematical Problems in Engineering 2010 (2010): 1–18. http://dx.doi.org/10.1155/2010/808019.

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An array of nonidentical and locally connected chaotic biological neurons is modelled by a single representative chaotic neuron model based on an extension of the Hindmarsh-Rose neuron. This model is then employed in conjunction with the unscented Kalman filter to study the associated state estimation problem. The archetypal system, which was deliberately chosen to be chaotic, was corrupted with noise. The influence of noise seemed to annihilate the chaotic behaviour. Consequently it was observed that the filter performs quite well in reconstructing the states of the system although the introd

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Khan, Abdul Basit, Abdul Shakoor Akram, and Woojin Choi. "State of Charge Estimation of Flooded Lead Acid Battery Using Adaptive Unscented Kalman Filter." Energies 17, no. 6 (2024): 1275. http://dx.doi.org/10.3390/en17061275.

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Flooded Lead Acid (FLA) batteries remain a cost-effective choice in various industries. Accurate State of Charge (SOC) estimation is crucial for effective battery management systems. This paper thoroughly examines the behavior of Open-Circuit Voltage (OCV) during hysteresis in FLA batteries, proposing a novel hysteresis modeling approach based on this behavior to enhance the SOC estimation accuracy. Additionally, we introduce an Adaptive Unscented Kalman Filter (AUKF) to further refine the SOC estimation precision. Experimental validation confirms the effectiveness of the proposed hysteresis m

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Hu, Juqi, Subhash Rakheja, and Youmin Zhang. "Real-time estimation of tire–road friction coefficient based on lateral vehicle dynamics." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 234, no. 10-11 (2020): 2444–57. http://dx.doi.org/10.1177/0954407020929233.

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This study proposes a two-stage framework for real-time estimation of tire–road friction coefficient of a vehicle on the basis of lateral dynamics of the vehicle. The estimation framework employs a new cascade structure consisting of an extended Kalman filter and two unscented Kalman filters to reduce the computational burden. In the first stage, extended Kalman filter is utilized to estimate lateral velocity of the vehicle and thereby both the front and rear tires’ side-slip angles. In the second stage, a two–unscented Kalman filters sub-framework is formulated in sequence to observe both the

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Zhang, Zipeng, Nan Xu, Hong Chen, Zhenfeng Wang, Fei Li, and Xinyu Wang. "State Observers for Suspension Systems with Interacting Multiple Model Unscented Kalman Filter Subject to Markovian Switching." International Journal of Automotive Technology 22, no. 6 (2021): 1459–73. http://dx.doi.org/10.1007/s12239-021-0126-z.

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Musunuri, Yogendra Rao, and Oh-Seol Kwon. "State Estimation Using a Randomized Unscented Kalman Filter for 3D Skeleton Posture." Electronics 10, no. 8 (2021): 971. http://dx.doi.org/10.3390/electronics10080971.

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In this study, we propose a method for minimizing the noise of Kinect sensors for 3D skeleton estimation. Notably, it is difficult to effectively remove nonlinear noise when estimating 3D skeleton posture; however, the proposed randomized unscented Kalman filter reduces the nonlinear temporal noise effectively through the state estimation process. The 3D skeleton data can then be estimated at each step by iteratively passing the posterior state during the propagation and updating process. Ultimately, the performance of the proposed method for 3D skeleton estimation is observed to be superior t

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Singh, Nivedita, M. A. Ansari, Manoj Tripathy, et al. "Islanding Event Detection in Grid-Connected Distributed Generation Systems Using Unscented Kalman Filter." International Journal of Energy Research 2023 (November 13, 2023): 1–17. http://dx.doi.org/10.1155/2023/8887455.

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A pressing concern in modern smart grid systems revolves around islanding, leading to unpredictable system parameters and a decline in power quality. In response to this concern, we introduce a novel passive method for identifying islanding in grid-connected distributed generation units. This method utilizes the unscented Kalman filter (UKF) to assess the voltage signal captured at the DG position. The triphase voltage signal observed at the point of common coupling (PCC) is used as the test signal. The UKF extracts and filters the harmonic content of the voltage signal to produce a residual s

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Badamchizadeh, Mohammad Ali, Iraj Hassanzadeh, and Mehdi Abedinpour Fallah. "Extended and Unscented Kalman Filtering Applied to a Flexible-Joint Robot with Jerk Estimation." Discrete Dynamics in Nature and Society 2010 (2010): 1–14. http://dx.doi.org/10.1155/2010/482972.

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Robust nonlinear control of flexible-joint robots requires that the link position, velocity, acceleration, and jerk be available. In this paper, we derive the dynamic model of a nonlinear flexible-joint robot based on the governing Euler-Lagrange equations and propose extended and unscented Kalman filters to estimate the link acceleration and jerk from position and velocity measurements. Both observers are designed for the same model and run with the same covariance matrices under the same initial conditions. A five-bar linkage robot with revolute flexible joints is considered as a case study.

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Askari, Mohsen, Jianchun Li, and Bijan Samali. "Application of Kalman Filtering Methods to Online Real-Time Structural Identification: A Comparison Study." International Journal of Structural Stability and Dynamics 16, no. 06 (2016): 1550016. http://dx.doi.org/10.1142/s0219455415500169.

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System identification refers to the process of building or improving mathematical models of dynamical systems from the observed experimental input–output data. In the area of civil engineering, the estimation of the integrity of a structure under dynamic loadings and during service condition has become a challenge for the engineering community. Therefore, there has been a great deal of attention paid to online and real-time structural identification, especially when input–output measurement data are contaminated by high-level noise. Among real-time identification methods, one of the most succe

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Sahl, Samer, Enbin Song, and Dunbiao Niu. "Robust Cubature Kalman Filter for Moving-Target Tracking with Missing Measurements." Sensors 24, no. 2 (2024): 392. http://dx.doi.org/10.3390/s24020392.

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Handling the challenge of missing measurements in nonlinear systems is a difficult problem in various scientific and engineering fields. Missing measurements, which can arise from technical faults during observation, diffusion channel shrinking, or the loss of specific metrics, can bring many challenges when estimating the state of nonlinear systems. To tackle this issue, this paper proposes a technique that utilizes a robust cubature Kalman filter (RCKF) by integrating Huber’s M-estimation theory with the standard conventional cubature Kalman filter (CKF). Although a CKF is often used for sol

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Chang, Guobin. "Loosely Coupled INS/GPS Integration with Constant Lever Arm using Marginal Unscented Kalman Filter." Journal of Navigation 67, no. 3 (2013): 419–36. http://dx.doi.org/10.1017/s0373463313000775.

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A loosely coupled Inertial Navigation System (INS) and Global Positioning System (GPS) are studied, particularly considering the constant lever arm effect. A five-element vector, comprising a craft's horizontal velocities in the navigation frame and its position in the earth-centred and earth-fixed frame, is observed by GPS, and in the presence of lever arm effect, the nonlinear observation equation from the state vector to the observation vector is established and addressed by the correction stage of an unscented Kalman filter (UKF). The conditionally linear substructure in the nonlinear obse

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Janiszewski, Dariusz. "Sensorless Model Predictive Control of Permanent Magnet Synchronous Motors Using an Unscented Kalman Filter." Energies 17, no. 10 (2024): 2387. http://dx.doi.org/10.3390/en17102387.

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This paper deals with the application of the Model Predictive Control (MPC) algorithm to the sensorless control of a Permanent Magnet Synchronous Motor (PMSM). The proposed estimation strategy, based on the unscented Kalman filter (UKF), uses only the measurement of the motor current for the online estimation of speed, rotor position and load torque. Information about the system state is fed into the MPC algorithm. The results verify the effectiveness and applicability of the proposed sensorless control technique. To demonstrate its real-world applicability, implementation in low-speed direct

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Ping, Xianyao, Shuo Cheng, Wei Yue, Yongchang Du, Xiangyu Wang, and Liang Li. "Adaptive estimations of tyre–road friction coefficient and body’s sideslip angle based on strong tracking and interactive multiple model theories." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 234, no. 14 (2020): 3224–38. http://dx.doi.org/10.1177/0954407020941410.

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Vehicle dynamic states and parameters, such as the tyre–road friction coefficient and body’s sideslip angle especially, are crucial for vehicle dynamics control with close-loop feedback laws. Autonomous vehicles also have strict demands on real-time knowledge of those information to make reliable decisions. With consideration of the cost saving, some estimation methods employing high-resolution vision and position devices are not for the production vehicles. Meanwhile, the bad adaptability of traditional Kalman filters to variable system structure restricts their practical applications. This p

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Zhang, WeiMing, and ZeLin Shi. "Mass Imbalance Compensation Control for Stabilized Platform Based on UKF Identification." MATEC Web of Conferences 198 (2018): 01005. http://dx.doi.org/10.1051/matecconf/201819801005.

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Due to the mass imbalance about the center of rotation, the stability of stabilized platform system degrades with carrier’s disturbances. Various feed-forward control methods are provided by reaserchers to solve this problem, however these methods are not well applied because the eccentricity of stabilized platform could not be measured directly. The dynamics model of a typical 2-axis stabilized platform is given. The eccentricity vector is identified through Unscented Kalman Filter(UKF) algorithm. Imbalance torque is precisely observed so that the real-time nonlinear compensation for mass imb

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Yang, Bo, Fan Si, Fan Xu, and Wenlan Zhou. "Adaptive Measurement Model of Navigation by Stellar Refraction based on Multiple Models Switching." Journal of Navigation 67, no. 4 (2014): 673–85. http://dx.doi.org/10.1017/s0373463314000125.

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In recent years, navigation by stellar refraction has received considerable attention, having advantages of high accuracy, simple construction, and low cost. Nevertheless, there are many limitations to the precision and application of this method using a traditional measurement model. This article studies the changing pattern of atmospheric density, the disturbed atmospheric density model and measurement model of stellar refraction ranging from 20 km to 50 km. Furthermore, a control algorithm of multiple mode switching and an adaptive measurement model are proposed. With this method, any refra

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