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摘要: 为了提高船舶航行控制质量, 建立了船舶航迹复合预测控制模型, 依据灰色预测模型处理船舶运动控制的不确定量, 利用反传多层感知器自适应网络从船舶航行偏差的历史数据中得出控制偏差趋势, 根据灰色预测和神经网络预测的误差大小, 进行组合模型优选及组合权系数优化, 确定航迹最优控制策略。仿真结果表明: 当船舶旋回性指数、船舶追随性指数与滞后时间其中一个大于1时, 任何参数的改变均会引起PID振荡, 而船舶航迹复合预测控制模型能以较少的操舵动作迅速收敛, 从而使船舶航迹与预定航线更加拟合, 因此, 其控制系统的鲁棒性、快速性和稳定性高。Abstract: In order to improve the control quality of ship's track, a hybrid prediction control model was established.The uncertain variables of ship's dynamics were estimated by using grey predicting technique, while back propagation adaptive neural network with multilayer perceptrons was used to analyze the trend of control error development for ship's track based on studying ship's response errors, the optimal combination and optimal weight coefficients of the model were ascertained according to the difference of the above two results, so that the optimum control strategy was found.Simulation result shows that the oscillation of ship PID control system will occurs when one index value of demurrage, turning ability and following ability is more than 1, while the model enables ship's track errors to converge quickly with less rudder movement, so that ship's planning track accords with ship's real track, hereby, the robustness, rapidness and stability of the model are higher.
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图 3 MLP & GM在K′=1, T′=1, τ=1 s时的仿真结果
Figure 3. Simulation result of MLP & GM when K′=1, T′=1 and τ=1 s
图 4 PID在K′=1, T′=1, τ=1 s时的仿真结果
Figure 4. Simulation result of PID when K′=1, T′=1 and τ=1 s
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