Data-Driven Control of Nonlinear Process Systems Using a Three-Degree-of-Freedom Model-on-Demand Model Predictive Control Framework.

ABSTRACT: This paper presents a Model-on-Demand (MoD) approach to system identification and its integration with a three-degree-of-freedom Kalman filter-based Model Predictive Control (3DoF-KF MPC) framework. MoD estimation represents a hybrid of local and global modeling techniques, judiciously formulated to take advantage of both while not being computationally demanding. The 3DoF-KF MPC algorithm enables responses to set point changes and measured and unmeasured disturbances to be tuned intuitively and independently, thereby providing superior performance and ease of use over tuning with move suppression and error weights as done with conventional MPC algorithms. The algorithm proposed in this paper involves estimating MoD-based predictive models that are seamlessly integrated into 3DoF-KF MPC to

SUBMITTER: Banerjee S 

PROVIDER: S-EPMC12551675 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature