Self-Optimizing Control Strategy for Distributed Parameter Systems

Abstract

Real-time optimization (RTO) has been a popular approach for addressing optimal operation by repetitively optimizing online. However, this approach is impractical for distributed parameter systems (DPSs) due to its expensive computational cost. Self-optimizing control (SOC) has emerged as a promising alternative strategy, which achieves acceptable optimality by controlling the designed controlled variables (CVs) at constant set points without reoptimizing. Motivated by this, the existing lumped parameter SOC method is for the first time expanded to a typical class of DPSs based on a high-fidelity discrete-space model. Starting from an infinite-order system itself, the SOC problem is constructed as the minimal loss functional regarding the CVs through the operator operation and variational method. To obtain an operational analytical solution of CVs, the optimization problem is reformulated based on the discrete-space system derived by the finite difference. Considering the variety of sensor and actuator placements, the CVs are analytically designed with the minimal static loss through the stacking matrix approach. Finally, a case study of a plug-flow reactor is presented to demonstrate the feasibility of the proposed approach to achieve optimal operation for DPSs.