In this research, a unique subspace data driven control for linear parameter changing system with scheduling parameters is presented. This paves the way investigating nonlinear based on results regarding that are already known. Only matrix utilized to represent output prediction value in future various time instants, while input-output observation used identify Markov form of state space forms....

Abstract This paper presents the TS model identification method by which a great number of systems whose parameters vary dramatically with working states can be identified via Fuzzy Neural Networks (FNN). The suggested method could overcome the drawbacks of traditional linear system identification methods which are only effective under certain narrow working states and provide global dynamic de...

ABSTRACT: System or process identification is a mathematical modeling of systems (processes) from test or experimental data. Process models obtained from identification process can be used for process simulation, analysis, design of safety systems and control systems for the process. This paper presents the Linear Parameter Varying (LPV) modeling of 210MW Industrial Coal Fired Boiler which is c...

This paper presents a technique to design switching gainscheduling controllers for plants with time-varying parameters. Given a desired L2-gain, the proposed method divides the parameter set into several subsets, and designs a robust controller for each subset. The method can achieve higher performances than traditional gain-scheduling controller design methods. A numerical example shows the ef...