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Estimates of the one-step-ahead errors in control signals are calculated through a developed fuzzy neural network-based predictive model of the plant and used for controller tuning.
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Iterative Feedback Tuning (IFT) is a widely used procedure for controller tuning.
The high practical significance of the I1 Td-plant approximations has been shown by the perhaps most frequently used method for controller tuning by (Ziegler and Nichols, 1942), modified later for sampled data systems by (Takahashi et al., 1971) and by many other authors.
In such procedures, evolutionary multi-objective optimisation has been extensively used for PI and PID controller tuning; one reason for this is due to their flexibility to include mechanisms in order to enhance convergence and diversity.
The single degree of freedom for controller tuning was the ratio of the tracking error to the control move, which was set to 0.1 for all subjects.
In this study, a linear PI controller is designed using classical controller tuning methods for controlling the crystallizer outlet temperature by manipulating the inlet jacket temperature; however, the response is not satisfactory.
This model is used for the PID controller tuning and the performances of fixed and adaptive PID controllers are compared for load rejection and set point tracking.
In this work, a newly developed algorithm (Munro, The systematic design of PID controllers, IEE Colloquium in Robust Control: Theory, Software and Applications, Austin Court, Birmingham, 1999) is used for tuning the PID controller.
In the paper, this parameter is used for aperiodic tuning of PI controllers.
An internal model controller (IMC) based PID method is used for tuning the outer loop controller.
Genetic Algorithm (GA) is used for tuning the converter controllers and filter parameters.
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