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

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 generate control actions that vary with operating conditions. This results in notable performance enhancements in the context of both SISO and MIMO control compared to conventional ARX models. Performance and robustness of the 3DoF-KF MoD MPC framework are demonstrated in this paper through two case studies involving (i) epidemic control of a variant of the widely used SISO Susceptible-Infected-Removed (SIR) model and (ii) a nonlinear, highly interactive MIMO Continuous Stirred Tank Reactor (CSTR) model. The second case study further provides guidelines for designing informative databases for effective MoD-based MIMO identification and implementing 3DoF-KF MPC-based control for a demanding class of systems. Overall, this paper demonstrates technological and practical improvements in system identification and control of nonlinear SISO and MIMO systems through the synergistic integration of MoD estimation and 3DoF-KF MPC, providing an effective approach for operating complex nonlinear process systems.