scholarly journals An Adaptive Multi-Dimensional Vehicle Driving State Observer Based on Modified Sage–Husa UKF Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6889
Author(s):  
Zeyuan Luo ◽  
Zanhao Fu ◽  
Qiwei Xu
Keyword(s):  
State Estimation ◽  
Degrees Of Freedom ◽  
Unscented Kalman Filter ◽  
Sine Wave ◽  
State Observer ◽  
Measurement Noise ◽  
Necessary Condition ◽  
Sensor Failure ◽  
State Observation ◽  
Transient Disturbances

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 observation. Based on the seven-degrees-of-freedom vehicle model and the Dugoff tire model, the proposed algorithm corrects the measurement noise by using modified Sage–Husa maximum posteriori. To reduce the influence of transient disturbance on the subsequent process, covariance matrix is updated after divergence is detected. The effectiveness of the algorithm is tested on the double lane change and Sine Wave road conditions. The robustness of the algorithm is tested under severe transient disturbance. The results demonstrate that the modified Sage–Husa UKF algorithm can accurately detect transient disturbance and effectively reduce the resulted accumulated error. Compared to classical AUKF, our algorithm significantly improves the accuracy and robustness of vehicle driving state estimation. The research in this paper provides a reference for multi-dimensional data processing under changeable vehicle driving states.

scholarly journals Huber-Based Robust Unscented Kalman Filter Distributed Drive Electric Vehicle State Observation

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 750
Author(s):  
Wenkang Wan ◽  
Jingan Feng ◽  
Bao Song ◽  
Xinxin Li
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Degrees Of Freedom ◽  
Unscented Kalman Filter ◽  
State Parameter ◽  
State Observation ◽  
Straight Line ◽  
Distributed Drive Electric Vehicle ◽  
Huber Method ◽  
Yaw Moment

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.

An improved unscented Kalman filter based dynamic state estimation algorithm for electric distribution Systems

2017 ◽  
Vol 36 (4) ◽  
pp. 1220-1236 ◽  
Author(s):  
Fiaz Ahmad ◽  
Kabir Muhammad Abdul Rashid ◽  
Akhtar Rasool ◽  
Esref Emre Ozsoy ◽  
Asif Sabanoviç ◽  
...  
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Radial Distribution ◽  
Distribution Systems ◽  
Unscented Kalman Filter ◽  
Distribution Networks ◽  
Measurement Noise ◽  
Content Type ◽  
Load Variations ◽  
Bad Data

Purpose To propose an improved algorithm for the state estimation of distribution networks based on the unscented Kalman filter (IUKF). The performance comparison of unscented Kalman filter (UKF) and newly developed algorithm, termed Improved unscented Kalman Filter (IUKF) for IEEE-30, 33 and 69-bus radial distribution networks for load variations and bad data for two measurement noise scenarios, i.e. 30 and 50 per cent are shown. Design/methodology/approach State estimation (SE) plays an instrumental role in realizing smart grid features like distribution automation (DA), enhanced distribution generation (DG) penetration and demand response (DR). Implementation of DA requires robust, accurate and computationally efficient dynamic SE techniques that can capture the fast changing dynamics of distribution systems more effectively. In this paper, the UKF is improved by changing the way the state covariance matrix is calculated, to enhance its robustness and accuracy under noisy measurement conditions. UKF and proposed IUKF are compared under the cummulative effect of load variations and bad data based on various statistical metrics such as Maximum Absolute Deviation (MAD), Maximum Absolute Per cent Error (MAPE), Root Mean Square Error (RMSE) and Overall Performance Index (J) for three radial distribution networks. All the simulations are performed in MATLAB 2014b environment running on an hp core i5 laptop with 4GB memory and 2.6 GHz processor. Findings An Improved Unscented Kalman Filter Algorithm (IUKF) is developed for distribution network state estimation. The developed IUKF is used to predict network states (voltage magnitude and angle at all buses) and measurements (source voltage magnitude, line power flows and bus injections) in the presence of load variations and bad data. The statistical performance of the coventional UKF and the proposed IUKF is carried out for a variety of simulation scenarios for IEEE-30, 33 and 69 bus radial distribution systems. The IUKF demonstrated superiority in terms of: RMSE; MAD; MAPE; and overall performance index J for two measurement noise scenarios (30 and 50 per cent). Moreover, it is shown that for a measurement noise of 50 per cent and above, UKF fails while IUKF performs. Originality/value UKF shows degraded performance under high measurement noise and fails in some cases. The proposed IUKF is shown to outperform the UKF in all the simulated scenarios. Moreover, this work is novel and has justified improvement in the robustness of the conventional UKF algorithm.

scholarly journals On co-estimation and validation of vehicle driving states by a UKF-based approach

2021 ◽  
Vol 12 (1) ◽  
pp. 19-30
Author(s):  
Peng Wang ◽  
Hui Pang ◽  
Zijun Xu ◽  
Jiamin Jin
Keyword(s):  
State Estimation ◽  
Degrees Of Freedom ◽  
Unscented Kalman Filter ◽  
Control Plant ◽  
Estimation Method ◽  
Lateral Acceleration ◽  
Accurate Information ◽  
Sideslip Angle ◽  
Safety Control ◽  
Vehicle State Estimation

Abstract. It is necessary to acquire the accurate information of vehicle driving states for the implementation of automobile active safety control. To this end, this paper proposes an effective co-estimation method based on an unscented Kalman filter (UKF) algorithm to accurately predict the sideslip angle, yaw rate, and longitudinal speed of a ground vehicle. First, a 3 degrees-of-freedom (DOFs) nonlinear vehicle dynamics model is established as the nominal control plant. Then, based on CarSim software, the simulation results of the front steer angle and longitudinal and lateral acceleration are obtained under a variety of working conditions, which are regarded as the pseudo-measured values. Finally, the joint simulation of vehicle state estimation is realized in the MATLAB/Simulink environment by using the pseudo-measured values and UKF algorithm concurrently. The results show that the proposed UKF-based vehicle driving state estimation method is effective and more accurate in different working scenarios compared with the EKF-based estimation method.

Topology detection of a distribution network based on adaptive state observer

2021 ◽  
pp. 1-9
Author(s):  
Yong Xiao ◽  
Yonggang Zeng ◽  
Yun Zhao ◽  
Yuxin Lu ◽  
Weibin Lin
Keyword(s):  
Smart Grid ◽  
Distribution Network ◽  
Detection Algorithm ◽  
State Observer ◽  
Matlab Simulation ◽  
Unknown Parameters ◽  
State Observation ◽  
Observation Network ◽  
Topology Information ◽  
Adaptive State Observer

The traditional distribution network lacks real-time topology information, which makes the implementation of smart grid complicated. The smart grid needs to monitor and dispatch the grid to maintain the economic and safe operation of the system. In this paper, we propose a topology detection algorithm of the distribution network based on adaptive state observer. Based on the transient dynamic model of the distribution network, the line states of the distribution network are regarded as unknown parameters, a virtual adaptive state observation network is built, and the topology can be inferred by the changes of adaptive state parameters. Finally, the effectiveness of our algorithm is verified by the MATLAB simulation experiments.

scholarly journals Maximum Correntropy Unscented Kalman Filter for Spacecraft Relative State Estimation

Sensors ◽  
2016 ◽  
Vol 16 (9) ◽  
pp. 1530 ◽  
Author(s):  
Xi Liu ◽  
Hua Qu ◽  
Jihong Zhao ◽  
Pengcheng Yue ◽  
Meng Wang
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Unscented Kalman Filter ◽  
Relative State

scholarly journals State Feedback and Torque Feed Forward Combined Control System for Suppressing Drill-Strings Stick-Slip Vibration

2019 ◽  
Vol 37 (2) ◽  
pp. 291-298
Author(s):  
Meng Fu ◽  
Jianghong Li ◽  
Yafeng Wu ◽  
Shubiao Song ◽  
Aiqi Zhao ◽  
...  
Keyword(s):  
Control System ◽  
Degrees Of Freedom ◽  
State Feedback ◽  
Dynamic Performance ◽  
Experimental Tests ◽  
State Observer ◽  
Lumped Parameter Model ◽  
Stick Slip ◽  
Feed Forward ◽  
Reference Governor

In drilling field, drill-strings stick-slip vibration is a common phenomenon and may lead to a series of drilling accidents. In order to improve drilling efficiency, this paper commits to study a new control system to suppress the undesired stick-slip vibration. In this work, a two degrees of freedom lumped parameter model is established to imitate the drill-strings. A state observer is proposed to estimate the unknown drill-strings states. A reference governor is put forward to optimize drilling parameters. In addition, in order to enhance the anti-interference ability of the closed-loop system, a torque feed forward is introduced into the control system. Based on the state observer and the reference governor, a state feedback and torque feed forward combined controller is designed. The simulation results indicate preliminarily that the designed state feedback and torque feed forward controller, compared with the drilling industry PI controller, has better dynamic performance and stronger ability to eliminate the drill-strings stick-slip vibration. Finally, the control system is applied in the drilling field. The experimental tests demonstrate that the designed controller can effectively suppress the drill-strings stick-slip vibration.

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