This concept provides simple analytic design relations for tuning PI controllers.
In this paper we present a new method for tuning PI controllers with symmetric send-on-delta (SSOD) sampling strategy.
Finally, the results of the SP-SPC are compared to optimally tuned PI controllers when subject to the same robustness constraints.
The behavior of the proposed ICA has been estimated with the behavior of the conventional ZN (Ziegler Nichols) and GA (Genetic Algorithm) in order to prove the superiority of the proposed ICA in tuning PI (Proportional plus Integral) controller.
The behavior of the proposed ACO has been estimated with the behavior of Genetic Algorithm (GA) in order to prove the superior efficiency of the proposed ACO in tuning PI controller over GA.
Moreover, it is shown that properly tuned diagonal PI controllers can provide performance and robustness properties which equal well-tuned PI controllers extended with decoupling.
In order to tune the PI gains, the control objective function is optimized using the genetic algorithm (GA).
A comparison of the performance of the IFT-based tuned PI-FC and the performance of the PI-FC tuned by an evolutionary-based optimization algorithm shows the performance improvement and advantages of our IFT approach to fuzzy control.
Application to the tuning of PI controllers is presented.
Moreover, a fuzzy gain scheduling assists in tuning the PI control parameters against uncertain disturbances.
