AI Summary & Key Takeaways
AI InsightThe obtained results show that the CS optimized approach based on the proposed objective cost function outperforms than that of the integral based objective functions with higher efficiency and better quality no matter whether the process systems are employed under unload or load conditions.
📌 3 Key Takeaways
Abstract
A time domain performance criterion based on the multi-objective Pareto front solutions is proposed to tune the Proportional-Integral-Derivative (PID) controller parameters with the Cuckoo Search (CS) algorithm for different process systems: first order plus dead time (FOPDT) and high order dynamics. The proposed multi-objective cost function consists of conflicting objective functions including the overshoot, rise time, settling time and steady state error. In this paper, multi-objective genetic algorithm (MOGA) is used for obtaining the Pareto optimal solutions of the conflicting objective functions. The weights in the proposed multi-objective cost function are calculated by way of nondominated solutions of the obtained Pareto fronts based on the four conflicting objective functions. Also, the optimal tuning parameters of the PID controller are obtained by minimizing the integral based objective functions commonly introduced in the literature using the CS algorithm. The obtained results show that the CS optimized approach based on the proposed objective cost function outperforms than that of the integral based objective functions with higher efficiency and better quality no matter whether the process systems are employed under unload or load conditions.