ILC-based generalised PI control for output PDF of stochastic systems using LMI and RBF neural networks

Hong Wang; Puya Afshar; Hong Yue

ILC-based generalised PI control for output PDF of stochastic systems using LMI and RBF neural networks

Hong Wang, Puya Afshar, Hong Yue

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

Abstract

In this paper, a fixed-structure Iterative Learning Control (ILC) control design is presented for the tracking control of the output probability density functions (PDF) in general stochastic systems with non-Gaussian variables. The approximation of the output PDF is firstly realized using a Radial Basis Function Neural Network (RBFNN). Then the control horizon is divided to certain intervals called batches. ILC laws are employed to tune the PDF model parameters between two adjacent batches. A three-stage method is proposed which incorporates a) Identifying nonlinear parameters of the PDF model using subspace system identification methods; b) Calculating the generalised PI controller coefficients using LMI-based convex optimisation approach; and c) Updating the RFBNN parameters between batches based on ILC framework. Closed-loop stability and convergence analysis together with simulation results are also included in the paper. © 2006 IEEE.

Original language	English
Title of host publication	Proceedings of the IEEE Conference on Decision and Control\|Proc IEEE Conf Decis Control
Pages	5048-5053
Number of pages	5
Publication status	Published - 2006
Event	45th IEEE Conference on Decision and Control 2006, CDC - San Diego, CA Duration: 1 Jul 2006 → …

Conference

Conference	45th IEEE Conference on Decision and Control 2006, CDC
City	San Diego, CA
Period	1/07/06 → …

Keywords

Convergence analysis
ILC mechanism
LMI
PI control
RBF neural networks
Stochastic systems
Subspace system identification
Tracking performance

Cite this

@inproceedings{e6f7b8f9c131499fa1befcb4b993b97d,

title = "ILC-based generalised PI control for output PDF of stochastic systems using LMI and RBF neural networks",

abstract = "In this paper, a fixed-structure Iterative Learning Control (ILC) control design is presented for the tracking control of the output probability density functions (PDF) in general stochastic systems with non-Gaussian variables. The approximation of the output PDF is firstly realized using a Radial Basis Function Neural Network (RBFNN). Then the control horizon is divided to certain intervals called batches. ILC laws are employed to tune the PDF model parameters between two adjacent batches. A three-stage method is proposed which incorporates a) Identifying nonlinear parameters of the PDF model using subspace system identification methods; b) Calculating the generalised PI controller coefficients using LMI-based convex optimisation approach; and c) Updating the RFBNN parameters between batches based on ILC framework. Closed-loop stability and convergence analysis together with simulation results are also included in the paper. {\textcopyright} 2006 IEEE.",

keywords = "Convergence analysis, ILC mechanism, LMI, PI control, RBF neural networks, Stochastic systems, Subspace system identification, Tracking performance",

author = "Hong Wang and Puya Afshar and Hong Yue",

year = "2006",

language = "English",

isbn = "1424401712",

pages = "5048--5053",

booktitle = "Proceedings of the IEEE Conference on Decision and Control|Proc IEEE Conf Decis Control",

note = "45th IEEE Conference on Decision and Control 2006, CDC ; Conference date: 01-07-2006",

}

TY - GEN

T1 - ILC-based generalised PI control for output PDF of stochastic systems using LMI and RBF neural networks

AU - Wang, Hong

AU - Afshar, Puya

AU - Yue, Hong

PY - 2006

Y1 - 2006

N2 - In this paper, a fixed-structure Iterative Learning Control (ILC) control design is presented for the tracking control of the output probability density functions (PDF) in general stochastic systems with non-Gaussian variables. The approximation of the output PDF is firstly realized using a Radial Basis Function Neural Network (RBFNN). Then the control horizon is divided to certain intervals called batches. ILC laws are employed to tune the PDF model parameters between two adjacent batches. A three-stage method is proposed which incorporates a) Identifying nonlinear parameters of the PDF model using subspace system identification methods; b) Calculating the generalised PI controller coefficients using LMI-based convex optimisation approach; and c) Updating the RFBNN parameters between batches based on ILC framework. Closed-loop stability and convergence analysis together with simulation results are also included in the paper. © 2006 IEEE.

AB - In this paper, a fixed-structure Iterative Learning Control (ILC) control design is presented for the tracking control of the output probability density functions (PDF) in general stochastic systems with non-Gaussian variables. The approximation of the output PDF is firstly realized using a Radial Basis Function Neural Network (RBFNN). Then the control horizon is divided to certain intervals called batches. ILC laws are employed to tune the PDF model parameters between two adjacent batches. A three-stage method is proposed which incorporates a) Identifying nonlinear parameters of the PDF model using subspace system identification methods; b) Calculating the generalised PI controller coefficients using LMI-based convex optimisation approach; and c) Updating the RFBNN parameters between batches based on ILC framework. Closed-loop stability and convergence analysis together with simulation results are also included in the paper. © 2006 IEEE.

KW - Convergence analysis

KW - ILC mechanism

KW - LMI

KW - PI control

KW - RBF neural networks

KW - Stochastic systems

KW - Subspace system identification

KW - Tracking performance

M3 - Conference contribution

SN - 1424401712

SN - 9781424401710

SP - 5048

EP - 5053

BT - Proceedings of the IEEE Conference on Decision and Control|Proc IEEE Conf Decis Control

T2 - 45th IEEE Conference on Decision and Control 2006, CDC

Y2 - 1 July 2006

ER -

ILC-based generalised PI control for output PDF of stochastic systems using LMI and RBF neural networks

Abstract

Conference

Keywords

Fingerprint

Cite this