自主式水下机器人控制系统及声呐目标识别

Chinese Journal of Ship Research - - Contents -

根据仿真结果,从航向变化图曲线来看,3种AUV控制器下的 实际航向角均能收敛至目标值FOPID附近,而遗传算法整定的 的超调量显著小2于其它 个控制器。因此,采用遗传算法整定的FOPID控制器得到的效果最好,其次为采用遗传IOPID,最后为试凑法整定的IOPID。算法整定的 4 3此外,本文还计算了 种仿真情况下 个控制器的上升时间、超调量、超调量百分比、稳态误差1和稳态误差百分比,相应结果在表 中列出。1 FOPID由表 可知,遗传算法整定的 控制器得到的航向曲线的超调量和稳态误差值均比遗传IOPID算法和试凑法整定 控制器的小,而上升时IOPID间几乎一致。虽然试凑法整定的 控制器得到的航向曲线具有稳态误差较小的特点,但其超调量较大。考虑到在航行器的控制上,超调量过大会直接导致控制性能变差甚至是失控,因此,超调量对控制性能的影响应当摆在首要考虑位置。

10°~40°的多组仿真实验中此外,以上结论在定向 FOPID均成立,说明遗传算法整定 在航向控制上IOPID普遍优于遗传算法和试凑法整定的 。综FOPID合以上分析,遗传算法整定的 具有更好的性能。

5结语

AUV本文针对 航向自动控制的问题,提出了λ μ一种基于遗传算法的分数阶 PI D 控制器的设计λ方案。仿真表明,基于遗传算法得到的分数阶PI Dμ控制器在航向控制过程中比基于遗传算法的整数PID PID阶 控制器和常规试凑法 控制器具有更好的动态性能。该方法可以对常规的航向自动舵进行改进,不仅减少了参数整定的工作量,还提高了控制器性能,具有较高的应用价值。

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图6 40°下的航向变化图定向Fig.6 Heading angle change at 40°

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