PARAMETER IDENTIFICATION IN A HIGH PERFORMANCE HYDROSTATIC ACTUATION SYSTEM USING THE UNSCENTED KALMAN FILTER

2006 ◽  
Vol 30 (3) ◽  
pp. 375-390 ◽  
Author(s):  
Yuvin Chinniah ◽  
Saeid Habibi ◽  
Richard Burton ◽  
Eric Sampson
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
High Performance ◽  
Unscented Kalman Filter ◽  
Actuation System

Parameter Estimation in a Hydrostatic System Using Extended Kalman Filter

2001 ◽  
Author(s):  
Yuvin A. Chinniah ◽  
Richard Burton ◽  
Saeid Habibi
Keyword(s):  
Kalman Filter ◽  
Bulk Modulus ◽  
Extended Kalman Filter ◽  
High Performance ◽  
State Space Model ◽  
System Response ◽  
Actuation System ◽  
Accuracy And Precision ◽  
Hydrostatic System ◽  
Entrained Air

Abstract In this paper, the Extended Kalman Filter (EKF) estimation technique is applied to a novel hydrostatic actuation system referred to as the Electrohydraulic Actuator (EHA). A state space model of the EHA is developed and the effective bulk modulus is estimated in simulation. The EHA is a high performance actuation system capable of moving large loads with very high accuracy and precision. In a practical situation, this parameter is very difficult to measure directly as it depends on entrained air which cannot be known at a particular point of time. The bulk modulus is critical for system response and a low bulk modulus as a result of air in the system can seriously hinder the performance of EHA and cause safety problems.

Parameter identification of the dynamic Winkler soil–structure interaction model using a hybrid unscented Kalman filter–multi-objective harmony search algorithm

2020 ◽  
Vol 23 (12) ◽  
pp. 2653-2668
Author(s):  
Javier Naranjo-Pérez ◽  
Javier Fernando Jiménez-Alonso ◽  
Andrés Sáez
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Soil Structure ◽  
Optimization Problem ◽  
Unscented Kalman Filter ◽  
Harmony Search ◽  
Soil Structure Interaction ◽  
Structure Interaction ◽  
Multi Objective ◽  
Winkler Model

Soil–structure interaction is a key aspect to take into account when simulating the response of civil engineering structures subjected to dynamic actions. To this end, and due to its simplicity and ease of implementation, the dynamic Winkler model has been widely used in practical engineering applications. In this model, soil–structure interaction is simulated by means of spring–damper elements. A crucial point to guarantee the adequate performance of the approach is to accurately estimate the constitutive parameters of these elements. To this aim, this article proposes the application of a recently developed parameter identification method to address such problem. In essence, the parameter identification problem is transformed into an optimization problem, so that the parameters of the dynamic Winkler model are estimated by minimizing the relative differences between the numerical and experimental modal properties of the overall soil–structure system. A recent and efficient hybrid algorithm, based on the combination of the unscented Kalman filter and multi-objective harmony search algorithms, is satisfactorily implemented to solve the optimization problem. The performance of this proposal is then validated via its implementation in a real case-study involving an integral footbridge.

scholarly journals Online Parameter Identification and State of Charge Estimation of Battery Based on Multitimescale Adaptive Double Kalman Filter Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Wenxian Duan ◽  
Chuanxue Song ◽  
Yuan Chen ◽  
Feng Xiao ◽  
Silun Peng ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Unscented Kalman Filter ◽  
Least Square Method ◽  
State Of Charge ◽  
Least Square ◽  
Accurate Estimation ◽  
Battery Life ◽  
Model Parameters ◽  
Simulation Results

An accurate state of charge (SOC) can provide effective judgment for the BMS, which is conducive for prolonging battery life and protecting the working state of the entire battery pack. In this study, the first-order RC battery model is used as the research object and two parameter identification methods based on the least square method (RLS) are analyzed and discussed in detail. The simulation results show that the model parameters identified under the Federal Urban Driving Schedule (HPPC) condition are not suitable for the Federal Urban Driving Schedule (FUDS) condition. The parameters of the model are not universal through the HPPC condition. A multitimescale prediction model is also proposed to estimate the SOC of the battery. That is, the extended Kalman filter (EKF) is adopted to update the model parameters and the adaptive unscented Kalman filter (AUKF) is used to predict the battery SOC. The experimental results at different temperatures show that the EKF-AUKF method is superior to other methods. The algorithm is simulated and verified under different initial SOC errors. In the whole FUDS operating condition, the RSME of the SOC is within 1%, and that of the voltage is within 0.01 V. It indicates that the proposed algorithm can obtain accurate estimation results and has strong robustness. Moreover, the simulation results after adding noise errors to the current and voltage values reveal that the algorithm can eliminate the sensor accuracy effect to a certain extent.

scholarly journals Parameter Identification of the Acrobot Using the Unscented Kalman Filter

2005 ◽  
Vol 71 (708) ◽  
pp. 2563-2570 ◽  
Author(s):  
Nozomu ARAKI ◽  
Michito OKADA ◽  
Yasuo KONISHI ◽  
Hiroyuki ISHIGAKI
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Unscented Kalman Filter

scholarly journals Multiple Road-Objects Detection and Tracking for Autonomous Driving

2021 ◽  
Author(s):  
Wael Farag ◽  
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
High Performance ◽  
Unscented Kalman Filter ◽  
Measurement Data ◽  
Autonomous Driving ◽  
Estimation Accuracy ◽  
Time Performance ◽  
Detection And Tracking ◽  
Objects Detection

In this paper, a real-time road-Object Detection and Tracking (LR_ODT) method for autonomous driving is proposed. The method is based on the fusion of lidar and radar measurement data, where they are installed on the ego car, and a customized Unscented Kalman Filter (UKF) is employed for their data fusion. The merits of both devices are combined using the proposed fusion approach to precisely provide both pose and velocity information for objects moving in roads around the ego car. Unlike other detection and tracking approaches, the balanced treatment of both pose estimation accuracy and its real-time performance is the main contribution in this work. The proposed technique is implemented using the high-performance language C++ and utilizes highly optimized math and optimization libraries for best real-time performance. Simulation studies have been carried out to evaluate the performance of the LR_ODT for tracking bicycles, cars, and pedestrians. Moreover, the performance of the UKF fusion is compared to that of the Extended Kalman Filter fusion (EKF) showing its superiority. The UKF has outperformed the EKF on all test cases and all the state variable levels (-24% average RMSE). The employed fusion technique show how outstanding is the improvement in tracking performance compared to the use of a single device (-29% RMES with lidar and -38% RMSE with radar).

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