Noise control with Kalman filter for active headrest

A control strategy with Kalman filter (KF) is proposed for active noise control of virtual error signal for active headset. Comparing with the gradient based algorithm, KF algorithm has faster convergence speed and better convergence performance. In this paper, the state equation of the system is established on the basis of virtual error sensing, and only the weight coefficients of the control filter are considered in the state variables. In order to ensure the convergence performance of the algorithm, an online updating strategy of KF parameters is proposed. The fast-array method is also introduced into the algorithm to reduce the computation. The simulation results show that the present strategy can improve the convergence speed and effectively reduce the noise signal at the virtual error point.

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Non Linear State and Parameters Estimation in Chemical Processes: Analysis and Improvement of Three Estimation Structures Applied to a CSTR

International Journal of Chemical Reactor Engineering ◽

10.1515/1542-6580.2439 ◽

2011 ◽

Vol 9 (1) ◽

Cited By ~ 3

Author(s):

Héctor Botero ◽

Hernán Álvarez

Keyword(s):

Parameter Estimation ◽

Chemical Process ◽

Chemical Processes ◽

The State ◽

Convergence Speed ◽

Parameters Estimation ◽

State Variables ◽

Input Output ◽

On Line ◽

Linear State

This paper proposes a new composite observer capable of estimating the states and unknown (or changing) parameters of a chemical process, using some input-output measurements, the phenomenological based model and other available knowledge about the process. The proposed composite observer contains a classic observer (CO) to estimate the state variables, an observer-based estimator (OBE) to obtain the actual values of the unknown or changing parameters needed to tune the CO, and an asymptotic observer (AO) to estimate the states needed as input to the OBE. The proposed structure was applied to a CSTR model with three state variables. With the proposed structure, the concentration of reactants and other CSTR parameters can be estimated on-line if the reactor and jacket temperatures are known. The procedure for the design of the proposed structure is simple and guarantees observer convergence. In addition, the convergence speed of state and parameter estimation can be adjusted independently.

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Estimation of the State Variables and Unknown Input of a Two-Speed Electric Vehicle Driveline Using Fading-Memory Kalman Filter

IEEE Transactions on Transportation Electrification ◽

10.1109/tte.2016.2516105 ◽

2016 ◽

Vol 2 (2) ◽

pp. 210-220 ◽

Cited By ~ 9

Author(s):

Mir Saman Rahimi Mousavi ◽

Benoit Boulet

Keyword(s):

Kalman Filter ◽

Electric Vehicle ◽

The State ◽

Fading Memory ◽

Unknown Input ◽

State Variables

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Stability analysis of continuous-time linear systems consisting of n subsystems with different fractional orders

Bulletin of the Polish Academy of Sciences Technical Sciences ◽

10.2478/v10175-012-0037-2 ◽

2012 ◽

Vol 60 (2) ◽

pp. 279-284 ◽

Cited By ~ 8

Author(s):

M. Busłowicz

Keyword(s):

Linear Systems ◽

Continuous Time ◽

State Equation ◽

The State ◽

Natural Order ◽

State Variables ◽

The Stability ◽

Derivatives Of ◽

Fractional Orders ◽

Time Linear

Abstract. The stability problem of continuous-time linear systems described by the state equation consisting of n subsystems with different fractional orders of derivatives of the state variables has been considered. The methods for asymptotic stability checking have been given. The method proposed in the general case is based on the Argument Principle and it is similar to the modified Mikhailov stability criterion known from the stability theory of natural order systems. The considerations are illustrated by numerical examples.

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Parameter and State Estimator for State Space Models

The Scientific World JOURNAL ◽

10.1155/2014/106505 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Ruifeng Ding ◽

Linfan Zhuang

Keyword(s):

State Space ◽

State Equation ◽

The State ◽

Parameter Estimates ◽

Identification Algorithm ◽

State Variables ◽

Input Output ◽

State Estimator ◽

Output Data ◽

Canonical State

This paper proposes a parameter and state estimator for canonical state space systems from measured input-output data. The key is to solve the system state from the state equation and to substitute it into the output equation, eliminating the state variables, and the resulting equation contains only the system inputs and outputs, and to derive a least squares parameter identification algorithm. Furthermore, the system states are computed from the estimated parameters and the input-output data. Convergence analysis using the martingale convergence theorem indicates that the parameter estimates converge to their true values. Finally, an illustrative example is provided to show that the proposed algorithm is effective.

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Structural Stiffness Identification Based on the Extended Kalman Filter Research

Abstract and Applied Analysis ◽

10.1155/2014/103102 ◽

2014 ◽

Vol 2014 ◽

pp. 1-8

Author(s):

Fenggang Wang ◽

Xianzhang Ling ◽

Xun Xu ◽

Feng Zhang

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

State Equation ◽

The State ◽

Physical Parameters ◽

Structural Stiffness ◽

Structural Dynamic ◽

Discrete State ◽

Response Acquisition ◽

Shear Structure

For the response acquisition of the structure section measuring points, the method of identifying the structural stiffness parameters is developed by using the extended Kalman filter. The state equation of structural system parameter is a nonlinear equation. Dispersing the structural dynamic equation by using Newmark-βmethod, the state transition matrix of discrete state equation is deduced and the solution of discrete state equation is simplified. The numerical simulation shows that the error of structural recognition doesnot exceed 5% when the noise level is 3%. It meets the requirements of the error limit of the engineering structure, which indicates that the derivation described in this paper has the robustness for the structural stiffness recognition. Shear structure parameter identification examples illustrate its applicability, and the method can also be used to identify physical parameters of large structure.

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The Random Walk Model Kalman Filter in Multichannel Active Noise Control

IEEE Signal Processing Letters ◽

10.1109/lsp.2015.2475357 ◽

2015 ◽

Vol 22 (12) ◽

pp. 2244-2248 ◽

Cited By ~ 6

Author(s):

Paulo A. C. Lopes ◽

Jose A. B. Gerald ◽

Moises S. Piedade

Keyword(s):

Kalman Filter ◽

Random Walk ◽

Noise Control ◽

Active Noise Control ◽

Random Walk Model ◽

Active Noise

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Design of Extended Kalman Filter for a Shape Memory Alloy Manipulator

Volume 5: 19th Biennial Conference on Mechanical Vibration and Noise, Parts A, B, and C ◽

10.1115/detc2003/vib-48504 ◽

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.

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Application of the Extended Kalman Filter to Control of a Shape Memory Alloy Arm

Dynamic Systems and Control, Volumes 1 and 2 ◽

10.1115/imece2003-42013 ◽

2003 ◽

Cited By ~ 1

Author(s):

Mohammad H. Elahinia ◽

Hashem Ashrafiuon ◽

Mehdi Ahmadian ◽

Daniel J. Inman

Keyword(s):

Kalman Filter ◽

Shape Memory Alloy ◽

Shape Memory ◽

Extended Kalman Filter ◽

State Vector ◽

Angular Position ◽

The State ◽

State Variables ◽

Time Step ◽

State Variable

This paper presents a robust nonlinear control that uses a state variable estimator for control of a single degree of freedom rotary manipulator actuated by Shape Memory Alloy (SMA) wire. A model for SMA actuated manipulator is presented. The model includes nonlinear dynamics of the manipulator, a constitutive model of the Shape Memory Alloy, and the electrical and heat transfer behavior of SMA wire. The current experimental setup allows for the measurement of only one state variable which is the angular position of the arm. Due to measurement difficulties, the other three state variables, arm angular velocity and SMA wire stress and temperature, cannot be directly measured. A model-based state estimator that works with noisy measurements is presented based on the Extended Kalman Filter (EKF). This estimator predicts the state vector at each time step and corrects its prediction based on the angular position measurements. The estimator is then used in a nonlinear and robust control algorithm based on Variable Structure Control (VSC). The VSC algorithm is a control gain switching technique based on the arm angular position (and velocity) feedback and EKF estimated SMA wire stress and temperature. The state vector estimates help reduce or avoid the undesirable and inefficient overshoot problem in SMA one-way actuation control.

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