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EN
Aiming at the challenges to the accurate and stable heading control of underactuated unmanned surface vehicles arising from the nonlinear interference caused by the overlay and the interaction of multi interference, and also the uncertainties of model parameters, a heading control algorithm for an underactuated unmanned surface vehicle based on an improved backpropagation neural network is proposed. Based on applying optimization theory to realize that the underactuated unmanned surface vehicle tracks the desired yaw angle and maintains it, the improved momentum of weight is combined with an improved tracking differentiator to improve the robustness of the system and the dynamic property of the control. A hyperbolic tangent function is used to establish the nonlinear mappings an approximate method is adopted to summarize the general mathematical expressions, and the gradient descent method is applied to ensure the convergence. The simulation results show that the proposed algorithm has the advantages of strong robustness, strong anti-interference and high control accuracy. Compared with two commonly used heading control algorithms, the accuracy of the heading control in the complex environment of the proposed algorithm is improved by more than 50%.
EN
The process of heading control system design for a kind of micro-unmanned surface vessel (micro-USV) is addressed in this paper and a novel adaptive expert S-PID algorithm is proposed. First, a motion control system for the micro-USV is designed based on STM32-ARM and the PC monitoring system is developed based on Labwindows/CVI. Second, by combining the expert control technology, S plane and PID control algorithms, an adaptive expert S-PID control algorithm is proposed for heading control of the micro-USV. Third, based on SL micro-USV developed in this paper, a large number of pool experiments and lake experiments are carried out, to verify the effectiveness and reliability of the motion control system designed and the heading control algorithm proposed. A great amount of comparative experiment results shows the superiority of the proposed adaptive expert S-PID algorithm in terms of heading control of the SL micro-USV.
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