Optimization of PI-Cascaded Controller’s Parameters for Linear Servo Mechanism: A Comparative Study of Multiple Algorithms

In numerous industries, especially in automation and industrial processes, the linear servo mechanism is used. However, parameters of friction backlash models are frequently unknown for servomechanism systems, resulting system uncertainty. High steady-state inaccuracy caused by friction, whereas undesired vibration blowback. an issue that still not sufficiently addressed a realistic model. To address these challenges, this research on controlled proportional-integral (PI-Cascaded) controller, which enables systems to respond more rapidly, reduce or reject disturbance, arrive at steady state quickly. Moreover, controller’s crucial getting best performance from particular controller. As result, controller settings were adjusted using four different meta-heuristic optimization algorithms: Surrogate Based Optimization (SBO), Hybrid Genetic Pattern Search Algorithm (HGSPA), Particle Swarm (PSO), Simulated Annealing (SA) with objective functions: Integral Square Error (ISE), Absolute (IAE), Time (ITSE), (ITAE). Throughout system’s experimental testing, 50 cm was employed as reference input. Negligible overshoot, quick rise settling times, excellent responsiveness all characteristics PSO algorithm ITSE function. assess robustness. A N force applied system, sine wave signal input into system. The shows remarkable stability resilience throughout load experimentation test.