Abstract

The stability of the speed and pressure of the water flow is determined by the height and volume of the water. The speed of water flow in the actuator is determined by the use of this flow sensor system. A good tank-based water flow control model must be developed. At a certain point, the actuator stabilizes the water production rate per minute. Therefore, it is necessary to develop automatic and precise control techniques. Many Artificial Intelligence (AI) methods are used in system optimization. Among them are the Firefly Algorithm (FA) and Particle Swarm Optimization (PSO). In this research, conventional methods, Auto tuning methods, and PSO methods are used. The PSO method produces better optimization compared to the previous method. The water flow stability indicator in this simulation is shown by the size of the overshot and undershot values for each method. The best water level control simulation results are the PSO method with the smallest overshot value of 0.0333 pu, the smallest undershot value of 0.0347 pu, and the output flow results have the smallest overshot value of 0.0013 pu, the smallest undershot value of 0.0011 pu.