Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|Title:||A novel model-free adaptive control design for multivariable industrial processes|
|Citation:||IEEE Transactions on Industrial Electronics, 2014; 61(11):6391-6398|
|Dezhi Xu, Bin Jiang and Peng Shi|
|Abstract:||In this paper, a multiple adaptive observer-based strategy is proposed for the control of multi-input multi-output nonlinear processes using input/output (I/O) data. In the strategy, the pseudopartial-derivative parameter matrix of compact form dynamic linearization is estimated by a multiple adaptive observer, which is used to dynamically linearize a nonlinear system. Then, the proposed data-driven model-free-adaptive-control algorithm is only based on the online identified multiobserver models derived from the I/O data of the controlled plants, and Lyapunov-based stability analysis is used to ensure that all signals of the close-loop control system are bounded. A numerical example and a Wood/Berry distillation column example are provided to show that the proposed control algorithm has a very reliable tracking ability and a satisfactory robustness to disturbances and process dynamics variations.|
|Keywords:||Data-driven control; model-free adaptive control (MFAC); multiple adaptive observer; multivariable nonlinear systems; pseudopartial derivative (PPD)|
|Rights:||© 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.|
|Appears in Collections:||Electrical and Electronic Engineering publications|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.