考虑舵机故障的船舶鲁棒自适应航向保持控制

Robust adaptive course-keeping control of under-actuated ships with the rudder failure

    摘要
  • 摘要:
      目的  针对舵机故障、控制增益未知和海洋环境干扰情况下的欠驱动船舶航向保持问题，设计一种考虑舵机故障的船舶鲁棒自适应航向保持控制算法。
      方法  通过结合鲁棒神经阻尼技术和自适应方法，对繁重的神经网络权值进行横向压缩，仅需设计2个自适应学习参数对未知增益和舵机故障参数在线补偿，以确保船舶在舵机故障的情况下能够有效执行航向保持任务。通过李雅普诺夫理论，证明所提出的控制器半全局最终一致稳定有界(semi-global uniform and ultimately bounded, SGUUB)。最后以“育鲲”轮为仿真对象，建立非线性Nomoto数学模型，在海洋干扰下进行对比仿真试验验证。
      结果  结果表明，在此策略下，“育鲲”轮在舵机故障情况下平均舵角输出比仿真试验中所对比的传统方法降低了51%，可改善航向保持控制效果。
      结论  研究结果可为欠驱动船舶的航向保持控制问题提供借鉴。

     

    Abstract:
      Objective  A robust adaptive course-keeping control algorithm is designed to deal with the course-keeping problem for under-actuated ships with rudder faults, gain uncertainty and marine disturbances.
      Methods  By combining the robust neural damping technique and adaptive approach, numerous neural network (NN) weights can be compressed horizontally, and only two gain-related adaptive learning parameters need to be designed to compensate for both the gain uncertainty and unknown fault parameters. The proposed controller is proven to be semi-global uniform and ultimately bounded (SGUUB) through Lyapunov analysis. Finally, the Nomoto mathematical model is established using "Yukun", and the effectiveness and superiority of the course-keeping algorithm is illustrated by carrying out comparison experiments under marine interference conditions.
      Results  The results show that the average rudder angle of "Yukun" under rudder failure is reduced by 51%, significantly improving control performance.
      Conclusion  The results of this study can provide references for tackling the course-keeping control problem of under-actuated ships.

     

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