<![CDATA[Single-phase induction motors play an important role in industrial and household applications due to their ability to drive equipment efficiently. PID (Proportional-Integral-Derivative) control is often used to regulate the speed of these induction motors, but determining optimal PID parameters can be a challenge. The Particle Swarm Optimization (PSO) algorithm is proven to be effective in optimizing PID parameters thereby increasing system adaptability and performance. This research combines PID control with the PSO algorithm to create a more intelligent and responsive single-phase induction motor speed control system. Scilab is used as a simulation platform to implement and test this control model. The research results show that combining PSO with PID control is able to reduce oscillations, improve system response, and increase energy efficiency where the initial parameter values are (Kp = 1, Ki = 2, Kd = 3) resulting in new parameter values. after going through an optimization process using the PSO algorithm (Kp = 0.20 Ki = 1.40 and Kd = 2.00). With this optimization, single-phase induction motors can operate more efficiently and reliably in dealing with various operational conditions.]]>
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