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Presented is an easy to use and broadly applicable method for tuning PID controllers for integrating processes. Details are presented on the requirements for collecting closed loop dynamic process test data near the design level of operation, the fitting of an integrating dynamic model form to this test data and correlations for computing controller tuning values based on the parameters from the resulting model fit.
The method presented is applicable to PID control algorithms in both the interacting and non-interacting derivative forms. The work builds on the work of Chien and Fruehauf  and their use of the internal model control (IMC) structure to derive tuning correlations for integrating processes. One novel contribution of this work is the extension of the tuning correlations to include the PID with derivative filter forms.
The design and tuning method is demonstrated on process simulations for both set point tracking and disturbance rejection. Results show that the methods described here compare favorably with other more computationally intensive approaches.
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