ǤǤ Ȁ Ȁ
G P ( S ) G st ( S )
Optimization Techniques
Jellyfish swarm optimization 0.8 Elephant herding optimization 1.123
1.053 1.781
Moth flame optimization Ant lion optimization
3.532 3.819
2.61 2.98
Table 2 . Performance comparison of complementary sensitivity function with optimal methods.
Parameters
PID PI
FOPID
IAE ISE
0.8397 0.8156 0.9076 0.1743 0.1735 0.1772 0.9027 0.7681 0.9382 0.03 0.055 0.011
ITAE
Rise time (sec)
Overshoot time (sec) 0.8
0.46 -
Settling time (sec)
1.01 4.02 0.5
Table 3 . Analysis parameters of the various controllers.
could further enhance the controller’s performance. Additionally, implementing the JSO-FOPID controller in real-time scenarios would provide valuable insights into its practical viability and resilience under real-world disturbances and uncertainties. Developing adaptive versions of the JSO-FOPID controller to handle evolving system dynamics in real-time could also be a promising area of exploration. These recommendations aim to build upon the current findings, ensuring that the JSO-FOPID controller continues to evolve as a cutting-edge solution for complex control systems.
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(2025) 15:1631
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