Edge-type Model Predictive Control with Online Self-tuning Function

Abstract

In this paper, we propose a self-tuning algorithm by which we can achieve autonomous engineering including model identification and control parameter tuning on the edge-type MPC (Model Predictive Control). In the model identification algorithm, offset estimation is introduced in order to replace step response tests. We compare the control performance of the self-tuning edge-type MPC with those of PID control and offline-tuned edge-type MPC for a MIMO stir process model. As a result, the proposed self-tuning edge-type MPC achieves faster tracking performance without any pre-tuning.