孙秀军, 陈重喆, 周莹. 波浪滑翔器航向控制方法与实验研究. 力学与实践, 2024, 46(3): 1-12. DOI: 10.6052/1000-0879-23-280
引用本文: 孙秀军, 陈重喆, 周莹. 波浪滑翔器航向控制方法与实验研究. 力学与实践, 2024, 46(3): 1-12. DOI: 10.6052/1000-0879-23-280
Sun Xiujun, Chen Zhongzhe, Zhou Ying. Research on the heading control method and experiment of wave glider. Mechanics in Engineering, 2024, 46(3): 1-12. DOI: 10.6052/1000-0879-23-280
Citation: Sun Xiujun, Chen Zhongzhe, Zhou Ying. Research on the heading control method and experiment of wave glider. Mechanics in Engineering, 2024, 46(3): 1-12. DOI: 10.6052/1000-0879-23-280

波浪滑翔器航向控制方法与实验研究

RESEARCH ON THE HEADING CONTROL METHOD AND EXPERIMENT OF WAVE GLIDER

  • 摘要: 波浪滑翔器是一种典型的非线性、强耦合、欠驱动系统。传统比例-积分-微分(proportion integral derivative, PID)控制器在复杂多变的海洋环境下难以满足高精度的航向控制要求且存在参数整定困难、无法在线调整等缺点。针对此问题提出一种基于改进粒子群优化(improved particle swarm optimization, IPSO)算法的反向传播(back propagation, BP)神经网络PID控制方法,首先建立波浪滑翔器数学模型,其次利用BP神经网络的自学习能力自适应调整PID参数。同时针对BP神经网络存在对初始权值敏感、反向传播易陷入局部极值等缺点,引入IPSO算法对网络初始权值进行优化,确保BP-PID网络能够获取全局最优解。基于仿真进行海试验证,结果表明所提算法能够显著提高航向控制性能,验证了所提算法的有效性和真实性。

     

    Abstract: The wave glider is a typical nonlinear, strongly coupled, underdriven system. Conventional proportional integral derivative (PID) control algorithm, which has the disadvantages such as difficulty in parameter adjustment and inability to adjust online, hardly meets the requirements of high-precision heading control in complex and time-varying marine environment. In this paper, a back propagation (BP) neural network incremental PID control method based on the improved particle swarm optimization (IPSO) optimization is proposed for this problem. Firstly, a mathematical model for the wave glider is developed. Secondly, the self-learning ability of BP neural network is utilized to adaptively adjust the PID parameters. Meanwhile, to overcome the shortcomings of BP neural network such as high sensitivity to initial weights and easyness to fall into local extremes by back propagation, an IPSO algorithm is introduced to optimally select the initial weights of the network, which can ensure the BP-PID network to obtain the global optimal solution. Finally, simulations and sea trials are conducted and the results show that the proposed algorithm is feasible, and can significantly improve the heading control performance of the wave glider, which verifies the effectiveness and realism of the algorithm.

     

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