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|Title:||A feature-based data-driven approach for controller design and tuning|
|Authors:||Xu, J.-X. |
|Keywords:||Basis function space|
|Citation:||Xu, J.-X.,Ji, D. (2010). A feature-based data-driven approach for controller design and tuning. 2010 IEEE Conference on Cybernetics and Intelligent Systems, CIS 2010 : 172-178. ScholarBank@NUS Repository. https://doi.org/10.1109/ICCIS.2010.5518560|
|Abstract:||Taditionally 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 efective controller tuning schemes that are data driven instead of model driven. To address this issue, in this paper we frst 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 learing tuning method can be used. © 2010 IEEE.|
|Source Title:||2010 IEEE Conference on Cybernetics and Intelligent Systems, CIS 2010|
|Appears in Collections:||Staff Publications|
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