Please use this identifier to cite or link to this item:
Title: On Multi-Zone Tracking and Non-Gaussian Noise Filtering for Model Predictive Control
Keywords: Optimal Control, Semiconductor Manufacturing, Temperature Control, Model Predictive Control, Non-Gaussian Noise Filter, Computational Load
Issue Date: 31-Mar-2014
Citation: WANG XIAOQIONG (2014-03-31). On Multi-Zone Tracking and Non-Gaussian Noise Filtering for Model Predictive Control. ScholarBank@NUS Repository.
Abstract: Model Predictive Control (MPC) has been widely studied and adopted in industrial applications. Some attempts have been done for temperature uniformity control which focused on the set-point tracking uniformity from batch to batch, not the uniformity of the zone-to-zone temperature trajectories. We proposed Uniformity MPC (UMPC) to achieve output uniformity. The idea of UMPC is to reconstruct the cost function of the Standard MPC. Bake-plate experiments were carried out to verify the UMPC uniformity advantage over SMPC. Most of MPC designs use Kalman filter to filter the measurement noise which is assumed to be Gaussian distributed. This is a limitation in the case of non-Gaussian noise as Kalman filter is sensitive to outliers. We proposed ARMAX filter for MPC by modeling noise with the GT distribution, as it can model non-Gaussian noise. We provide one of the first experimental verification of the computational load reduction property of Multiplexed MPC.
Appears in Collections:Ph.D Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
WangXiaoqiong.pdf4.21 MBAdobe PDF



Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.