On Multi-Zone Tracking and Non-Gaussian Noise Filtering for Model Predictive Control

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.