Relative analysis of controller effectiveness for vertical plane control of an autonomous underwater vehicle

Abstract

Control and guidance of Autonomous Underwater Vehicle (AUV) is tedious process as it is an underactuated model where the numbers of inputs to the actuator are less than the number of degrees-of-freedom to be controlled; also the dynamics of AUV is affected by internal interruptions like sensor noise and exterior distractions namely hydrodynamic effects and ocean current. The difficulty of uncertainty in the vehicle dynamics can be handled by adaptive control, chosen based on its clarity using conventional controller like Proportional-Integral-Derivative (PID) and also by intelligent Fuzzy Logic Control (FLC). To increase the quality of these autonomous systems, model-based control strategies such as Internal Model Control (IMC) and Model Predictive Control (MPC) has been focused in this paper. The control operation includes motion planning and trajectory generation tasks which provide the desired vehicle depth location as a function of time and subsequently the controller determines required stern angle position, based on the sensor measurements. The comparative result shows that Model based control strategies exhibits better performance than the classical controls.