Title :
A feature-based data-driven approach for controller design and tuning
Author :
Xu, Jian-Xin ; Ji, Dongxu
Author_Institution :
Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore, Singapore
Abstract :
Traditionally controller tuning is model based. In many practical applications, however, the process model cannot be obtained and model-free tuning is imperative. In industrial control the huge amount of data is available, but we lack effective controller tuning schemes that are data driven instead of model driven. To address this issue, in this paper we first introduce the concept of feature space that can capture the characteristics of a control process, either in the time domain, frequency domain, or others. (data space to feature space, dim reduction) Next we introduce the control basis function space and control parameter space. The features and parameters form a mapping relationship. The controller tuning process can thus be formulated into the inversion of the mapping that yields appropriate control parameters and minimizes the mismatching between reference features and actual features. When the inversion is not analytically solvable, the iterative learning tuning method can be used.
Keywords :
control system synthesis; three-term control; control basis function space; control parameter space; controller design; controller tuning; feature space concept; feature-based data-driven approach; model-free tuning; Data engineering; Electrical equipment industry; Frequency domain analysis; Industrial control; Iterative methods; Process control; Stability; Three-term control; Tuning; Zinc; basis function space; data-driven; feature-based tuning; parameter searching;
Conference_Titel :
Cybernetics and Intelligent Systems (CIS), 2010 IEEE Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-6499-9
DOI :
10.1109/ICCIS.2010.5518560