Iterative fault tolerant control based on Stochastic Distribution

A new design of a fault tolerant control (FTC)-based an adaptive, fixed-structure PI controller, with constraints on the state vector for nonlinear discrete-time system subject to stochastic non-Gaussian disturbance is studied. The objective of the reliable control algorithm scheme is to design a co...

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Published in:2011 50th IEEE Conference on Decision and Control and European Control Conference pp. 2100 - 2105
Main Authors: Skaf, Z., AI-Bayati, Ahmad, Hong Wang, Aiping Wang
Format: Conference Proceeding
Language:English
Published: IEEE 01.12.2011
Subjects:
Closed loop systems
Fault tolerance
Fault tolerant systems
Probability density function
Stochastic systems
Vectors
ISBN:9781612848006, 1612848001
ISSN:0191-2216
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Abstract A new design of a fault tolerant control (FTC)-based an adaptive, fixed-structure PI controller, with constraints on the state vector for nonlinear discrete-time system subject to stochastic non-Gaussian disturbance is studied. The objective of the reliable control algorithm scheme is to design a control signal such that the actual probability density function (PDF) of the system is made as close as possible to a desired PDF, and make the tracking performance converge to zero, not only when all components are functional but also in case of admissible faults. A Linear Matrix Inequality (LMI)-based FTC method is presented to ensure that the fault can be estimated and compensated for. A radial basis function (RBF) neural network is used to approximate the output PDF of the system. Thus, the aim of the output PDF control will be a RBF weight control with an adaptive tuning of the basis function parameters. The key issue here is to divide the control horizon into a number of equal time intervals called batches. Within each interval, there are a fixed number of sample points. The design procedure is divided into two main algorithms, within each batch, and between any two adjacent batches. A P-type ILC law is employed to tune the parameters of the RBF neural network so that the PDF tracking error decreases along with the batches. Sufficient conditions for the proposed fault tolerance are expressed as LMIs. An analysis of the ILC convergence is carried out. Finally, the effectiveness of the proposed method is demonstrated with an illustrated example.
AbstractList A new design of a fault tolerant control (FTC)-based an adaptive, fixed-structure PI controller, with constraints on the state vector for nonlinear discrete-time system subject to stochastic non-Gaussian disturbance is studied. The objective of the reliable control algorithm scheme is to design a control signal such that the actual probability density function (PDF) of the system is made as close as possible to a desired PDF, and make the tracking performance converge to zero, not only when all components are functional but also in case of admissible faults. A Linear Matrix Inequality (LMI)-based FTC method is presented to ensure that the fault can be estimated and compensated for. A radial basis function (RBF) neural network is used to approximate the output PDF of the system. Thus, the aim of the output PDF control will be a RBF weight control with an adaptive tuning of the basis function parameters. The key issue here is to divide the control horizon into a number of equal time intervals called batches. Within each interval, there are a fixed number of sample points. The design procedure is divided into two main algorithms, within each batch, and between any two adjacent batches. A P-type ILC law is employed to tune the parameters of the RBF neural network so that the PDF tracking error decreases along with the batches. Sufficient conditions for the proposed fault tolerance are expressed as LMIs. An analysis of the ILC convergence is carried out. Finally, the effectiveness of the proposed method is demonstrated with an illustrated example.
Author Aiping Wang
Skaf, Z.
Hong Wang
AI-Bayati, Ahmad
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  surname: Skaf
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  givenname: Ahmad
  surname: AI-Bayati
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  surname: Hong Wang
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  organization: Control Syst. Center, Univ. of Manchester, Manchester, UK
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  surname: Aiping Wang
  fullname: Aiping Wang
  email: apwang401@126.com
  organization: Inst. of Comput. Sci., Anhui Univ., Hefei, China
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Snippet A new design of a fault tolerant control (FTC)-based an adaptive, fixed-structure PI controller, with constraints on the state vector for nonlinear...
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StartPage 2100
SubjectTerms Closed loop systems
Fault tolerance
Fault tolerant systems
Probability density function
Stochastic systems
Vectors
Title Iterative fault tolerant control based on Stochastic Distribution
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