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.

scholarly journals EMP: Extended Kalman Filter Based Self-Adaptive Mold Protection Method on a Toggle Mechanism

2020 ◽  
Vol 10 (3) ◽  
pp. 940
Author(s):  
Baiping Chen ◽  
Huifeng Wu ◽  
Hongwei Zhou ◽  
Danfeng Sun
Keyword(s):  
Kalman Filter ◽  
Injection Molding ◽  
Extended Kalman Filter ◽  
High Performance ◽  
Foreign Bodies ◽  
Adaptive Method ◽  
Protection Method ◽  
Current Curve ◽  
Maintenance Process ◽  
Self Adaptive

Nowadays, the plastic injection molding industry is ever-growing, crucial, and its plastic products can be seen everywhere. However, the mold damage problem still frustrates operators because of its high maintenance price and time-consuming maintenance process. This damage is commonly caused by foreign bodies in mold area, and the conventional mold protection method is insufficient for high-performance injection molding machines because of the uncertainty from many setting parameters. To improve detection precision of mold protection driven by a toggle mechanism ( T M ), this paper puts forward E M P , i.e., an extended Kalman filter ( E K F ) based self-adaptive mold protection method, wherein the E K F is used in current curve optimization, and the self-adaptive method ( S A M ) is proposed to gain an safety range of current curve. The E M P was verified in a 140-ton electric injection molding machine. Compared with a general method, the proposed method decreases the detected distance of mold protection by 22% under different thickness foreign bodies.

Identification of Stochastic System and Controller via Projection Filters

1996 ◽  
Vol 118 (2) ◽  
pp. 169-176 ◽  
Author(s):  
Hyun Chang Lee ◽  
Min-Hung Hsiao ◽  
Jen-Kuang Huang ◽  
Chung-Wen Chen
Keyword(s):  
Kalman Filter ◽  
State Space ◽  
Stochastic System ◽  
State Space Model ◽  
Open Loop ◽  
The State ◽  
Loop System ◽  
Test Facility ◽  
System Response ◽  
Space Model

A method based on projection filters is presented for identifying an open-loop stochastic system with an existing feedback controller. The projection filters are derived from the relationship between the state-space model and the AutoRegressive with eXogeneous input (ARX) model including the system, Kalman filter and controller. Two ARX models are identified from the control input, closed-loop system response and feedback signal using least-squares method. Markov parameters of the open-loop system, Kalman filter and controller are then calculated from the coefficients of the identified ARX models. Finally, the state-space model of the open-loop stochastic system and the gain matrices for the Kalman filter and controller are realized. The method is validated by simulations and test data from an unstable large-angle magnetic suspension test facility.

Design of Extended Kalman Filter for a Shape Memory Alloy Manipulator

2003 ◽  
Author(s):  
Mohammad H. Elahinia ◽  
Hashem Ashrafiuon ◽  
Mehdi Ahmadian ◽  
William T. Baumann
Keyword(s):  
Kalman Filter ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Extended Kalman Filter ◽  
State Space Model ◽  
Angular Position ◽  
The State ◽  
State Variables ◽  
Time Step ◽  
Noisy Measurements

This paper presents an Extended Kalman Filter (EKF) for estimation of the state variables of a single degree of freedom rotary manipulator actuated by Shape Memory Alloy (SMA). A state space model for the SMA manipulator is presented. The model includes nonlinear dynamics of the manipulator, constitutive model of Shape Memory Alloy, and the electrical and heat transfer behavior of SMA wire. In the experimental setup, angular position of the arm is the only state variable that is measured. The other state variables of the system are arm’s angular velocity, SMA wire’s stress, temperature and the Martensite factor, which are not available experimentally due to measurement difficulties. Hence, a model-based state estimator that works with noisy measurements is presented based on the Extended Kalman Filter. This estimator predicts the state vector at each time step and corrects its prediction based on the angular position of the arm which can be measured experimentally. The state variables collected through model simulations are also used to evaluate the performance of the EKF. Several EKF simulations are presented that show accurate, and robust performance of the estimator for different types of inputs.

Adaptive Model Estimation of Vibration Motion for a Nanopositioner With Moving Horizon Optimized Extended Kalman Filter

2013 ◽  
Vol 135 (4) ◽  
Author(s):  
Tomáš Polóni ◽  
Arnfinn Aas Eielsen ◽  
Boris Rohal’-Ilkiv ◽  
Tor Arne Johansen
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
High Performance ◽  
Optimization Procedure ◽  
Vibration Signal ◽  
Performance Model ◽  
Superior Performance ◽  
Displacement Sensor ◽  
Vibration Model ◽  
Before And After

Fast, reliable online estimation and model adaptation is the first step towards high-performance model-based nanopositioning control and monitoring systems. This paper considers the identification of parameters and the estimation of states of a nanopositioner with a variable payload based on the novel moving horizon optimized extended Kalman filter (MHEKF). The MHEKF is experimentally tested and verified with measured data from the capacitive displacement sensor. The payload, attached to the nanopositioner's sample platform, suddenly changes during the experiment triggering the transient motion of the vibration signal. The transient is observed through the load dependent parameters of a single-degree-of-freedom vibration model, such as spring, damping, and actuator gain constants. The platform, before and after the payload change, is driven by the excitation signal applied to the piezoelectric actuator. The information regarding displacement and velocity, together with the system parameters and a modeled force disturbance, is estimated through the algorithm involving the iterative sequential quadratic programming (SQP) optimization procedure defined on a moving horizon window. The MHEKF provided superior performance in comparison with the benchmark method, extended Kalman filter (EKF), in terms of faster convergence.

scholarly journals A Progressive Extended Kalman Filter Method for Freeway Traffic State Estimation Integrating Multisource Data

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Yingshun Liu ◽  
Shanglu He ◽  
Bin Ran ◽  
Yang Cheng
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
State Space Model ◽  
Heterogeneous Data ◽  
Filter Method ◽  
Multiple Sources ◽  
Freeway Traffic ◽  
Traffic Conditions ◽  
Kalman Filter Method

Variable techniques have been used to collect traffic data and estimate traffic conditions. In most cases, more than one technology is available. A legitimate need for research and application is how to use the heterogeneous data from multiple sources and provide reliable and consistent results. This paper aims to integrate the traffic features extracted from the wireless communication records and the measurements from the microwave sensors for the state estimation. A state-space model and a Progressive Extended Kalman Filter (PEKF) method are proposed. The results from the field test exhibit that the proposed method efficiently fuses the heterogeneous multisource data and adaptively tracks the variation of traffic conditions. The proposed method is satisfactory and promising for future development and implementation.

On-Line Monitoring of Fluid Effective Bulk Modulus Using Piezoelectric Transducer Impedance

2007 ◽  
Author(s):  
Gi-Woo Kim ◽  
K. W. Wang
Keyword(s):  
Bulk Modulus ◽  
Automatic Transmission ◽  
System Response ◽  
Hydraulic Control ◽  
Actuation System ◽  
Effective Bulk Modulus ◽  
System Response Time ◽  
On Line ◽  
And Performance ◽  
Entrapped Air

The fluid effective bulk modulus plays a significant role in hydraulic control systems due to its effect on the system response time and performance. In general, the fluid effective bulk modulus is a function of the amount of entrapped air, pressure, and temperature variations. Therefore, it has been recognized that monitoring of the effective bulk modulus is essential for the control of hydraulic actuation system. Measuring of the effective bulk modulus is a very challenging task. Current methods normally require precision testing equipments. Furthermore, the required equipment usually consists of many complex components that will affect the bulk modulus. Their size is in general large and thus makes on-line measurement impractical. In this research, we develop a new on-line effective bulk modulus measuring technique based upon the impedance of piezoelectric transducers. Using the piezoelectric impedance equation, numerical simulation for predicting the peak resonance frequency is performed to identify its dependency on the variation of the fluid bulk modulus. In order to verify the results, the effective bulk modulus of an ATF (automatic transmission fluid) is examined experimentally. The proposed method and the conventional method are also compared. The experimental results show the validity of the proposed method.

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