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This paper describes the dynamic modeling, structural analysis, implementation and experimental test of a Manta-type Unmanned Underwater Vehicle (MUUV). Various controllers such as PID, Sliding mode, and Fuzzy and H_∞ controllers are designed for depth and heading control in order to compare the performance of each controller based on simulation. In addition, experimental tests are carried out in a towing tank for depth keeping and heading angle tracking.

This paper describes the mathematical modeling and control algorithms of an unmanned underwater vehicle (UUV) named Minekiller. This UUV has two longitudinal thrusters, one vertical thruster, and an internal mass moving system, which can control the pitch rate. The UUV is equipped with a movable mass for pitch control. It is different from other common UUVs, in that it can maintain a static pitch angle. The UUV's 6-DOF (Degrees of Freedom) dynamics model is derived from the hydrodynamic forces and moments acting on it. We applied these hydrodynamic coefficients to dynamic modeling for numerical simulations by MATLAB/SIMULINK^©. To compare the performance in various cases, we used a PID controller for depth and heading control. Also, the navigation controller can analyze the way-point tracking performance. These simulation results show the performance of the control algorithms and maneuvering performance of the underwater vehicle.

Fuzzy Logic Controllers (FLCs) are intelligent control methods, where membership functions and corresponding rules are defined to get a proper control signal. The parameters were defined for these controllers, and they are named as PID-like FLC since the input and output parameters are connected to the Fuzzy controller with integral and derivative action of the error signal to change the behavior/performance of FLC. In this research, three different rule sets for Fuzzy controllers; 3 × 3, 5 × 5, and 7 × 7 are used and parameters are optimized with; differential evolution, genetic algorithm, particle swarm optimization and quantum-behaved particle swarm optimization. In addition to these controllers, a novel algorithm named as improved quantum particle swarm optimization is proposed as a part of this research. The simulation and real-life implementation on the experimental set results of these controllers are discussed in this paper.

We investigate the sampled-data redesign problem for nonlinear control affine multi-input systems and consider sampled-data feedback laws for which the trajectories of the sampled-data closed loop system converge to the continuous time trajectories with a prescribed rate of convergence as sampling time vanishes. We analyze geometric conditions for the existence of such sampled-data feedback laws and give formulae and algorithms for their computation.