Meta-Heuristic Algorithms for Autonomous Underwater Vehicle Steering in the Presence of Parameter Uncertainty and Nonlinear Inputs

Abstract

Autonomous underwater vehicles (AUVs) have several uses because of their propensity for lengthy journeys, excellent camouflage, high degree of intelligence, and capacity to take the place of people in hazardous situations. Motion control systems for AUV s have been the focus of a lot of research because they can keep the vehicles stable in the rough ocean environment. In this study, we suggest an AUV motion control system that uses a fractional-order proportional integer derivative controller (FOPID). It is an expansion of the traditional PID controller, and by increasing two tuning parameters, it improves system control performance. The controller design and calculation processes can be significantly streamlined using the Firefly algorithm. The fractional-order PID controller is being compared with the internal model control-based PID (IMC-PID) controller. Last but not least, comparative numerical simulations of AUV motion control were carried out to confirm the superiority of the suggested control algorithm. The simulation results demonstrate the suggested control algorithm's enhanced stability and transient performance.