SwarmPID: Enhancing Amphibious Robot Control through Partial Swarm Optimization

Abstract

In the realm of control systems, PID controllers stand out as the go-to choice for their widespread application and user-friendly nature. Achieving optimal performance for complex systems, such as an amphibious robot, hinges greatly on setting the PID controller parameters just right. Traditionally, this has been a painstaking task, often involving manual tuning methods aided by software calculators. However, a promising alternative emerges in the form of particle swarm optimization (PSO). In our study, we employ PSO to efficiently determine the optimal PID controller parameters. This method offers a streamlined approach, leveraging the collective intelligence of particles to navigate the parameter space and converge upon the most effective settings. To evaluate the efficacy of our proposed approach, we meticulously compare its performance against other tuning methods, including pole placement and LQR. Through rigorous testing, we assess the composite control system's behavior under various error conditions and time responses. By embracing PSO for PID parameter tuning, we aim to streamline the optimization process, enhance control system performance, and pave the way for more efficient and reliable operation of complex systems like the amphibious robot.