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.

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.

Dynamic Sliding Mode Control of Air-to-Fuel Ratio in Internal Combustion Engines Using the Hybrid Extended Kalman Filter

2012 ◽  
Vol 433-440 ◽  
pp. 2092-2098 ◽  
Author(s):  
Majid Zohari ◽  
Mohamadreza Ahmadi ◽  
Hamed Mojallali
Keyword(s):  
Kalman Filter ◽  
Sliding Mode Control ◽  
Extended Kalman Filter ◽  
Sliding Mode ◽  
Nonlinear Observer ◽  
Superior Performance ◽  
Fuel Ratio ◽  
Mode Control ◽  
Dynamic Sliding Mode Control ◽  
Dynamic Sliding Mode

The large modeling uncertainties and the nonlinearities associated with air manifold and fuel injection in spark ignition (SI) engines has given rise to difficulties in the task of designing an adequate controller for air-to-fuel ratio (AFR) control. Although sliding mode control approaches has been suggested, the inescapable time-delay between control action and measurement update results in chattering. This paper proposes the implementation of a nonlinear observer based control scheme incorporating the hybrid extended Kalman filter (HEKF) and the dynamic sliding mode control (DSMC). The results established upon the proposed methodology are given which demonstrate superior performance in terms of reducing the chattering magnitude.

Experimental Study on Impact-Induced Damage Detection Using an Improved Extended Kalman Filter

2014 ◽  
Vol 14 (05) ◽  
pp. 1440007 ◽  
Author(s):  
Ying Lei ◽  
Zhilu Lai ◽  
Songye Zhu ◽  
Xiao-Hua Zhang
Keyword(s):  
Experimental Study ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Structural Damage ◽  
Shaking Table Test ◽  
Impact Load ◽  
Structural Parameters ◽  
Optimization Procedure ◽  
Shaking Table ◽  
Improved Performance

This paper presents an experimental study on using an improved extended Kalman filter (EKF) to identify impact-induced structural damage. By introducing the optimization of estimated residual error into the classical EKF, this real-time approach demonstrates an excellent capability to identify the abrupt changes of structural parameters instantly and accurately. The optimization procedure is activated when a prescribed threshold is exceeded. A shaking table test of a three-story steel frame subjected to abrupt damage induced by impact load was conducted to validate the improved EKF approach. The results clearly reveal its improved performance and good anti-noise ability in identifying time-variant structural parameters.

Hardware Implementation Optimization of Extended Kalman Filter for the Estimation of State of Charge of Li-Ion Battery

2015 ◽  
Vol 713-715 ◽  
pp. 1094-1098
Author(s):  
Hong Ce Zhang ◽  
Pu Shen Wang ◽  
Jiang Jiang ◽  
Hong Fei Cao
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Extended Kalman Filter ◽  
High Performance ◽  
Hardware Implementation ◽  
State Of Charge ◽  
Li Ion Batteries ◽  
Soc Estimation ◽  
Li Ion ◽  
Time Restriction

Extended Kalman Filter (EKF) is widely studied in the field of State of Charge (SOC) estimation of Li-ion batteries, however, in applications like Electric Vehicles (EV), there are usually a large number of individual battery cells. In order to meet the demand of real-time computation, MCU of high performance is essential. In this paper, we proposed a hardware structure to implement EKF which is economical in area and power consumption and could be easily integrated in a larger design and at the same time could satisfy the real-time restriction.

Real-Time and High-Performance Attitude and Heading Reference System Based on MIMU/Magnetometers

2009 ◽  
Vol 60-61 ◽  
pp. 219-223 ◽  
Author(s):  
Wei Qin ◽  
Wei Zheng Yuan ◽  
Hong Long Chang ◽  
Liang Xue ◽  
Guang Min Yuan
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Extended Kalman Filter ◽  
Reference System ◽  
High Performance ◽  
Dynamic Mode ◽  
Stationary Mode ◽  
Real Time Measurement ◽  
Data Refresh ◽  
Adaptive Extended Kalman Filter

In the paper, an attitude and heading reference system based on MIMU/magnetometers with moderate accuracy is presented. To meet the requirements of the real-time measurement, a master/slave CPU structure is proposed in order to improve the data refresh rate effectively. In the algorithm part, an adaptive extended Kalman filter equation is applied in the system, where the filter equation uses three tilt angles of attitude and three bias errors for the gyroscopes as state vectors, the measurements of three accelerometers and magnetometers are used to drive the state update. When the system is in dynamic mode, the measured values of the accelerometers consist of the gravity vector and the dynamic accelerations, an adaptive extended Kalman filter tunes its gain automatically based on the system dynamics sensed by the accelerometers to yield optimal performance. The experiment result shows that the attitude and heading angle errors are within 0.2 deg and 0.5 deg respectively in stationary mode, and the result can reflect the attitude angles reasonably in dynamic mode.

scholarly journals Extended Kalman Filter Based Speed-Sensorless Load Torque and Inertia Estimations with Observability Analysis for Induction Motors

2018 ◽  
Vol 3 (1) ◽  
pp. 115-127 ◽  
Author(s):  
Emrah Zerdali ◽  
Murat Barut
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
High Performance ◽  
Induction Motors ◽  
Viscous Friction ◽  
Speed Sensorless ◽  
Load Torque ◽  
Observability Analysis ◽  
Friction Term ◽  
Flux Estimation

Abstract This paper aims to introduce a novel extended Kalman filter (EKF) based estimator including observability analysis to the literature associated with the high performance speed-sensorless control of induction motors (IMs). The proposed estimator simultaneously performs the estimations of stator stationary axis components of stator currents and rotor fluxes, rotor mechanical speed, load torque including the viscous friction term, and reciprocal of total inertia by using measured stator phase currents and voltages. The inertia estimation is done since it varies with the load coupled to the shaft and affects the performance of speed estimation especially when the rotor speed changes. In this context, the estimations of all mechanical state and parameters besides flux estimation required for high performance control methods are performed together. The performance of the proposed estimator is tested by simulation and real-time experiments under challenging variations in load torque and velocity references; and in both transient and steady states, the quite satisfactory estimation performance is achieved.

scholarly journals Online Identification of Time-Variant Structural Parameters Under Unknown Inputs Basing On Extended Kalman Filter

2021 ◽  
Author(s):  
Xiaoxiong Zhang ◽  
Jia He ◽  
Xugang Hua ◽  
Zhengqing Chen ◽  
Ou Yang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Structural Parameters ◽  
Optimization Procedure ◽  
Unbiased Estimation ◽  
Time Varying ◽  
Time Step ◽  
Varying Parameters ◽  
Unknown Inputs ◽  
Time Varying Parameters

Abstract To date, a number of parameter identification methods have been developed for the purpose of structural health monitoring and vibration control. Among them, the extended Kalman filter (EKF) series methods are attractive in view of the efficient unbiased estimation in recursive manner. However, most of these methods are performed on the premise that the parameters are time-invariant and/or the loadings are known. To circumvent the aforementioned limitations, an online EKF with unknown input (OEKF-UI) approach is proposed in this paper for the identification of time-varying parameters and the unknown excitation. A revised observation equation is obtained with the aid of projection matrix. To capture the changes of structural parameters in real-time, an online tracking matrix (OTM) associated with the time-varying parameters is introduced and determined via an optimization procedure. Then, based on the principle of EKF, the recursive solution of structural states including the time-variant parameters can be analytically derived. Finally, using the estimated structural states, the unknown inputs are identified by means of least-squares estimation (LSE) at the same time-step. The effectiveness of the proposed approach is validated via linear and nonlinear numerical examples with the consideration of parameters being varied abruptly.

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