Robust Tuning And Sensitivity Analysis Of Stochastic Integer And Fractional-Order Pid Control Systems: Application Of Surrogate-Based Robust Simulation-Optimization

This paper aims to make a trade-off between performance and robustness in stochastic control systems with probabilistic uncertainties. For this purpose, we develop a surrogate-based robust simulation-optimization approach for robust tuning and analyzing the sensitivity of stochastic controllers. Kriging surrogate is combined with robust design optimization to construct a robust simulation-optimization model in the class of dual response surfaces. Randomness in simulation experiments due to uncertainty is analyzed through bootstrapping technique by computing confidence regions for the estimation of Pareto frontier. Results confirmed a proper trade-off between the model's performance with the measure of expected Integral Squared Error (ISE) and robustness against uncertainty in the plant's physical parameters. Finally, the proposed method is evaluated in terms of accuracy, computational cost, and simplicity particularly in comparison with some common existed techniques in the tuning of the Proportional-Integral-Derivative (PID) and Fractional-Order PID (FOPID) controllers.