基于遗传算法和分数阶技术的水下机器人航向控制

Heading control of AUV based on GA and fractional order technology

    摘要
  • 摘要:
      目的  自主式水下机器人（AUV）在海洋资源勘探、水下设备检修、水下搜救等领域发挥着重要作用，是探索海洋、开发海洋资源的重要工具。AUV的航向控制是其完成水下作业任务的基础。目前国内工程上多使用常规整数阶PID（比例、积分、微分）控制器进行航向控制，但该方法存在鲁棒性较差和参数整定复杂的问题。
      方法  针对以上常规航向控制方法的不足，提出一种基于分数阶PID技术的航向控制器，并结合遗传算法完成控制参数自动整定，以提高控制器的实用性。分别对试凑法整定整数阶PID参数、基于遗传算法整定整数阶和分数阶PID参数的3种航向控制器进行算法仿真对比。
      结果  结果表明：基于遗传算法整定分数阶PID参数的航向控制器相较于其他2个控制器，在上升时间与稳态误差基本相当的情况下超调量显著减小。
      结论  说明基于遗传算法整定参数的分数阶水下机器人航向控制算法有效并具有优越性。

     

    Abstract:
      Objectives  Autonomous Underwater Vehicle (AUV) is an important tool for ocean exploration and exploitation, which plays an important role in ocean resources exploration, underwater equipment maintenance, underwater search and rescue, etc. Heading control of AUV is the fundamental function to accomplish underwater mission. At present, conventional Integer-Order PID (IOPID) controller is widely used for heading control in domestic engineering. However, its robustness is poor and parameter setting is complex.
      Methods  Due to the shortcomings mentioned above, a heading controller based on Fractional-Order PID(FOPID)technology is proposed, and Genetic Algorithm(GA)is adopted to automatically tune the control gains and enable the practical implementation of the controller. The numerical simulations for PID, GA based IOPID and GA based FOPID are compared respectively by trial-and-error method.
      Results  The results showed that, compared with the other two controllers, the GA based FOPID heading controller has a significantly reduced overshoot when the rise time and the steady-state error are basically equal,
      Conclusions  which indicates that the AUV applying the GA based FOPID is more effective and advantageous.

     

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