PFC based PID design using genetic algorithm for chamber pressure in a coke furnace

Abstract This paper proposes a genetic algorithm (GA) optimization based PID controller design using non-minimal state space model. This strategy is inspired by the lack of analytical tuning guideline of weighting matrices for state space model based controller design. The strategy consists of two steps. First, a PID controller is designed using a non-minimal state space model through predictive functional control optimization. Then the weighting matrices in the controller design are optimized through GA to achieve the desired closed-loop control performance. The performance of the proposed method is tested on the chamber pressure of an industrial coke furnace and compared with the recent extended non-minimal state space predictive functional control (ENMSSPFC) based PID controller, where results demonstrate that the proposed PID shows better performance than ENMSSPFC based PID control.

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