Adaptive estimation algorithm of boost-phase trajectory using binary asynchronous observation

2016 ◽  
Vol 230 (14) ◽  
pp. 2661-2672 ◽  
Author(s):  
Nan Wu ◽  
Lei Chen ◽  
Yongjun Lei ◽  
Fankun Meng
Keyword(s):  
State Estimation ◽  
Phase Trajectory ◽  
Prior Information ◽  
Estimation Error ◽  
The State ◽  
Estimation Accuracy ◽  
Unknown Input ◽  
Noise Variance ◽  
Process Noise ◽  
Variance Matrix

A kind of adaptive filter algorithm based on the estimation of the unknown input is proposed for studying the adaptive adjustment of process noise variance of boost phase trajectory. Polynomial model is used as the motion model of the boost trajectory, truncation error is regarded as an equivalent to the process noise and the unknown input and process noise variance matrix is constructed from the estimation value of unknown input according to the quantitative relationship among the unknown input, the state estimation error, and optimal process noise variance. The simulation results show that in the absence of prior information, the unknown input is estimated effectively in terms of magnitude, a positive definite matrix of process noise covariance which is close to the optimal value is constructed real-timely, and the state estimation error approximates the error lower bound of the optimal estimation. The estimation accuracy of the proposed algorithm is similar to that of the current statistical model algorithm using accurate prior information.

scholarly journals Globally Optimal Distributed Kalman Filtering for Multisensor Systems with Unknown Inputs

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2976 ◽  
Author(s):  
Yali Ruan ◽  
Yingting Luo ◽  
Yunmin Zhu
Keyword(s):  
State Estimation ◽  
Kalman Filtering ◽  
Optimal Algorithm ◽  
The State ◽  
Unknown Input ◽  
Initial Value ◽  
State Estimate ◽  
Multisensor Systems ◽  
Unknown Inputs ◽  
Augmented State

In this paper, the state estimation for dynamic system with unknown inputs modeled as an autoregressive AR (1) process is considered. We propose an optimal algorithm in mean square error sense by using difference method to eliminate the unknown inputs. Moreover, we consider the state estimation for multisensor dynamic systems with unknown inputs. It is proved that the distributed fused state estimate is equivalent to the centralized Kalman filtering using all sensor measurement; therefore, it achieves the best performance. The computation complexity of the traditional augmented state algorithm increases with the augmented state dimension. While, the new algorithm shows good performance with much less computations compared to that of the traditional augmented state algorithms. Moreover, numerical examples show that the performances of the traditional algorithms greatly depend on the initial value of the unknown inputs, if the estimation of initial value of the unknown input is largely biased, the performances of the traditional algorithms become quite worse. However, the new algorithm still works well because it is independent of the initial value of the unknown input.

scholarly journals A Strong Tracking Mixed-Degree Cubature Kalman Filter Method and Its Application in a Quadruped Robot

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2251 ◽  
Author(s):  
Jikai Liu ◽  
Pengfei Wang ◽  
Fusheng Zha ◽  
Wei Guo ◽  
Zhenyu Jiang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Real Time ◽  
Quadruped Robot ◽  
The State ◽  
Estimation Accuracy ◽  
Robot System ◽  
Calculation Time ◽  
Motion State ◽  
Cubature Kalman Filter

The motion state of a quadruped robot in operation changes constantly. Due to the drift caused by the accumulative error, the function of the inertial measurement unit (IMU) will be limited. Even though multi-sensor fusion technology is adopted, the quadruped robot will lose its ability to respond to state changes after a while because the gain tends to be constant. To solve this problem, this paper proposes a strong tracking mixed-degree cubature Kalman filter (STMCKF) method. According to system characteristics of the quadruped robot, this method makes fusion estimation of forward kinematics and IMU track. The combination mode of traditional strong tracking cubature Kalman filter (TSTCKF) and strong tracking is improved through demonstration. A new method for calculating fading factor matrix is proposed, which reduces sampling times from three to one, saving significantly calculation time. At the same time, the state estimation accuracy is improved from the third-degree accuracy of Taylor series expansion to fifth-degree accuracy. The proposed algorithm can automatically switch the working mode according to real-time supervision of the motion state and greatly improve the state estimation performance of quadruped robot system, exhibiting strong robustness and excellent real-time performance. Finally, a comparative study of STMCKF and the extended Kalman filter (EKF) that is commonly used in quadruped robot system is carried out. Results show that the method of STMCKF has high estimation accuracy and reliable ability to cope with sudden changes, without significantly increasing the calculation time, indicating the correctness of the algorithm and its great application value in quadruped robot system.

Sensor Fusion of Odometer, Compass and Beacon Distance for Mobile Robots

2020 ◽  
Vol 10 (1) ◽  
pp. 1-17
Author(s):  
Rufus Fraanje ◽  
René Beltman ◽  
Fidelis Theinert ◽  
Michiel van Osch ◽  
Teade Punter ◽  
...  
Keyword(s):  
State Estimation ◽  
Sensor Fusion ◽  
Simulation Experiment ◽  
Estimation Problem ◽  
Unknown Input ◽  
Fusion Algorithm ◽  
Process Noise ◽  
Distance Measurements ◽  
Differential Drive ◽  
Filter Solution

The estimation of the pose of a differential drive mobile robot from noisy odometer, compass, and beacon distance measurements is studied. The estimation problem, which is a state estimation problem with unknown input, is reformulated into a state estimation problem with known input and a process noise term. A heuristic sensor fusion algorithm solving this state-estimation problem is proposed and compared with the extended Kalman filter solution and the Particle Filter solution in a simulation experiment.

scholarly journals State Uncertainty Normality Detection

2019 ◽  
Vol 67 (3) ◽  
pp. 1044-1062
Author(s):  
Sven K. Flegel ◽  
James C. Bennett
Keyword(s):  
State Estimation ◽  
Orbit Determination ◽  
Estimation Error ◽  
The State ◽  
Body Motion ◽  
Optical Observations ◽  
Test Cases ◽  
Unscented Transform ◽  
State Uncertainty ◽  
Random Particle

AbstractTwo fundamentally different approaches of determining normality of the probability density function of the state estimation error are compared by application to a range of test cases. The first method is the Henze-Zirkler test, which operates on a random particle sample. The variability of its result is quantified. Using this method, departure from normality has been found to occur in three stages which are detailed. The second test compares the offset in whitened space of the predicted state to the predicted covariance mean obtained from the unscented transform. This test is much more efficient than the random particle based approach and can be applied using any perturbations model. The comparison is performed on the state estimation error in Cartesian space and using two-body motion without process noise. The more efficient, unscented transform based approach shows excellent agreement with the Henze-Zirkler test for constructed test cases. Application to orbit determination results from passive optical observations assessed with a Batch-Least-Squares orbit determination however reveals some discrepancies which have yet to be understood and underline the importance of rigorous testing.

Hierarchical Estimator of Dual Clutch Torques for a Power-Split Hybrid Electric Vehicle

2019 ◽  
Author(s):  
Jianwu Zhang ◽  
Defeng Xu
Keyword(s):  
Hybrid Electric Vehicle ◽  
Control Strategies ◽  
Estimation Error ◽  
Transient State ◽  
Estimation Accuracy ◽  
Unknown Input ◽  
Unknown Input Observer ◽  
Reduced Order ◽  
Power Split ◽  
Reduced Order Observer

Abstract For fast drive mode transitions by shifting clutches equipped in the dedicated compound power-split hybrid transmission, correct estimations of pressure and torque of the clutches are crucial for control strategies. A hierarchical estimator is proposed herein for individual estimation of the clutch torques, consisting of not only the reference layer containing the unknown input observer of vehicle resistance and the reduced-order observer of drive shaft torque, but also the estimation layer combining the unknown input observer with the reduced-order observer. The estimator is implemented to strike a balance between estimation accuracy in the steady state and real time response in the transient state. For validation of the estimator, simulations and real car tests are carried out in specific drive conditions. By numerical results, it’s demonstrated that excellent predictive abilities are found including reasonably small estimation error and adaptive capability and, as a result, shift to shift induced driveline oscillations and vehicle jerks are reduced significantly.

scholarly journals Smart Grid State Estimation with PMUs Time Synchronization Errors

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5148
Author(s):  
Marco Todescato ◽  
Ruggero Carli ◽  
Luca Schenato ◽  
Grazia Barchi
Keyword(s):  
State Estimation ◽  
Distribution System ◽  
Time Synchronization ◽  
The State ◽  
Estimation Accuracy ◽  
Real Field ◽  
State Variables ◽  
Smart Power ◽  
Actual Performance ◽  
Synchronization Errors

State Estimation (SE) is one of the essential tasks to monitor and control the smart power grid. This paper presents a method to estimate the state variables combining the measurement of power demand at each bus with the data collected from a limited number of Phasor Measurement Units (PMUs). Although PMU data are usually assumed to be perfectly synchronized with the Coordinated Universal Time (UTC), this work explicitly considers the presence of time-synchronization errors due, for instance, to the actual performance of GPS receivers and the limited stability of the internal oscillator. The proposed algorithm is a recursive Kalman filter which not only estimates the state variables of the power system, but also the frequency deviations causing clock offsets which eventually affect the timestamps of the measures returned by different PMUs. The proposed solution was tested and compared with alternative approaches using both synthetic data applied to the IEEE 123 bus distribution feeder and real-field data collected from a small-size medium-voltage (MV) distribution system located inside the EPFL campus in Lausanne. Results show the validity of the proposed method in terms of state estimation accuracy. In particular, when some synchronization errors are present, the proposed algorithm can estimate and compensate for them.

scholarly journals Performance and stochastic stability of the adaptive fading extended Kalman filter with the matrix forgetting factor

2016 ◽  
Vol 14 (1) ◽  
pp. 934-945
Author(s):  
Cenker Biçer ◽  
Levent Özbek ◽  
Hasan Erbay
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
Stochastic Systems ◽  
Estimation Error ◽  
The State ◽  
Forgetting Factor ◽  
The Matrix ◽  
Stochastic Framework ◽  
Theoretical Results

AbstractIn this paper, the stability of the adaptive fading extended Kalman filter with the matrix forgetting factor when applied to the state estimation problem with noise terms in the non–linear discrete–time stochastic systems has been analysed. The analysis is conducted in a similar manner to the standard extended Kalman filter’s stability analysis based on stochastic framework. The theoretical results show that under certain conditions on the initial estimation error and the noise terms, the estimation error remains bounded and the state estimation is stable.The importance of the theoretical results and the contribution to estimation performance of the adaptation method are demonstrated interactively with the standard extended Kalman filter in the simulation part.

scholarly journals Parameter and State Estimation of Shear Buildings Using Spline Interpolation and Linear Integral Filters

2018 ◽  
Vol 2018 ◽  
pp. 1-21
Author(s):  
Antonio Concha ◽  
Luis Alvarez-Icaza
Keyword(s):  
State Estimation ◽  
Spline Interpolation ◽  
Estimation Error ◽  
Fast Response ◽  
High Gain ◽  
The State ◽  
Small Scale ◽  
Identification Method ◽  
Linear Integral ◽  
Shear Building

A parameter identification method and a high gain observer are proposed in order to identify the model and to recover the state of a seismically excited shear building using acceleration responses of the ground and instrumented floors levels, as well as the responses at noninstrumented floors, which are reconstructed by means of cubic spline shape functions. The identification method can be implemented online or offline and uses Linear Integral Filters, whose bandwidth must enclose the spectrum of a seismically excited building. On the other hand, the proposed state observer estimates the displacements and velocities of all the structure floors using the model estimated by the identification method. The observer allows obtaining a fast response and reducing the state estimation error, while depending on a single gain. The performance of the parameter and state estimators is verified through experiments carried out on a five-story small scale building.

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