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