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Research on Stochastic Dynamic System’s Learning and Control

Author: ZhaoPing
Tutor: QianFuCai
School: Xi'an University of Technology
Course: Control Theory and Control Engineering
Keywords: Stochastic dynamic systems Control Learning
CLC: TP13
Type: Master's thesis
Year: 2010
Downloads: 35
Quote: 1
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Abstract


With the development of modern science and technology and production technology , the control system increasingly complicated modeling and control of complex systems has become one of the hot issues of the control sector research . Uncertainties , actual prevalence of different degrees of system control problems can be used not simple deterministic model to describe , so it is necessary to control systems and learning systems uncertainty combined as an issue to consider . The nature of learning and control of : the one hand , the control signal so that the system output tends desired target (called the control action) ; the other hand , the control signal but also reducing the uncertainty of the system parameters (referred to parameter uncertainty learning effect). Two roles in the implementation of the control law is contradictory , the former requires the control signal changes tend gentle , so they need to be weighed against the latter requires to maintain a certain level of incentive . In this paper, from the linear and nonlinear stochastic systems and multi-model nonlinear stochastic systems . ( 1 ) For the Gaussian white noise stochastic linear systems with unknown parameters , the utility function proposed a learning control strategy and control trade-offs . The controller is able to control both the system track the desired output , but also to learn the unknown parameters . Through simulation , to verify the effectiveness of the control method . (2) proposed a learning control problem of nonlinear stochastic systems with unknown parameters and control the trade-off optimization algorithm . Control on the one hand to make the output track the desired output ; RBF network on the other hand can be used to learn the unknown parameters in nonlinear systems . And simulation comparison shows the superiority of the algorithm . ( 3 ) for a class of unknown nonlinear multi- parameter model stochastic systems using RBF network online learning nonlinear function to estimate the model , the use of Bayes posterior probability Finally, according to the control signal on the cost function . Simulation , this algorithm can be more accurate draw system switching time , be able to accurately track system changes .

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CLC: > Industrial Technology > Automation technology,computer technology > Automated basic theory > Automatic control theory
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