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Literatura académica sobre el tema "Unscented Kalman observer"

Autor: Grafiati

Publicado: 25 de mayo de 2024

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Artículos de revistas sobre el tema "Unscented Kalman observer"

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 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 (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 (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 (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 (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 (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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