Fast predictive functional control based on auxiliary variable optimization

doi:10.1002/cjce.70455

Fast predictive functional control based on auxiliary variable optimization

2026-05-01

8

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AI

DOI：10.1002/cjce.70455

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For chemical processes characterized by large time delays and high inertia, traditional control strategies often suffer from high computational complexity. To address this, this paper proposes an auxiliary-variable-based fast predictive functional control (AV-FPFC) strategy. First, an extended non-minimal state space (ENMSS) model is constructed by incorporating input increments, output increments, and tracking errors, thereby avoiding the need for additional observers. On this basis, a predictive functional control (PFC) strategy is adopted to reduce computational burden. Second, auxiliary variables are designed to enhance the efficiency of solving constrained PFC problems. Specifically, the method first uses an analytical solution to detect constraint violations. If a violation occurs, the original optimization problem is transformed into a quadratic programming problem concerning the auxiliary variable. Finally, the proposed strategy is applied to a furnace control system characterized by large time delay and high inertia. Simulation results show that, under the same parameter settings, compared with ENMSS-PFC, the AV-FPFC strategy reduces the total computation time by 53.7%, while the steady-state error and overshoot are reduced by 71.1% and 69.1%, respectively. These results indicate that the proposed strategy can effectively alleviate the online computational burden of constrained time-delay systems while improving tracking performance.

Keyword:

auxiliary variable

constraints

ENMSS model

predictive functional control

quadratic programming

Fast predictive functional control based on auxiliary variable optimization

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