A Data-based Approach to Auto-Tuning PID Controller

C. Cheng and M.-S. Chiu (Singapore)


PID, JustinTime Learning, Nonlinear Control


In this paper, a data-based auto-tuning PID controller design is proposed for nonlinear process control. In the proposed method, a controller database is constructed to store the known PID parameters with their corresponding information vectors, while another database is employed for the standard use by Just-in-Time Learning (JITL) modeling techniques for modeling purpose. The PID parameters are automatically extracted from controller database according to the current process dynamics characterized by the information vector at every sampling instant. Moreover, the PID parameters can be further fine tuned, whenever necessary, and the resulting updated PID parameters with their corresponding information vector are stored into the controller database. The effectiveness of the proposed controller is evaluated by a control problem of polymerization reactor.

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