Journal articles: '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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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 effective and accuracy, so it is may apply it the electric vehicle control and observation.

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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 depending on the observed states of the system. Meanwhile, the control accuracy has been enhanced by model-following control with parameter adaptation. The position control performance of PMSM has been improved by the proposed scheme.

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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 a state observer to estimate these variables based on Unscented Kalman Filter (UKF). To improve the accuracy of UKF, measurement noise covariance is also self-adaptive regulated. In addition, a nonlinear dynamics tire model is utilized to improve the accuracy of tire lateral force estimation. The simulation and experiment results show that the proposed observer can provide the precision values of the vehicle state.

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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 (August 2, 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 Kalman filter observer using only inertial measurement unit measurements, which can work as a standalone sensor. The artificial neural network is trained solely with numerical data obtained with a Vi-Grade model and outputs a pseudo-sideslip angle which is used as input for the unscented Kalman filter. This is based on a kinematic model making the filter completely transparent to model uncertainty. A direct integration with integral damping and integral reset value allows the estimation of the longitudinal velocity of the kinematic model. A modification strategy of the pseudo-sideslip angle is then proposed to improve the convergence of the filter’s output. The algorithm is tested on both numerical data and experimental data. The results show the effectiveness of the solution.

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Urbanski, Konrad, and Dariusz Janiszewski. "Sensorless Control of the Permanent Magnet Synchronous Motor." Sensors 19, no. 16 (August 14, 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 environment. The experimental results indicate that the presented methods are capable of tracking the actual values of speed and motor position with small deviation, sufficient for precise control.

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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 (January 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 Network (ANN) trained in simulated manoeuvres. In this way, it was possible to reduce the error that the observer make on the estimation of the lateral vehicle speed, and so of the side slip angle, making possible an improvement of the control activity. To assess this increase in performance, a Montecarlo analysis was made comparing the architecture proposed, ANN+UKF, with state observed, UKF, with no input measure of lateral speed. The tests were done in co-simulation environment of Vi-Grade’s CarRealTime software and Matlab-Simulink.

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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 (September 8, 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 noise and accurately measure the wall thickness. Compared with FFT and UKF, the signal-to-noise ratio of the signals processed by SMO–UKF shows a maximum increase of 155% and 171%. Meanwhile, a random assignment method is proposed for the self-regulation of hyper parameters in the process of Kalman filtering. Experimental results show that the automatic adjustment of hyper parameters can be accomplished in finite cycle numbers and greatly shortens the overall filtering time.

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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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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 upper level control system by using robust control theory is proposed to specify a desired yaw moment and correction front steering angle to work on the electric vehicles. The robustness of proposed algorithm is also analyzed. The wheel torques are distributed optimally by the wheel torque distribution control algorithm. Numerical simulation is carried out in Matlab/Simulink-Carsim cosimulation environment to demonstrate the effectiveness of the designed robust control algorithm for lateral stability control of in-wheel motored vehicle.

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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 (December 7, 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 machine drive system. The cascade control scheme is combined with an Unscented Kalman Filter as a state observer, capable of estimating the load torque and losses. The performance of the proposed control system design is checked in simulation and tested in real-time on a low power BDFRM prototype.

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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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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 (December 2, 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 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.

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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 (May 11, 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 for a group of AUVs deployed in formation because underwater channel encounter a number of constraints such as low data rate, packet delays and dropouts. Findings – It is observed that the sliding mode control-unscented Kalman filter formation control exhibits superior control performance such as mitigating larger initial error of estimation and removing the use of the Jacobian matrices among the three controllers developed. The proposed nonlinear observer estimates the un-measureable states such as position in x, y and z-axes, heading, rudder and sturn angle, needed for generating the formation control. A simulation setup is realized to demonstrate the performance of the proposed observer-based formation controller. Simulations were performed in MATLAB and the obtained results are analysed and compared which envisage that the proposed control algorithm provides efficient formation control under the acoustic communication constraints. Originality/value – Development of observer for achieving formation control of AUVs in underwater area – common reference velocity and error signals being available to all cooperating AUVs – UKO performs better based on initial error estimation and tracking the same path in shallow water area.

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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 (February 25, 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 the system has been designed by using unscented Kalman filter (UKF). The total pressure in the cabin and the partial pressure of oxygen in the electrolytic cell can be observed. Then, considered the actual conditions of the manned space mission with one more astronaut, i.e., 3 astronauts, the simulation experiment is carried out. The simulation results show that the method can effectively estimate the system state, and it is of great significance to ensure the normal operation of the electrolytic EOGS system in the space station.

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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 (November 5, 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 skid-steered wheeled vehicle in flat ground driving conditions. Then, an observer using the unscented Kalman filter with a strong tracking algorithm and adaptive noise matrix adjustment (AN-STUKF) is established to estimate vehicle motion states based on the 3-DOF dynamic model. Finally, the experiment is carried out in three different driving conditions to verify the accuracy and stability of the proposed method. The results show that the AN-STUKF method possesses better accuracy and tracking rate than the traditional UKF, and the phenomenon of ICRs shifting forward of the skid-steered wheeled vehicle is also verified.

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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 (June 20, 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 effectiveness of the combined method is guaranteed by Lyapunov stability theorem and Barbalat’s lemma in theory. Numerical simulations demonstrate that, when two parameters are estimated simultaneously, the combined approach has better performance and higher accuracy than only using UKF or ALS method. This exploration of the proposed approach may play an important role in studying new treatments in seizure control.

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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 (May 1, 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 machine more suitable for the application. Both estimation algorithms are built using C-code and assessed in MATLAB Simulink. The estimation algorithms are used to provide motor and powered ankle-foot’s angular speed and position. Two-level control system is used to evaluate the estimation algorithms; the control system role is to mimic biological ankle-foot performance during normal ground level walking speed. Based on the result of this article the unscented Kalman filter (UKF) is applicable for the application, as a result of the observer ability to estimate the motor load and angular position. On the other hand, extended Kalman filter (EKF) accuracy is affected by the load applied to the motor. Furthermore, the angular position is evaluated by integration of the angular speed which means integration of angular speed estimation error.

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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 of uniformly magnetized ellipsoid is adopted to fit the magnetic field of the ship. The simulation results show that, under the condition of no prior information, the estimated ship parameters based on the observation of the single-observer are invalid, whereas the estimated ship parameters based on the observation of the double-observer are valid. Further, the estimated results of real-world recorded magnetic signals show that the ship parameters estimated by PSO based on the double-observer are also valid, as the estimated parameters are used as the initial parameters of the unscented Kalman filter (UKF), and a ship can be tracked effectively by the UKF filter. Moreover, the estimated half focal length can be used as a feature to distinguish noise environment, ships with different sizes, and mine sweepers.

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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 (February 13, 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 characteristics of electrodes, the fusion method improves the OCV curve’s accuracy. In addition, we design a state observer with model parameters and SOC, and the unscented Kalman filter (UKF) method is employed for co-estimation of SOC and model parameters to eliminate the drift noise effects. The SOC estimation root mean square error (RMSE) of the proposed method achieves 0.99% and 1.67% in the pulse constant current test and dynamic discharge test, respectively. Experimental results and comparisons with other methods highlight the SOC estimation accuracy and robustness of the proposed method.

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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 (July 1, 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 obtained by Lyapunov stability theory. In the outer loop, a model reference sliding mode controller is developed to track the desired states of the hybrid reference model that combines skyhook and groundhook control methods. Besides, to obtain the unmeasured states of the system, an unscented Kalman filter is utilized to provide necessary information for the controller. Simulation results show that the exerted force can be tracked precisely even in the existence of uncertainties. Moreover, the proposed controller can improve the suspension’s performance effectively.

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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 (October 4, 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 Formula (MF) tire model was designed. As a widely used model control method, the multi-constraints model predictive control (MMPC) was proposed and that was then used to calculate the desired front steering angle for tracking the planned path. The performance of the MMPC controller, with the estimator, was evaluated by the vehicle simulation software CARSIM and Matlab/Simulink. The simulation results show that the designed MMPC controller with the estimator successfully performs path-tracking at high speed for the intelligent vehicle.

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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 (November 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 accuracy of INS in short-time period use, inertial navigation systems (INS) are liable to drifts of their positioning solution due to the inertial sensor errors that are inherent to them; therefore, this problem leads to errors accumulation over time then integration techniques are used to eliminate the resulting errors. Moreover, many filters are used in the process of integration, such as the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Particular Filter (PF) and Invariant Extended Kalman Filter (IEKF). Moreover, this work introduces the left-invariant extended Kalman filter (LIEKF) as a navigation filter for a loosely coupled integration to eliminate positioning errors. Furthermore, the LIEKF is based on the symmetry-preserving observer theory, which claims that the estimation error depends on the theory of a Lie group matrix, and the proposed system INS/GPS-based LIEKF converges to constant values, unlike the traditional INS/GPS. Moreover, the proposed system INS/GPS-based LIEKF depends on State-estimate-independent Jacobians, and the LIEKF is more efficient and has better performance due to results such as the 2D position RMS error due to the INS/GPS-based EKF is 19.43m. However, the 2D position RMS error due to the INS/GPS-based LIEKF is 3.32m with 83% improvement. Moreover, the 2D position errors were enhanced using the INS/GPS-based LIEKF system compared to the INS/GPS-based EKF system.

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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 (January 5, 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 normal force and slip ratio are directly calculated from the measured signals, a modified force observer based on the wheel rotational dynamics is developed for estimating the tire braking force. A constrained unscented Kalman filter (CUKF) algorithm is subsequently proposed to identify the TRFC for achieving rapid convergence and enhanced estimation accuracy. The effectiveness of the proposed methodology is evaluated through CarSim™-MATLAB/Simulink™ co-simulations considering vehicle motions on high-, medium-, and low-friction roads at different speeds. The results suggest that the proposed two-stage methodology can yield an accurate estimation of the road friction with a relatively lower effect on the vehicle speed.

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Singh, Kanwar Bharat. "Virtual sensor for real-time estimation of the vehicle sideslip angle." Sensor Review 40, no. 2 (July 29, 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 estimator at par with expensive commercially available sensors used for physically measuring the vehicle sideslip angle. Design/methodology/approach The proposed estimator uses an adaptive tire model in conjunction with a model-based observer. The performance of the estimator is evaluated through experimental tests on a rear-wheel drive vehicle. Findings Detailed experimental results show that the developed system can reliably estimate the vehicle sideslip angle during both steady state and transient maneuvers, within the desired accuracy levels. Originality/value This paper presents a novel framework for vehicle sideslip angle estimation. The presented framework combines an adaptive tire model, an unscented Kalman filter-based axle force observer and data from tire mounted sensors. Tire model adaptation is achieved by making extensions to the magic formula, by accounting for variations in the tire inflation pressure, load, tread-depth and temperature. Predictions with the adapted tire model were validated by running experiments on the Flat-Trac® machine. The benefits of using an adaptive tire model for sideslip angle estimation are demonstrated through experimental tests. The performance of the observer is satisfactory, in both transient and steady state maneuvers. Future work will focus on measuring tire slip angle and road friction information using tire mounted sensors and using that information to further enhance the robustness of the vehicle sideslip angle observer.

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Chen, Xiao, Chun Zhang, Ju-Cai Chang, Guang-Ming Zhao, Wan-Shun Zang, Zhen-Cai Zhu, and Gang Shen. "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 (January 18, 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 encoder differential signals and expand observation variables. An unscented Kalman observer based on nonlinear mine hoist model is designed to estimate the unknown state. To overcome the influence of the uncertain parameters, an improved differential evolution (DE) algorithm combining parameter adaptive method, reverse learning competition scheme and multiple parallel populations strategy is proposed to find unknown parameters of the observation model and autotune the parameters of the algorithms by using low sampling rate acceleration. Sensor data of the simulated experiment platform were collected and processed by the xPC system to validate the effectiveness of the proposed strategy. The experimental results showed that the improved DE (IDE) algorithm had a faster mean time for parameter tuning and the smallest fitness value compared to the standard DE, the particle swarm optimization algorithm and the genetic algorithm. Moreover, the longitudinal vibration estimation system, after parameter tuning by the IDE optimization algorithm, could achieve the purpose of signal estimation, with a smaller estimation error and a better estimation effect.

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Wang, Yanbo, Fasheng Wang, Jianjun He, and Fuming Sun. "Iterative Truncated Unscented Particle Filter." Information 11, no. 4 (April 16, 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 method to satisfy the constraints. The detailed iterative unscented Kalman filter and truncation method is provided and incorporated into the particle filter framework. Experimental results show that the proposed algorithm is superior to other similar algorithms.

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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 introduction of relatively low noise had a profound effect on the system. Neither the noise-corrupted process model nor the filter gave any indications of chaos. We believe that this behaviour can be generalised and expect that unscented Kalman filtering of the states of a biological neuron is completely feasible even when the uncorrupted process model exhibits chaos. Finally the methodology of the unscented Kalman filter is applied to filter a typical simulated ECG signal using a synthetic model-based approach.

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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 (March 7, 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 modeling. A comparative analysis against the traditional Unscented Kalman Filter (UKF) under random charge/discharge profiles underscores the superior performance of AUKF, showcasing an improved convergence to the correct SOC value and a significant reduction in the SOC estimation error to approximately 2%, in contrast to the 5% error observed with the traditional UKF.

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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 (June 26, 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 front- and rear-axle tire forces, and to subsequently identify their respective tire–road friction coefficient, regarded as two unknown states. All the measured signals required in the study could be realized from the conventional on-board sensors. Typical double-lane change and single-lane change maneuvers were designed and the developed algorithm was verified through CarSim–MATLAB/Simulink software platform considering high-, mid-, and low-friction road conditions. The simulation results show that the proposed method can yield accurate and rapid estimations of the tire–road friction coefficient for mid- and low-friction road conditions even under a single-lane change maneuver, although double-lane change maneuver is needed to accurately estimate the tire–road friction coefficient for high-friction road condition.

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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 (November 15, 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 (April 19, 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 to that of conventional methods based on experimental results.

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Singh, Nivedita, M. A. Ansari, Manoj Tripathy, Pratiksha Gupta, Ikbal Ali, Yahya Alward, and Adel Rawea. "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 signal, which detects changes in the power system. The estimation of total harmonic distortion (THD) follows, and its fluctuations help discern between islanding and typical events. This suggested approach undergoes assessment through a test system simulated in MATLAB/Simulink across different situations. Outcome findings underscore the efficacy of the suggested approach in distinguishing between islanding and regular occurrences, ensuring enhanced reliability and resilience against incorrect operations by removing the zone of nondetection. In our detailed experiments, we found that the proposed unscented Kalman filter (UKF) technique improved islanding detection accuracy by approximately 90% over traditional methods, under varied conditions. Specifically, the nondetection zone (NDZ) was reduced by 95% when compared to the most commonly used passive methods. Furthermore, in scenarios with high harmonic content and noise, the UKF showcased a 90% improvement in reliability over conventional techniques.

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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. Simulation results verify the effectiveness of the proposed filters.

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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 (June 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 successful and widely used algorithms for estimation of system states and parameters is the Kalman filter and its various nonlinear extensions such as extended Kalman filter (EKF), Iterated EKF (IEKF), the recently developed unscented Kalman filter (UKF) and Iterated UKF (IUKF). In this paper, an investigation has been carried out on the aforementioned techniques for their effectiveness and efficiencies through a highly nonlinear single degree of freedom (SDOF) structure as well as a two-storey linear structure. Although IEKF is an improved version of EKF, results show that IUKF generally produces better results in terms of structural parameters and state estimation than UKF and IEKF. Also IUKF is more robust to noise levels compared to the other approaches.

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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 (January 9, 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 solving nonlinear filtering problems, its effectiveness might be limited due to a lack of knowledge regarding the nonlinear model of the state and noise-related statistical information. In contrast, the RCKF demonstrates an ability to mitigate performance degradation and discretization issues related to track curves by leveraging covariance matrix predictions for state estimation and output control amidst dynamic disruption errors—even when noise statistics deviate from prior assumptions. The performance of extended Kalman filters (EKFs), unscented Kalman filters (UKFs), CKFs, and RCKFs was compared and evaluated using two numerical examples involving the Univariate Non-stationary Growth Model (UNGM) and bearing-only tracking (BOT). The numerical experiments demonstrated that the RCKF outperformed the EKF, EnKF, and CKF in effectively handling anomaly errors. Specifically, in the UNGM example, the RCKF achieved a significantly lower ARMSE (4.83) and ANCI (3.27)—similar outcomes were observed in the BOT example.

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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 (December 16, 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 observation equation is exploited, and a computationally efficient refinement of the UKF called marginalized UKF (MUKF) is investigated to incorporate this substructure where fewer sigma points are needed, and the computational expense is cut down while the high accuracy and good applicability of the UKF are retained. A performance comparison between UKF and MUKF demonstrates that the MUKF can achieve, if not better, at least a comparable performance to the UKF, but at a lower computational expense.

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Janiszewski, Dariusz. "Sensorless Model Predictive Control of Permanent Magnet Synchronous Motors Using an Unscented Kalman Filter." Energies 17, no. 10 (May 16, 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 drive astronomy telescope mount systems is investigated. The outcomes of the implementation are thoroughly examined, leading to insightful conclusions drawn from the observed results. Through rigorous theoretical analysis and extensive simulation studies, this paper establishes a solid foundation for the proposed sensorless control technique. The results obtained from simulation studies and real-world applications underscore the efficacy and versatility of the proposed approach, offering valuable insights for the advancement of sensorless control strategies in motor applications. The main aim of this work is to demonstrate and validate the practical feasibility of combining two complex techniques, establishing that such an integration is not only possible but also effective in achieving the desired objectives.

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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 (July 30, 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 paper introduces a cost-efficient estimation scheme using on-board sensors. Improved Strong Tracking Unscented Kalman filter is constructed to estimate the friction coefficient with fast convergence rate on time-variant road surfaces. On the basis of previous step, an estimator based on interactive multiple model is built to tolerant biased noise covariance matrices and observe body’s sideslip angle. After the vehicle modelling errors are considered, a Self-Correction Data Fusion algorithm is developed to integrate results of the estimator and direct integral method with error correction theory. Some simulations and experiments are also implemented, and their results verify the high accuracy and good robustness of the cooperative estimation scheme.

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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 imbalance is achieved through a feed-forward loop. The simulation result indicates that the Root Mean Squared Error (RMSE) of parameters estimation is 0.024 after convergence. the LOS stabilization with carrier’s 2.5Hz vibration is 0.04 rad/s, which improves 78% compared to conventional feed-back control.

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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 (March 13, 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 refracted starlight from the scope of between 20 km and 50 km can be captured and the measurement model at the appropriate height can be automatically established. Due to this, the reliability and practicality of navigation have been raised considerably. Accuracy of navigation using the adaptive measurement method is observed to improve by about 14%, using computer simulation based on an Unscented Kalman Filter (UKF).

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Journal articles on the topic 'Unscented Kalman observer'

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