Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) for optimizing PID parameters on Autonomous Underwater Vehicle (AUV) control system
Teguh Herlambang, Dinita Rahmalia, Tony Yulianto · Journal of Physics Conference Series · 2019
Indonesia consists of seventy percents of sea such that Indonesia has much marine resource. For exploring marine resource, it is required Autonomous Underwater Vehicle (AUV) with its control. In AUV, there are surge, sway, heave position and roll, pitch, yaw angle which have to be controlled. PID (Proportional-Integral-Derivative) control has been developed in many control system problems. In previous research, the tuning of PID parameters such as Kp, Ki, and Kd has been applied by Ziegler-Nichols technique. In this research, the optimization of PID parameters will be approached by heuristic methods such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). PSO is inspired by the flock of birds or fishes in food search while ACO is inspired by the cooperative behavior of ant colonies, to find the shortest path from their nest to the food source. Either particle in PSO or path consisting pheromone in ACO represents PID parameters and the fitness function is integral of absolute error (IAE). Based on simulations, heuristic methods can result responses with small overshoot and fast rise time and settling time.