• Title/Summary/Keyword: lyapunov

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Stability Regions of Linear Slowly Time-Varying Systemsa (천천히 변하는 선형 시변 시스템의 안정도 영역)

  • 최종호;장태정
    • 제어로봇시스템학회:학술대회논문집
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    • 1988.10a
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    • pp.210-213
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    • 1988
  • By using Lyapunov method, sufficient conditions for linear time-varying continuous-time and discrete-time systems to be stable are presented under the assumption that the systems are slowly time-varying. Though it is not simple to find the stability regions immediately, one could find practical and large stability regions by constructing an appropriate algorithm.

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The delay margin of the LQG regulator

  • Kim, Sang-Woo;Kwon, Wook-Hyun
    • 제어로봇시스템학회:학술대회논문집
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    • 1988.10b
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    • pp.749-752
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    • 1988
  • In this paper, the delay margins of the LQ and the LQG regulators are obtained in the time domain. These margins are represented in terms of the singular values of system matrices and the solutions of a Riccati equation and a Lyapunov one. And their asymptotical properties when gains tend to infinity are investigated.

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A STUDY ON INITIAL CONVERGENCE PROPERTIES OF THE KALMAN FILLTERING ALGORITHM

  • Park, Dong-Jo
    • 제어로봇시스템학회:학술대회논문집
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    • 1988.10b
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    • pp.1051-1054
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    • 1988
  • In this paper we present initial convergence properties of the Kalman filtering algorithm, we put an arbitrary small positive correlation matrix as an initial condition in the recursive algorithm. This arbitrary small initial condition perturbs the Kalman filtering algorithm and may lead to initial instability. We derive a condition which insures the stable operation of the Kalman filtering algorithm from the stochastic Lyapunov difference equation.

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LQG design under plant perturbation and uncertain noise covariance (패러미터와 잡음전력이 불확실한 시스템에 대한 LQG 제어기 설계)

  • 오원근;서병설
    • 제어로봇시스템학회:학술대회논문집
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    • 1991.10a
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    • pp.203-207
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    • 1991
  • In this paper, a linear stocastic dynamic system with norm - bounded plant perpurbations and norm - bounded noise covariarice is studied. Instead of Bellman-Gronwall inequality used in previous study, Lyapunov stability theorem is used to derive stability condition. The new condition is of more compact form than the previous result.

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Constrained $H_\infty$ Optimal Control

  • Park, Jinhoon
    • 전기의세계
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    • v.49 no.9
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    • pp.4-8
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    • 2000
  • Recently we have shown based on Lyapunov theorem that the closed loop system with the constrained infinite horizon H$\infty$ optimal controller is exponentially stable. moreover the on-line feedback implementation of the constrained infinite horizon H$\infty$ optimal control based on quadratic programs has been proposed. n this paper we summarize and discuss these results.

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Exponential stability of stochastic static neutral neural networks with varying delays

  • Sun, Xiaoqi
    • Computers and Concrete
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    • v.30 no.4
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    • pp.237-242
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    • 2022
  • This paper is concerned with exponential stability in mean square for stochastic static neutral neural networks with varying delays. By using Lyapunov functional method and with the help of stochastic analysis technique, the sufficient conditions to guarantee the exponential stability in mean square for the neural networks are obtained and some results of related literature are extended.

Stable Path Tracking Control of a Mobile Robot Using a Wavelet Based Fuzzy Neural Network

  • Oh, Joon-Seop;Park, Jin-Bae;Choi, Yoon-Ho
    • International Journal of Control, Automation, and Systems
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    • v.3 no.4
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    • pp.552-563
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    • 2005
  • In this paper, we propose a wavelet based fuzzy neural network (WFNN) based direct adaptive control scheme for the solution of the tracking problem of mobile robots. To design a controller, we present a WFNN structure that merges the advantages of the neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the process of fuzzy reasoning of the wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. In our control system, the control signals are directly obtained to minimize the difference between the reference track and the pose of a mobile robot via the gradient descent (GD) method. In addition, an approach that uses adaptive learning rates for training of the WFNN controller is driven via a Lyapunov stability analysis to guarantee fast convergence, that is, learning rates are adaptively determined to rapidly minimize the state errors of a mobile robot. Finally, to evaluate the performance of the proposed direct adaptive control system using the WFNN controller, we compare the control results of the WFNN controller with those of the FNN, the WNN and the WFM controllers.