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Matsuura, Tsubasa, Masahiro Matsushita, Gan Chen, and Isao Takami. "Gain-scheduled Control Using Unscented Kalman Filter." Proceedings of Conference of Tokai Branch 2019.68 (2019): 316. http://dx.doi.org/10.1299/jsmetokai.2019.68.316.
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He, Xiaoyou, Yu Su, and Yuhe Qiu. "An Improved Unscented Kalman Filter for Maneuvering Target Tracking*." Journal of Physics: Conference Series 2216, no. 1 (March 1, 2022): 012010. http://dx.doi.org/10.1088/1742-6596/2216/1/012010.
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Abstract The photoelectric pod provides angular information and distance information for the UAV (Unmanned Aerial Vehicle), and the UAV uses it to estimate the status information of the moving target. Since the measurement information of the photoelectric pod is the angle of sight and relative distance, the measurement equation contains some nonlinear functions in the Cartesian coordinate system, and the output frequency of the photoelectric pod is low. The improved unscented Kalman filter combines the function of prediction and correction, introduces the prior information of the target acceleration constraint, and uses the offline data to obtain steady gain, and then estimates the target state online. The simulation result show that the algorithm can track the target and need less time compared with the traditional unscented Kalman filter.
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Yem Souhe, Felix Ghislain, Alexandre Teplaira Boum, Pierre Ele, Camille Franklin Mbey, and Vinny Junior Foba Kakeu. "A Novel Smart Method for State Estimation in a Smart Grid Using Smart Meter Data." Applied Computational Intelligence and Soft Computing 2022 (May 10, 2022): 1–14. http://dx.doi.org/10.1155/2022/7978263.
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Smart grids have brought new possibilities in power grid operations for control and monitoring. For this purpose, state estimation is considered as one of the effective techniques in the monitoring and analysis of smart grids. State estimation uses a processing algorithm based on data from smart meters. The major challenge for state estimation is to take into account this large volume of measurement data. In this article, a novel smart distribution network state estimation algorithm has been proposed. The proposed method is a combined high-gain state estimation algorithm named adaptive extended Kalman filter (AEKF) using extended Kalman filter (EKF) and unscented Kalman filter (UKF) in order to achieve better intelligent utility grid state estimation accuracy. The performance index and the error are indicators used to evaluate the accuracy of the estimation models in this article. An IEEE 37-node test network is used to implement the state estimation models. The state variables considered in this article are the voltage module at the measurement nodes. The results obtained show that the proposed hybrid algorithm has better performance compared to single state estimation methods such as the extended Kalman filter, the unscented Kalman filter, and the weighted least squares (WLS) method.
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Liang, Yunpei, Jiahui Dai, Kequan Wang, Xiaobo Li, and Pengcheng Xu. "A Strong Tracking SLAM Algorithm Based on the Suboptimal Fading Factor." Journal of Sensors 2018 (2018): 1–14. http://dx.doi.org/10.1155/2018/9684382.
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This paper proposes an innovative simultaneous localization and mapping (SLAM) algorithm which combines a strong tracking filter (STF), an unscented Kalman filter (UKF), and a particle filter (PF) to deal with the low accuracy of unscented FastSLAM (UFastSLAM). UFastSLAM lacks the capacity for online self-adaptive adjustment, and it is easily influenced by uncertain noise. The new algorithm updates each Sigma point in UFastSLAM by an adaptive algorithm and obtains optimized filter gain by the STF adjustment factor. It restrains the influence of uncertain noise and initial selection. Therefore, the state estimation would converge to the true value rapidly and the accuracy of system state estimation would be improved eventually. The results of simulations and practical tests show that strong tracking unscented FastSLAM (STUFastSLAM) has a significant improvement in accuracy and robustness.
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Legowo, Ari, Zahratu H. Mohamad, and Hoon Cheol Park. "Mixed Unscented Kalman Filter and Differential Evolution for Parameter Identification." Applied Mechanics and Materials 256-259 (December 2012): 2347–53. http://dx.doi.org/10.4028/www.scientific.net/amm.256-259.2347.
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This paper presents parameters estimation techniques for coupled industrial tanks using the mixed Unscented Kalman Filter (UKF) and Differential Evolution (DE) method. UKF have known to be a typical estimation technique used to estimate the state vectors and parameters of nonlinear dynamical systems and DE is one of the most powerful stochastic real-parameter optimization algorithms. Meanwhile, liquid tank systems play important role in industrial application such as in food processing, beverage, dairy, filtration, effluent treatment, pharmaceutical industry, water purification system, industrial chemical processing and spray coating. The aim is to model the coupled tank system using mixed UKF and DE method to estimate the parameters of the tank. First, a non-linear mathematical model is developed. Next, its parameters are identified using mixed Unscented Kalman Filter (UKF) and Differential Evolution (DE) based on the experimental data. DE algorithm is integrated into the UKF algorithm to optimize the Kalman gain obtained. The obtained results demonstrate good performances.
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Cao, Lu, Weiwei Yang, Hengnian Li, Zhidong Zhang, and Jianjun Shi. "Robust double gain unscented Kalman filter for small satellite attitude estimation." Advances in Space Research 60, no. 3 (August 2017): 499–512. http://dx.doi.org/10.1016/j.asr.2017.03.014.
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Tehrani, Mohammad, Nader Nariman-zadeh, and Mojtaba Masoumnezhad. "Adaptive fuzzy hybrid unscented/H-infinity filter for state estimation of nonlinear dynamics problems." Transactions of the Institute of Measurement and Control 41, no. 6 (August 8, 2018): 1676–85. http://dx.doi.org/10.1177/0142331218787607.
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In this paper, a new hybrid unscented Kalman (UKF) and unscented [Formula: see text](U[Formula: see text]F) filter is presented that can adaptively adjust its performance better than that of either UKF and/or U[Formula: see text], accordingly. In this way, two Takagi-Sugeno-Kang (TSK) fuzzy logic systems are presented to adjust automatically some weights that combine those UK and U[Formula: see text] filters, independent of the dynamics of the problem. Such adaptive fuzzy hybrid unscented Kalman/[Formula: see text] filter (AFUK[Formula: see text]) is based on the combination of gain, a priori state estimation, and a priori measurement estimation. The simulation results of an inverted pendulum and a re-entry vehicle tracking problem clearly demonstrate robust and better performance of this new AFUK[Formula: see text] filter in comparison with those of both UKF and U[Formula: see text]F, appropriately. It is shown that, therefore, the new presented AFUK[Formula: see text] filter can simply eliminate the need for either UKF or U[Formula: see text] F effectively in the presence of Gaussian and/or non-Gaussian noises.
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Kim, DongBeom, Daekyo Jeong, Jaehyuk Lim, Sawon Min, and Jun Moon. "Application of Recurrent Neural-Network based Kalman Filter for Uncertain Target Models." Journal of the Korea Institute of Military Science and Technology 26, no. 1 (February 5, 2023): 10–21. http://dx.doi.org/10.9766/kimst.2023.26.1.010.
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For various target tracking applications, it is well known that the Kalman filter is the optimal estimator(in the minimum mean-square sense) to predict and estimate the state(position and/or velocity) of linear dynamical systems driven by Gaussian stochastic noise. In the case of nonlinear systems, Extended Kalman filter(EKF) and/or Unscented Kalman filter(UKF) are widely used, which can be viewed as approximations of the(linear) Kalman filter in the sense of the conditional expectation. However, to implement EKF and UKF, the exact dynamical model information and the statistical information of noise are still required. In this paper, we propose the recurrent neural-network based Kalman filter, where its Kalman gain is obtained via the proposed GRU-LSTM based neural-network framework that does not need the precise model information as well as the noise covariance information. By the proposed neural-network based Kalman filter, the state estimation performance is enhanced in terms of the tracking error, which is verified through various linear and nonlinear tracking problems with incomplete model and statistical covariance information.
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Fan, Yongcun, Haotian Shi, Shunli Wang, Carlos Fernandez, Wen Cao, and Junhan Huang. "A Novel Adaptive Function—Dual Kalman Filtering Strategy for Online Battery Model Parameters and State of Charge Co-Estimation." Energies 14, no. 8 (April 17, 2021): 2268. http://dx.doi.org/10.3390/en14082268.
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This paper aims to improve the stability and robustness of the state-of-charge estimation algorithm for lithium-ion batteries. A new internal resistance-polarization circuit model is constructed on the basis of the Thevenin equivalent circuit to characterize the difference in internal resistance between charge and discharge. The extended Kalman filter is improved through adding an adaptive noise tracking algorithm and the Kalman gain in the unscented Kalman filter algorithm is improved by introducing a dynamic equation. In addition, for benignization of outliers of the two above-mentioned algorithms, a new dual Kalman algorithm is proposed in this paper by adding a transfer function and through weighted mutation. The model and algorithm accuracy is verified through working condition experiments. The result shows that: the errors of the three algorithms are all maintained within 0.8% during the initial period and middle stages of the discharge; the maximum error of the improved extension of Kalman algorithm is over 1.5%, that of improved unscented Kalman increases to 5%, and the error of the new dual Kalman algorithm is still within 0.4% during the latter period of the discharge. This indicates that the accuracy and robustness of the new dual Kalman algorithm is better than those of traditional algorithm.
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Wang, Junting, Tianhe Xu, and Zhenjie Wang. "Adaptive Robust Unscented Kalman Filter for AUV Acoustic Navigation." Sensors 20, no. 1 (December 20, 2019): 60. http://dx.doi.org/10.3390/s20010060.
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Autonomous underwater vehicle (AUV) acoustic navigation is challenged by unknown system noise and gross errors in the acoustic observations caused by the complex marine environment. Since the classical unscented Kalman filter (UKF) algorithm cannot control the dynamic model biases and resist the influence of gross errors, an adaptive robust UKF based on the Sage-Husa filter and the robust estimation technique is proposed for AUV acoustic navigation. The proposed algorithm compensates the system noise by adopting the Sage-Husa noise estimation technique in an online manner under the condition that the system noise matrices are kept as positive or semi positive. In order to control the influence of gross errors in the acoustic observations, the equivalent gain matrix is constructed to improve the robustness of the adaptive UKF for AUV acoustic navigation based on Huber’s equivalent weight function. The effectiveness of the algorithm is verified by the simulated long baseline positioning experiment of the AUV, as well as the real marine experimental data of the ultrashort baseline positioning of an underwater towed body. The results demonstrate that the adaptive UKF can estimate the system noise through the time-varying noise estimator and avoid the problem of negative definite of the system noise variance matrix. The proposed adaptive robust UKF based on the Sage-Husa filter can further reduce the influence of gross errors while adjusting the system noise, and significantly improve the accuracy and stability of AUV acoustic navigation.
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Goll, Stanislaw, and Elena Zakharova. "An active beacon-based leader vehicle tracking system." ACTA IMEKO 8, no. 4 (December 16, 2019): 33. http://dx.doi.org/10.21014/acta_imeko.v8i4.685.
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This article focuses on mobile robot convoying along a path travelled by a certain leader carrying the active ultrasonic beacon. The robot is equipped with the three-dimensional receiver array in order to receive both the ultrasonic wave and the RF wave marking the beginning of the measurement cycle. To increase measurement reliability, each receiver contains two independent measurement channels with automatic gain control. The distance measurements are pre-processed in order to identify the artefacts and then either remove them or replace them with the interpolated value. To estimate the position of the beacon in the robot’s local coordinate system, several methods are used, including the least squares method with subsequent exponential smoothing, the linear Kalman filter, the Rauch-Tung-Striebel smoother, the extended Kalman Filter, the unscented Kalman filter, and the particle filter. The experiments were undertaken in order to estimate the estimation method preferable for following the leader’s path.
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Luo, Yarong, Chi Guo, Shengyong You, and Jingnan Liu. "A Novel Perspective of the Kalman Filter from the Rényi Entropy." Entropy 22, no. 9 (September 3, 2020): 982. http://dx.doi.org/10.3390/e22090982.
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Rényi entropy as a generalization of the Shannon entropy allows for different averaging of probabilities of a control parameter α. This paper gives a new perspective of the Kalman filter from the Rényi entropy. Firstly, the Rényi entropy is employed to measure the uncertainty of the multivariate Gaussian probability density function. Then, we calculate the temporal derivative of the Rényi entropy of the Kalman filter’s mean square error matrix, which will be minimized to obtain the Kalman filter’s gain. Moreover, the continuous Kalman filter approaches a steady state when the temporal derivative of the Rényi entropy is equal to zero, which means that the Rényi entropy will keep stable. As the temporal derivative of the Rényi entropy is independent of parameter α and is the same as the temporal derivative of the Shannon entropy, the result is the same as for Shannon entropy. Finally, an example of an experiment of falling body tracking by radar using an unscented Kalman filter (UKF) in noisy conditions and a loosely coupled navigation experiment are performed to demonstrate the effectiveness of the conclusion.
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Li, Bo, Huawei Yi, and Xiaohui Li. "Innovative unscented transform–based particle cardinalized probability hypothesis density filter for multi-target tracking." Measurement and Control 52, no. 9-10 (October 21, 2019): 1567–78. http://dx.doi.org/10.1177/0020294019877494.
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Multi-target tracking is widely applied in video surveillance systems. As we know, although the standard particle cardinalized probability hypothesis density filter can estimate state of targets, it is difficult to define the proposal distribution function in prediction stage. Since the robust particles cannot be effectively drawn, the actual tracking accuracy should be enhanced. In this paper, an innovative unscented transform–based particle cardinalized probability hypothesis density filter is derived. Considering the different state spaces, we use the auxiliary particle method and then draw robust particles from the modified distributions in order to estimate the position of targets. Simultaneously, we present the recursion of the optimized Kalman gain to improve the general unscented transform for the velocity estimates. Using the track label, we further integrate them in the framework of the jump Markov model. The simulation results show that the proposed filter has advances in the multi-target tracking scenes. Moreover, the experiments indicate that the filter can track mobile targets with satisfactory results.
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Amin, Md, Md Rahman, Mohammad Hossain, Md Islam, Kazi Ahmed, and Bikash Miah. "Unscented Kalman Filter Based on Spectrum Sensing in a Cognitive Radio Network Using an Adaptive Fuzzy System." Big Data and Cognitive Computing 2, no. 4 (December 17, 2018): 39. http://dx.doi.org/10.3390/bdcc2040039.
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In this paper, we proposed the unscented Kalman filter (UKF) based on cooperative spectrum sensing (CSS) scheme in a cognitive radio network (CRN) using an adaptive fuzzy system—in this proposed scheme, firstly, the UKF to apply the nonlinear system which is used to minimize the mean square estimation error; secondly, an adaptive fuzzy logic rule based on an inference engine to estimate the local decisions to detect a licensed primary user (PU) that is applied at the fusion center (FC). After that, the FC makes a global decision by using a defuzzification procedure based on a proposed algorithm. Simulation results show that the proposed scheme achieved better detection gain than the conventional schemes like an equal gain combining (EGC) based soft fusion rule and a Kalman filter (KL) based soft fusion rule under any conditions. Moreover, the proposed scheme achieved the lowest global probability of error compared to both the conventional EGC and KF schemes.
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Liu, Changyun, Penglang Shui, Gang Wei, and Song Li. "Modified unscented Kalman filter using modified filter gain and variance scale factor for highly maneuvering target tracking." Journal of Systems Engineering and Electronics 25, no. 3 (June 2014): 380–85. http://dx.doi.org/10.1109/jsee.2014.00043.
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Lv, Jiechao, Baochen Jiang, Xiaoli Wang, Yirong Liu, and Yucheng Fu. "Estimation of the State of Charge of Lithium Batteries Based on Adaptive Unscented Kalman Filter Algorithm." Electronics 9, no. 9 (September 2, 2020): 1425. http://dx.doi.org/10.3390/electronics9091425.
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The state of charge (SOC) estimation of the battery is one of the important functions of the battery management system of the electric vehicle, and the accurate SOC estimation is of great significance to the safe operation of the electric vehicle and the service life of the battery. Among the existing SOC estimation methods, the unscented Kalman filter (UKF) algorithm is widely used for SOC estimation due to its lossless transformation and high estimation accuracy. However, the traditional UKF algorithm is greatly affected by system noise and observation noise during SOC estimation. Therefore, we took the lithium cobalt oxide battery as the analysis object, and designed an adaptive unscented Kalman filter (AUKF) algorithm based on innovation and residuals to estimate SOC. Firstly, the second-order RC equivalent circuit model was established according to the physical characteristics of the battery, and the least square method was used to identify the parameters of the model and verify the model accuracy. Then, the AUKF algorithm was used for SOC estimation; the AUKF algorithm monitors the changes of innovation and residual in the filter and updates system noise covariance and observation noise covariance in real time using innovation and residual, so as to adjust the gain of the filter and realize the optimal estimation. Finally came the error comparison analysis of the estimation results of the UKF algorithm and AUKF algorithm; the results prove that the accuracy of the AUKF algorithm is 2.6% better than that of UKF algorithm.
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Yang, Yi, Fei Li, Yi Gao, and Yanhui Mao. "Multi-Sensor Combined Measurement While Drilling Based on the Improved Adaptive Fading Square Root Unscented Kalman Filter." Sensors 20, no. 7 (March 29, 2020): 1897. http://dx.doi.org/10.3390/s20071897.
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In the process of the attitude measurement for a steering drilling system, the measurement of the attitude parameters may be uncertain and unpredictable due to the influence of server vibration on bits. In order to eliminate the interference caused by vibration on the measurement and quickly obtain the accurate attitude parameters of the steering drilling tool, a new method for multi-sensor dynamic attitude combined measurement is presented. Firstly, by using a triaxial accelerometer and triaxial magnetometer measurement system, the nonlinear model based on the quaternion is established. Then, an improved adaptive fading square root unscented Kalman filter is proposed for eliminating the vibration disturbance signal. In this algorithm, the square root of the state covariance matrix is used to replace the covariance matrix in the classical unscented Kalman filter (UKF) to avoid the filter divergence caused by the negative definite state covariance matrix. The fading factor is introduced into UKF to adjust the filter gain in real-time and improve the adaptive ability of the algorithm to mutation state. Finally, the computational method of the fading factor is optimized to ensure the self-adaptability of the algorithm and reduce the computational complexity. The results of the laboratory test and the field-drilling data show that the proposed method can filter out the interference noise in the attitude measurement sensor effectively, improve the solution accuracy of attitude parameters of drilling tools in the case of abrupt changes in the measuring environment, and thus ensuring the dynamic stability of the well trajectory.
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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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Liu, Huajun, Liwei Xia, and Cailing Wang. "Maneuvering Target Tracking Using Simultaneous Optimization and Feedback Learning Algorithm Based on Elman Neural Network." Sensors 19, no. 7 (April 2, 2019): 1596. http://dx.doi.org/10.3390/s19071596.
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Tracking maneuvering targets is a challenging problem for sensors because of the unpredictability of the target’s motion. Unlike classical statistical modeling of target maneuvers, a simultaneous optimization and feedback learning algorithm for maneuvering target tracking based on the Elman neural network (ENN) is proposed in this paper. In the feedback strategy, a scale factor is learnt to adaptively tune the dynamic model’s error covariance matrix, and in the optimization strategy, a corrected component of the state vector is learnt to refine the final state estimation. These two strategies are integrated in an ENN-based unscented Kalman filter (UKF) model called ELM-UKF. This filter can be trained online by the filter residual, innovation and gain matrix of the UKF to simultaneously achieve maneuver feedback and an optimized estimation. Monte Carlo experiments on synthesized radar data showed that our algorithm had better performance on filtering precision compared with most maneuvering target tracking algorithms.
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Battiston, Adrian, Inna Sharf, and Meyer Nahon. "Attitude estimation for collision recovery of a quadcopter unmanned aerial vehicle." International Journal of Robotics Research 38, no. 10-11 (August 8, 2019): 1286–306. http://dx.doi.org/10.1177/0278364919867397.
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An extensive evaluation of attitude estimation algorithms in simulation and experiments is performed to determine their suitability for a collision recovery pipeline of a quadcopter unmanned aerial vehicle. A multiplicative extended Kalman filter (MEKF), unscented Kalman filter (UKF), complementary filter, [Formula: see text] filter, and novel adaptive varieties of the selected filters are compared. The experimental quadcopter uses a PixHawk flight controller, and the algorithms are implemented using data from only the PixHawk inertial measurement unit (IMU). Performance of the aforementioned filters is first evaluated in a simulation environment using modified sensor models to capture the effects of collision on inertial measurements. Simulation results help define the efficacy and use cases of the conventional and novel algorithms in a quadcopter collision scenario. An analogous evaluation is then conducted by post-processing logged sensor data from collision flight tests, to gain new insights into algorithms’ performance in the transition from simulated to real data. The post-processing evaluation compares each algorithm’s attitude estimate, including the stock attitude estimator of the PixHawk controller, to data collected by an offboard infrared motion capture system. Based on this evaluation, two promising algorithms, the MEKF and an adaptive [Formula: see text] filter, are selected for implementation on the physical quadcopter in the control loop of the collision recovery pipeline. Experimental results show an improvement in the metric used to evaluate experimental performance, the time taken to recover from the collision, when compared with the stock attitude estimator on the PixHawk (PX4) software.
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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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Chien, Chiang-Heng, Wei-Yen Wang, Jun Jo, and Chen-Chien Hsu. "Enhanced Monte Carlo localization incorporating a mechanism for preventing premature convergence." Robotica 35, no. 7 (May 20, 2016): 1504–22. http://dx.doi.org/10.1017/s026357471600028x.
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SUMMARYIn this paper, we propose an enhanced Monte Carlo localization (EMCL) algorithm for mobile robots, which deals with the premature convergence problem in global localization as well as the estimation error existing in pose tracking. By incorporating a mechanism for preventing premature convergence (MPPC), which uses a “reference relative vector” to modify the weight of each sample, exploration of a highly symmetrical environment can be improved. As a consequence, the proposed method has the ability to converge particles toward the global optimum, resulting in successful global localization. Furthermore, by applying the unscented Kalman Filter (UKF) to the prediction state and the previous state of particles in Monte Carlo Localization (MCL), an EMCL can be established for pose tracking, where the prediction state is modified by the Kalman gain derived from the modified prior error covariance. Hence, a better approximation that reduces the discrepancy between the state of the robot and the estimation can be obtained. Simulations and practical experiments confirmed that the proposed approach can improve the localization performance in both global localization and pose tracking.
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Bagheri, Ahmad, Shahram Azadi, and Abbas Soltani. "A combined use of adaptive sliding mode control and unscented Kalman filter estimator to improve vehicle yaw stability." Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics 231, no. 2 (October 7, 2016): 388–401. http://dx.doi.org/10.1177/1464419316673960.
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In this paper, an adaptive sliding mode controller is proposed to improve the vehicle yaw stability and enhance the lateral motion by direct yaw moment control method using active braking systems. As the longitudinal and lateral velocities of the vehicles as well as many other vehicle dynamics variables cannot be measured in a cost-efficient way, a robust control method combined with a state estimator is required to guarantee the system stability. Furthermore, some parameters such as the tyre–road friction coefficient undergo frequent changes, and the aerodynamics resistance forces are often exerted as a disturbance during the wide driving condition. So, an adaptive sliding mode controller is applied to make vehicle yaw rate to track its reference with robustness against model uncertainties and disturbances and a non-linear estimator based on unscented Kalman filter is used to estimate wheel slip, yaw rate, road friction coefficient, longitudinal and lateral velocities. The estimation algorithm directly uses non-linear equations of the system and does not need the linearization and differentiation. The designed controller, which is insensitive to system uncertainties, offers the adaptive sliding gains to eliminate the precise determination of the bounds of uncertainties. The sliding gain values are determined using a simple adaptation algorithm that does not require extensive computational load. Numerical simulations of various manoeuvres using a non-linear full vehicle model with seven degrees of freedom demonstrate the high effectiveness of the presented controller for improving the vehicle yaw stability and handling performance.
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Qi, Liangwen, Liming Zheng, Xingzhi Bai, Qin Chen, Jiyao Chen, and Yan Chen. "Nonlinear Maximum Power Point Tracking Control Method for Wind Turbines Considering Dynamics." Applied Sciences 10, no. 3 (January 23, 2020): 811. http://dx.doi.org/10.3390/app10030811.
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A combined strategy of torque error feed-forward control and blade-pitch angle servo control is proposed to improve the dynamic power capture for wind turbine maximum power point tracking (MPPT). Aerodynamic torque is estimated using the unscented Kalman filter (UKF). Wind speed and tip speed ratio (TSR) are estimated using the Newton–Raphson method. The error between the estimated aerodynamic torque and the steady optimal torque is used as the feed-forward signal to control the generator torque. The gain parameters in the feed-forward path are nonlinearly regulated by the estimated generator speed. The estimated TSR is used as the reference signal for the optimal blade-pitch angle regulation at non-optimal TSR working points, which can improve the wind power capture for a wider non-optimal TSR range. The Fatigue, Aerodynamics, Structures, and Turbulence (FAST) code is used to simulate the aerodynamics and mechanical aspects of wind turbines while MATLAB/SIMULINK is used to simulate the doubly-fed induction generator (DFIG) system. The example of a 5 MW wind turbine model reveals that the new method is able to improve the dynamic response of wind turbine MPPT and wind power capture.
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Zhang, Hengbo, Xin Liu, Qingfeng Dou, and Qiongyao Han. "Multi-Source Error Calibration and Observability Analysis of Integrated Inertial Navigation System/Polarization Sensor Navigation System." Journal of Physics: Conference Series 2456, no. 1 (March 1, 2023): 012005. http://dx.doi.org/10.1088/1742-6596/2456/1/012005.
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Abstract Accurate polarization information acquisition is the key technique of the integrated inertial navigation system (INS)/polarization sensor (PS) navigation system. To solve the problem of poor performance of polarization sensors that real-time error calibration by using a rotary table, we propose a fast calibration method that polarization sensor combined with inertial navigation systems. Firstly, we construct the calibration model of polarization sensor. A tightly coupled INS/PS calibration model is proposed, specifically, the polarization sensor errors, which are the intensity gain coefficient, the degree of polarization coefficient, and the installation angle error of a polarizer, are augmented into the state of the integrated navigation systems. By introducing the error term of polarization sensor into the state of the integrated navigation systems, the error estimation can be carried out and the speed of error calibration can be improved. Secondly, considering the nonlinearity of error calibration model, we use the Unscented Kalman Filter (UKF) to calibrate the error. On this basis, we make use of the observability analysis to verify its validity of the proposed model in a dynamic scene. Finally, real-world experiments are conducted on the self-developed multi-channel polarization sensor. The experimental results show that the calibration method can be done quickly without the use of a rotary table.
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Chen, L., G. H. Wang, S. Y. Jia, and I. Progri. "Attitude Bias Conversion Model for Mobile Radar Error Registration." Journal of Navigation 65, no. 4 (July 10, 2012): 651–70. http://dx.doi.org/10.1017/s0373463312000239.
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Besides offset biases (such as range, the gain of range, azimuth, and elevation biases), for mobile radars, platform attitude biases (such as yaw, pitch, and roll biases) induced by the accumulated errors of the Inertial Measurement Units (IMU) of the Inertial Navigation System (INS) can also influence radar measurements. Both kinds of biases are coupled. Based on the analyses of the coupling influences and the observability of 3-D radars’ error registration model, in the article, an Attitude Bias Conversion Model (ABCM) based on Square Root Unscented Kalman Filter (SRUKF) is proposed. ABCM can estimate 3-D radars’ absolute offset biases under the influences of platform attitude biases. It converts platform attitude biases into radar measurement errors, by which the target East-North-Up (ENU) coordinates can be obtained from radar measurements directly without using the rotation transformation, which was usually used in the transition from platform frame to ENU considering attitude biases. In addition, SRUKF can avoid the inaccurate estimations caused by linearization, and it can weaken the adverse influences of the poor attitude bias estimation results in the application of ABCM. Theoretical derivations and simulation results show that 1) ABCM-SRUKF can improve elevation bias estimate accuracy to about 0·8 degree in the mean square error sense; 2) linearization is not the main reason for poor estimation of attitude biases; and 3) unobservability is the main reason.
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Chen, Yudi, Xiangyu Liu, Changqing Li, Jiao Zhu, Min Wu, and Xiang Su. "UAV Swarm Centroid Tracking for Edge Computing Applications Using GRU-Assisted Multi-Model Filtering." Electronics 13, no. 6 (March 12, 2024): 1054. http://dx.doi.org/10.3390/electronics13061054.
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When an unmanned aerial vehicles (UAV) swarm is used for edge computing, and high-speed data transmission is required, accurate tracking of the UAV swarm’s centroid is of great significance for the acquisition and synchronization of signal demodulation. Accurate centroid tracking can also be applied to accurate communication beamforming and angle tracking, bringing about a reception gain. Group target tracking (GTT) offers a suitable framework for tracking the centroids of UAV swarms. GTT typically involves accurate modeling of target maneuvering behavior and effective state filtering. However, conventional coordinate-uncoupled maneuver models and multi-model filtering methods encounter difficulties in accurately tracking highly maneuverable UAVs. To address this, an innovative approach known as 3DCDM-based GRU-MM is introduced for tracking the maneuvering centroid of a UAV swarm. This method employs a multi-model filtering technique assisted by a gated recurrent unit (GRU) network based on a suitable 3D coordinate-coupled dynamic model. The proposed dynamic model represents the centroid’s tangential load, normal load, and roll angle as random processes, from which a nine-dimensional unscented Kalman filter is derived. A GRU is utilized to update the model weights of the multi-model filtering. Additionally, a smoothing-differencing module is presented to extract the maneuvering features from position observations affected by measurement noise. The resulting GRU-MM method achieved a classification accuracy of 99.73%, surpassing that of the traditional IMM algorithm based on the same model. Furthermore, our proposed 3DCDM-based GRU-MM method outperformed the Singer-KF and 3DCDM-based IMM-EKF in terms of the RMSE for position estimation, which provides a basis for further edge computing.
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Rahimi, Hossein, Amir Ali Nikkhah, and Kaveh Hooshmandi. "A fast alignment of marine strapdown inertial navigation system based on adaptive unscented Kalman Filter." Transactions of the Institute of Measurement and Control, June 29, 2020, 014233122093429. http://dx.doi.org/10.1177/0142331220934293.
Full textAbstract:
This study has presented an efficient adaptive unscented Kalman filter (AUKF) with the new measurement model for the strapdown inertial navigation system (SINS) to improve the initial alignment under the marine mooring conditions. Conventional methods of the accurate alignment in the ship’s SINS usually fail to succeed within an acceptable period of time due to the components of external perturbations caused by the movement of sea waves and wind waves. To speed up convergence, AUKF takes into account the impact of the dynamic acceleration on the filter and its gain adaptively tuned by considering the dynamic scale sensed by accelerometers. This approach considerably improved the corrections of the current residual error on the SINS and decreased the influence due to the external perturbations caused by the ship’s movement. Initial alignment algorithm based on AUKF is designed for large misalignment angles and verified by experimental data. The experimental test results show that the proposed algorithm enhanced the convergence speed of SINS initial alignment compared with some state-of-the-art existing approaches.
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Bai, Xingzhen, Xinlei Zheng, Leijiao Ge, Feiyu Qin, and Yuanliang Li. "Event-Triggered Forecasting-Aided State Estimation for Active Distribution System With Distributed Generations." Frontiers in Energy Research 9 (July 23, 2021). http://dx.doi.org/10.3389/fenrg.2021.707183.
Full textAbstract:
In this study, the forecasting-aided state estimation (FASE) problem for the active distribution system (ADS) with distributed generations (DGs) is investigated, considering the constraint of data transmission. First of all, the system model of the ADS with DGs is established, which expands the scope of the ADS state estimation from the power network to the DGs. Moreover, in order to improve the efficiency of data transmission under the limited communication bandwidth, a component-based event-triggered mechanism is employed to schedule the data transmission from the measurement terminals to the estimator. It can efficiently reduce the amount of data transmission while guaranteeing the performance of system state estimation. Second, an event-triggered unscented Kalman filter (ET-UKF) algorithm is proposed to conduct the state estimation of the ADS with mixed measurements. To this end, the unscented transform (UT) technique is employed to approximate the probability distribution of the state variable after nonlinear transformation, which can reach more than second order, and then, an upper bound of the filtering error covariance is derived and subsequently minimized at each iteration. The gain of the desired filter is obtained recursively by following a certain set of recursions. Finally, the effectiveness of the proposed method is demonstrated by using the IEEE-34 distribution test system.
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Liang, Zhenhu, Dihuan Wang, Xing Jin, Luxin Fan, Xin Wen, Changwei Wei, and Xiaoli Li. "Tracking the effects of propofol, sevoflurane and (S)-ketamine anesthesia using an unscented kalman filter-based neural mass model." Journal of Neural Engineering, March 9, 2023. http://dx.doi.org/10.1088/1741-2552/acc2e8.
Full textAbstract:
Abstract Objective: Neural mass model (NMM) has been widely used to investigate the neurophysiological mechanisms of anesthestic drugs induced general anesthesia (GA). However, whether the parameters of NMM could track the effects of anesthesia still unknown.
Approach: We proposed using the cortical NMM (CNMM) to infer the potential neurophysiological mechanism of three different anesthetic drugs (i.e., propofol, sevoflurane, and (S)-ketamine) induced GA, and we employed unscented Kalman filter (UKF) to track any change in raw electroencephalography (rEEG) in frontal area during GA. We did this by estimating the parameters of population gain [i.e., excitatory/inhibitory postsynaptic potential (EPSP/IPSP, i.e., parameter A/B in CNMM) and the time constant rate of EPSP/IPSP (i.e., parameter a/b in CNMM). We compared the rEEG and simulated EEG (sEEG) from the perspective of spectrum, phase-amplitude coupling (PAC), and permutation entropy (PE). 
Main results: Under three estimated parameters (i.e., A, B, and a for propofol/sevoflurane or b for (S)-ketamine), the rEEG and sEEG had similar waveforms, time-frequency spectra, and PAC patterns during GA for the three drugs. The PE curves derived from rEEG and sEEG had high correlation coefficients (propofol: 0.97±0.03, sevoflurane: 0.96±0.03, (S)-ketamine: 0.98±0.02) and coefficients of determination (R2) (propofol: 0.86±0.03, sevoflurane: 0.68±0.30, (S)-ketamine: 0.70±0.18). Except for parameter A for sevoflurane, the estimated parameters for each drug in CNMM can differentiate wakefulness and non-wakefulness states. Compared with the simulation of three estimated parameters, the UKF-based CNMM had lower tracking accuracy under the simulation of four estimated parameters (i.e., A, B, a, and b) for three drugs.
Significance: The results demonstrate that a combination of CNMM and UKF could track the neural activities during GA. The EPSP/IPSP and their time constant rate can interpert the anesthetic drug’s effect on the brain, and can be used as a new index for depth of anesthesia monitoring.
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Chen, Ye, Guoliang Tao, and Yitao Yao. "A dual adaptive robust control for nonlinear systems with parameter and state estimation." Measurement and Control, October 10, 2023. http://dx.doi.org/10.1177/00202940231200956.
Full textAbstract:
Stabilization and learning are imperative to the high-performance feedback control of nonlinear systems. A dual adaptive robust control (DARC) scheme is proposed for nonlinear systems with model uncertainties to achieve a desired level of performance. Only the output of the nonlinear system is accessible in this work, all the states and parameters are learned online. Firstly, the DARC uses the prior physical bounds of systems to design a discontinuous projection with update rate limits which confines the bounds of parameter and state estimation. Then robustness of the nonlinear system can be guaranteed by the deterministic robust control (DRC) method. Secondly, a dual adaptive estimation mechanism (DAEM) is developed to learn the unknown parameters and states of systems. One part of the DAEM is the bounded gain forgetting (BGF) estimator, which is developed to handle inaccurate parameters and parametric variations. The other is the adaptive unscented Kalman filter (AUKF) synthesized for state estimation. The AUKF contains a statistic estimator based on the maximum a posterior (MAP) rule to estimate the unknown covariance matrix. Finally, simulation results illustrate the effectiveness of the suggested method.