Picture of wind turbine against blue sky

Open Access research with a real impact...

The Strathprints institutional repository is a digital archive of University of Strathclyde research outputs.

The Energy Systems Research Unit (ESRU) within Strathclyde's Department of Mechanical and Aerospace Engineering is producing Open Access research that can help society deploy and optimise renewable energy systems, such as wind turbine technology.

Explore wind turbine research in Strathprints

Explore all of Strathclyde's Open Access research content

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

Wang, H. and Afshar, P. and Yue, H. (2006) ILC-based generalised PI control for output PDF of stochastic systems using LMI and RBF neural networks. In: 45th IEEE Conference on Decision and Control, 2006-12-13 - 2006-12-15.

Full text not available in this repository. (Request a copy from the Strathclyde author)

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 LNH-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