• Title/Summary/Keyword: Adaptive Estimator

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Adaptive Immersion and Invariance Control of the Van der Pol Equation

  • Khovidhungij, Watcharapong;Santhanapipatkul, Ponesit
    • 제어로봇시스템학회:학술대회논문집
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    • 2005.06a
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    • pp.706-709
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    • 2005
  • We study the adaptive stabilization of the Van der Pol equation. A parameter update law is designed by the immersion and invariance method, and is used in conjunction with both the feedback linearization and backstepping control laws. Simulation results show that the responses obtained in the adaptive case are very similar to the known parameter case, and the parameter estimator converges to the true value.

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Adaptive Receding Horizon $H_{\infty}$ Controller Design for LPV Systems

  • P., PooGyeon;J., SeungCheol
    • 제어로봇시스템학회:학술대회논문집
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    • 2000.10a
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    • pp.535-535
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    • 2000
  • This paper presents an adaptive receding horizon H$_{\infty}$ controller for the linear parameter varying systems in the deterministic environment, which combines a parameter range estimator and a robust receding horizon H$_{\infty}$ controller using the parameter bounds. Using parameter set inclusion and terminal inequality condition, the closed-loop system stability is guaranteed. It is shown that the stabilizing adaptive receding horizon H$_{\infty}$ controller guarantees the H$_{\infty}$ norm bound.

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A Design of Adaptive Controller with Nonlinear Dynamic Friction Compensator for Precise Position Control of Linear Motor System (선형모터 정밀 위치제어를 위한 비선형 동적 마찰력 보상기를 갖는 적응 제어기 설계)

  • Lee, Jin-Woo;Cho, Hyun-Cheol;Lee, Young-Jin;Lee, Kwom-Soon
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.56 no.5
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    • pp.944-957
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    • 2007
  • In general mechanical servo systems, friction deteriorates the performance of controllers by its nonlinear characteristics. Especially, friction phenomenon causes steady-state tracking errors and limit cycles in position and velocity control systems, even though gains of controllers are tuned well in linear system model. Even if sensor is used higher accuracy level, it is difficult to improve tracking performance of the position to the same level with a general control method such as PID type. Therefore, many friction models were proposed and compensation methods have been researched actively. In this paper, we consider that the variation of mover's mass is various by loading and unloading. The normal force variation occurs by it and other parameters. Therefore, the proposed control system is composed of main position controller and a friction compensator. A parameter estimator for a nonlinear friction model is designed by adaptive control law and adaptive backstopping control method.

ASSVD: Adaptive Sparse Singular Value Decomposition for High Dimensional Matrices

  • Ding, Xiucai;Chen, Xianyi;Zou, Mengling;Zhang, Guangxing
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.6
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    • pp.2634-2648
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    • 2020
  • In this paper, an adaptive sparse singular value decomposition (ASSVD) algorithm is proposed to estimate the signal matrix when only one data matrix is observed and there is high dimensional white noise, in which we assume that the signal matrix is low-rank and has sparse singular vectors, i.e. it is a simultaneously low-rank and sparse matrix. It is a structured matrix since the non-zero entries are confined on some small blocks. The proposed algorithm estimates the singular values and vectors separable by exploring the structure of singular vectors, in which the recent developments in Random Matrix Theory known as anisotropic Marchenko-Pastur law are used. And then we prove that when the signal is strong in the sense that the signal to noise ratio is above some threshold, our estimator is consistent and outperforms over many state-of-the-art algorithms. Moreover, our estimator is adaptive to the data set and does not require the variance of the noise to be known or estimated. Numerical simulations indicate that ASSVD still works well when the signal matrix is not very sparse.

Inverse active wind load inputs estimation of the multilayer shearing stress structure

  • Chen, Tsung-Chien;Lee, Ming-Hui
    • Wind and Structures
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    • v.11 no.1
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    • pp.19-33
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    • 2008
  • This research investigates the adaptive input estimation method applied to the multilayer shearing stress structure. This method is to estimate the values of wind load inputs by analyzing the active reaction of the system. The Kalman filter without the input term and the adaptive weighted recursive least square estimator are two main portions of this method. The innovation vector can be produced by the Kalman filter, and be applied to the adaptive weighted recursive least square estimator to estimate the wind load input over time. This combined method can effectively estimate the wind loads to the structure system to enhance the reliability of the system active performance analysis. The forms of the simulated inputs (loads) in this paper include the periodic sinusoidal wave, the decaying exponent, the random combination of the sinusoidal wave and the decaying exponent, etc. The active reaction computed plus the simulation error is regard as the simulated measurement and is applied to the input estimation algorithm to implement the numerical simulation of the inverse input estimation process. The availability and the precision of the input estimation method proposed in this research can be verified by comparing the actual value and the one obtained by numerical simulation.

Estimating survival distributions for two-stage adaptive treatment strategies: A simulation study

  • Vilakati, Sifiso;Cortese, Giuliana;Dlamini, Thembelihle
    • Communications for Statistical Applications and Methods
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    • v.28 no.5
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    • pp.411-424
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    • 2021
  • Inference following two-stage adaptive designs (also known as two-stage randomization designs) with survival endpoints usually focuses on estimating and comparing survival distributions for the different treatment strategies. The aim is to identify the treatment strategy(ies) that leads to better survival of the patients. The objectives of this study were to assess the performance three commonly cited methods for estimating survival distributions in two-stage randomization designs. We review three non-parametric methods for estimating survival distributions in two-stage adaptive designs and compare their performance using simulation studies. The simulation studies show that the method based on the marginal mean model is badly affected by high censoring rates and response rate. The other two methods which are natural extensions of the Nelson-Aalen estimator and the Kaplan-Meier estimator have similar performance. These two methods yield survival estimates which have less bias and more precise than the marginal mean model even in cases of small sample sizes. The weighted versions of the Nelson-Aalen and the Kaplan-Meier estimators are less affected by high censoring rates and low response rates. The bias of the method based on the marginal mean model increases rapidly with increase in censoring rate compared to the other two methods. We apply the three methods to a leukemia clinical trial dataset and also compare the results.

Dual EKF-Based State and Parameter Estimator for a LiFePO4 Battery Cell

  • Pavkovic, Danijel;Krznar, Matija;Komljenovic, Ante;Hrgetic, Mario;Zorc, Davor
    • Journal of Power Electronics
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    • v.17 no.2
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    • pp.398-410
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    • 2017
  • This work presents the design of a dual extended Kalman filter (EKF) as a state/parameter estimator suitable for adaptive state-of-charge (SoC) estimation of an automotive lithium-iron-phosphate ($LiFePO_4$) cell. The design of both estimators is based on an experimentally identified, lumped-parameter equivalent battery electrical circuit model. In the proposed estimation scheme, the parameter estimator has been used to adapt the SoC EKF-based estimator, which may be sensitive to nonlinear map errors of battery parameters. A suitable weighting scheme has also been proposed to achieve a smooth transition between the parameter estimator-based adaptation and internal model within the SoC estimator. The effectiveness of the proposed SoC and parameter estimators, as well as the combined dual estimator, has been verified through computer simulations on the developed battery model subject to New European Driving Cycle (NEDC) related operating regimes.

Nonlinear Friction Control Using the Robust Friction State Observer and Recurrent Fuzzy Neural Network Estimator (강인한 마찰 상태 관측기와 순환형 퍼지신경망 관측기를 이용한 비선형 마찰제어)

  • Han, Seong-Ik
    • Transactions of the Korean Society of Machine Tool Engineers
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    • v.18 no.1
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    • pp.90-102
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    • 2009
  • In this paper, a tracking control problem for a mechanical servo system with nonlinear dynamic friction is treated. The nonlinear friction model contains directly immeasurable friction state and the uncertainty caused by incomplete modeling and variations of its parameter. In order to provide the efficient solution to these control problems, we propose a hybrid control scheme, which consists of a robust friction state observer, a RFNN estimator and an approximation error estimator with sliding mode control. A sliding mode controller and a robust friction state observer is firstly designed to estimate the unknown infernal state of the LuGre friction model. Next, a RFNN estimator is introduced to approximate the unknown lumped friction uncertainty. Finally, an adaptive approximation error estimator is designed to compensate the approximation error of the RFNN estimator. Some simulations and experiments on the mechanical servo system composed of ball-screw and DC servo motor are presented. Results demonstrate the remarkable performance of the proposed control scheme.

An Adaptive M-estimators Robust Estimation Algorithm (적응적 M-estimators 강건 예측 알고리즘)

  • Jang Seok-Woo;Kim Jin-Uk
    • Journal of the Korea Society of Computer and Information
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    • v.10 no.2 s.34
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    • pp.21-30
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    • 2005
  • In general, the robust estimation method is well known for a good statistical estimator that is insensitive to small departures from the idealized assumptions for which the estimation is optimized. While there are many existing robust estimation techniques that have been proposed in the literature, two main techniques used in computer vision are M-estimators and least-median of squares (LMS). Among these. we utilized the M-estimators since they are known to provide an optimal estimation of affine motion parameters. The M-estimators have higher statistical efficiency but tolerate much lower percentages of outliers unless properly initialized. To resolve these problems, we proposed an adaptive M-estimators algorithm that effectively separates outliers from non-outliers and estimate affine model parameters, using a continuous sigmoid weight function. The experimental results show the superiority of our method.

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