This study introduces a Constrained Predictive Functional Controller (CPFC) designed for regulating chamber pressure in a coke furnace. While the traditional controllers face challenges posed by the system’s complex multi-input multi-output (MIMO) structure, cross-coupling, time delays, physical constraints, and uncertainties, this article’s approach realized set-point tracking and disturbance rejection, addressing model mismatches while considering constraints on system inputs, input variations, and outputs. To tackle the constrained Model Predictive Control (MPC) optimization problem and minimize the cost function, the Quantum Simultaneous Whale Optimization Algorithm (QSWOA) is employed. This meta-heuristic optimization method integrates quantum coding and simultaneous search with the whale optimization algorithm, enhancing convergence speed and precision. Furthermore, the performance of the proposed CPFC-QSWOA controller is assessed through simulations of the chamber pressure control of a Coke furnace. Results demonstrate the superiority of the CPFC-QSWOA approach, showcasing high precision and robustness, particularly in the face of uncertainties and disturbances.
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