This paper deals with the design of new self-tuning Fuzzy Fractional Order PID (AFFOPID) controller based on nonlinear MIMO structure for an AUV in order to enhance the performance in both transient state and steady state of traditional PID controller. It is particularly advantageous when the effects of highly nonlinear processes, like high maneuver, parameters variation, have to be controlled in presence of sensor noises and wave disturbances. Aspects of AUV controlling are crucial because of Complexity and highly coupled dynamics, time variety and difficulty in hydrodynamic modeling. In this try, the comprehensive nonlinear model of AUV is derived through kinematics and dynamic equations. The scaling factor of the proposed AFFOPID Controller is adjusted online at different underwater conditions. Combination of adaptive fuzzy methods and PID controllers can enhance solving the uncertainty challenge in the PID parameters and AUV parameter uncertainty. The simulation results show that developed control system is stable, competent and efficient enough to control the AUV in path following with stabilized and controlled speed. Obtained results demonstrate that the proposed controller has good performance and significant robust stability in comparison to traditional tuned PID controllers.
This article introduces a new technique for stabilizing autonomous underwater vehicles in the descriptor model. Autonomous underwater vehicle stabilization is limited by several constraints, including time-variable uncertainty, time delay, and disturbances. The interaction of descriptor system requirements and the issues mentioned above adds complexities. This study outlines the delay-dependent [Formula: see text] robust stability achieved through memory-less and memory state feedback. Furthermore, a less conservative sufficient condition is obtained for admissibility using a new delay-dependent linear matrices inequality. Autonomous underwater vehicle is regular, impulse-free, and stable under all permissible conditions and satisfies prescribed [Formula: see text] performance conditions via a Lyapunov functional approach. The method applies to neutral and retarded dynamics with discrete and distributed time delays.
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