Abstract
The torpedo is a nonlinear object which is very difficult to control. Via to manage the rudder angle yaw, the diving plane angle, and the fin shake reduction, the torpedo yaw horizontal, the depth vertical and roll damping of the system are controlled accurately and steadily. In this paper, the particle swarm optimization is used to correct the imprecision of architecture fuzzy parameters. The coverage width of membership function and the overlap degree influence of neighboring membership functions are considered in the method to adjust dynamically from the system errors. Thereby optimizing the control signal and enhancing the torpedo motion quality. The proposed method results in a better performance compared to the other control method such as adaptive fuzzy-neural that proved effective of the proposed controller.
Highlights
The torpedo motion is a nonlinear and complicated in practical applications
In order to discover the optimal parameters for fuzzy controller, each variable of the membership function for Ke(t) and Kde/dt control input is attributed to a particle
Fuzzy Controller Design based on Particle Swarm Optimization We consider the fuzzy modulator which has a double-input, ex(t), dex(t)/d(t) and a singe-output, u(t)
Summary
The torpedo motion is a nonlinear and complicated in practical applications. The torpedo actuator system executes control commands to ensure that object sponges the reference trajectory. A Faruq et al (2011) present the fuzzy algorithm combined with the output gain optimization algorithm to improve the response quality as well as the weight update time of the controller [1]. The authors have been proposed fuzzy particle swarm optimization (FPSO) advantage control technical for torpedo motion optimization. Particle swarm optimization (PSO) is adopted to calibrate the structure of fuzzy controller, thereby enhancing the quality of system and optimizing the controller structure for torpedo motion.
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