Gaussian Process-based Min-norm Stabilizing Controller for Control-Affine Systems with Uncertain Input Effects and Dynamics

In this paper, we propose a Model Predictive Controller (MPC) based on Gaussian process for nonlinear systems with uncertain delays and external Gaussian disturbances. We investigate the ability of Gaussian process based MPC on handling the variable delay that follows a Gaussian distribution through a properly selected observation horizon. To test the effectiveness of this approach, comparisons are made for the proposed Gaussian process based MPC and RBF (Radial Basis Function) neural networks by analyzing the time complexity and control performance. In simulations, two experiments are designed to verify the results of different systems, including a first-order nonlinear plant and a second-order nonlinear plant with variable delays and Gaussian noises. It is demonstrated that the proposed approach may achieve the desired results.

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Integrated Fractional white Noise as an Alternative to Multifractional Brownian Motion

Journal of Applied Probability ◽

10.1017/s0021900200003041 ◽

2007 ◽

Vol 44 (02) ◽

pp. 393-408 ◽

Cited By ~ 1

Author(s):

Allan Sly

Keyword(s):

Stochastic Process ◽

Brownian Motion ◽

White Noise ◽

Gaussian Process ◽

Discrete Data ◽

Hölder Exponent ◽

Scaling Properties ◽

Multifractional Brownian Motion ◽

Local Properties

Multifractional Brownian motion is a Gaussian process which has changing scaling properties generated by varying the local Hölder exponent. We show that multifractional Brownian motion is very sensitive to changes in the selected Hölder exponent and has extreme changes in magnitude. We suggest an alternative stochastic process, called integrated fractional white noise, which retains the important local properties but avoids the undesirable oscillations in magnitude. We also show how the Hölder exponent can be estimated locally from discrete data in this model.

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A Stochastic Model for the Inheritance of the Cancer Proneness Phenotype

Methods of Information in Medicine ◽

10.1055/s-0038-1636689 ◽

1987 ◽

Vol 26 (03) ◽

pp. 117-123

Author(s):

P. Tautu ◽

G. Wagner

Keyword(s):

Stochastic Model ◽

Gaussian Process ◽

Covariance Function ◽

Neoplastic Disease ◽

Disease Phenotype ◽

Probabilistic Representation ◽

Temporal Behaviour ◽

Continuous Trait ◽

Continuous Parameter ◽

Level Zero

SummaryA continuous parameter, stationary Gaussian process is introduced as a first approach to the probabilistic representation of the phenotype inheritance process. With some specific assumptions about the components of the covariance function, it may describe the temporal behaviour of the “cancer-proneness phenotype” (CPF) as a quantitative continuous trait. Upcrossing a fixed level (“threshold”) u and reaching level zero are the extremes of the Gaussian process considered; it is assumed that they might be interpreted as the transformation of CPF into a “neoplastic disease phenotype” or as the non-proneness to cancer, respectively.

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Dự đoán xu thế chỉ số chứng khoán Việt Nam VN-Index sử dụng phân tích hồi quy Gaussian Process và mô hình tự hồi quy trung bình động ARMA

10.32913/rd-ict.571 ◽

2018 ◽

Author(s):

Quyết Thắng Huỳnh

Keyword(s):

Gaussian Process ◽

Viet Nam

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Circle and Popov Criterion for Output Feedback Stabilization of Uncertain Systems

Nonlinear Analysis Modelling and Control ◽

10.15388/na.2006.11.2.14754 ◽

2006 ◽

Vol 11 (2) ◽

pp. 137-148 ◽

Cited By ~ 1

Author(s):

A. Benabdallah ◽

M. A. Hammami

Keyword(s):

Dynamical Systems ◽

Output Feedback ◽

Uncertain Systems ◽

Feedback Stabilization ◽

Stabilizing Controller ◽

Nominal System ◽

Output Feedback Stabilization ◽

Uncertain Dynamical Systems

In this paper, we address the problem of output feedback stabilization for a class of uncertain dynamical systems. An asymptotically stabilizing controller is proposed under the assumption that the nominal system is absolutely stable.

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