Comparative analysis of fuzzy regulators for controlling the movement of an autonomous underwater vehicle

Pham, Tuan M.; Shpektorov, Andrew G.

doi:10.1109/eiconrus.2018.8317247

Cited by 5 publications

(2 citation statements)

References 3 publications

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“…To design the scale factor selector used in the controller, the following comparative experiments are designed. The order of the controller in the comparative experiment is 1.1, and the scale factor is selected between (5,25). Except for the different scale factors, other parameters are the same among different controllers.…”

Section: Scale Factor Selectormentioning

confidence: 99%

“…Aiming at the problem that membership function is mostly designed by empirical method and there is no unified standard, an optimization algorithm of membership function is proposed in [4]. Pham et al introduced additional parameters in the process of the membership function optimization algorithm to bring the optimized controller more in line with the requirements of the UUV control system [5]. Tuan proposed a fuzzy controller for UUV depth control and developed related test software based on the Kalman filter and a fuzzy controller [6].…”

Section: Introductionmentioning

confidence: 99%

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A Variable-Order Fuzzy Logic Controller Design Method for an Unmanned Underwater Vehicle Based on NSGA-II

et al. 2022

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UUV depth control requires the controlled system to have good transient response and robustness under the premise of ensuring real-time performance. The flexibility of fractional-order control provides an idea to solve this problem. This paper proposes a controller design method for UUV depth control (VD-SIFLC) based on fractional calculus, fuzzy control, dynamic parameters and a fast non-dominated sorting genetic algorithm (NSGA-II). First, the overall structure of the controller, the UUV model and the model of external disturbances are presented. Then, the design methods of control input, order selector, membership function and scale factor selector are given, respectively. Then, the necessary conditions, such as optimization objectives and optimization parameters in the optimization algorithm, are analyzed. Finally, the effectiveness of the proposed control scheme is verified by comparative experiments with the SIFLC. Simulation results show that the controlled system with the VD-FIFLC could achieve better robustness and dynamic and steady-state performance. Moreover, according to the actual task requirements, the appropriate parameters can be selected by the user from the pareto solution set, which is suitable to be used in the actual applications.

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Section: Scale Factor Selectormentioning

confidence: 99%