Model free adaptive path following control based on active disturbance rejection theory for AUV
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摘要: 面向自主水下航行器(AUV)精准回收的任务需求, 针对AUV运动中模型不确定性、易受环境干扰导致的路径跟踪精度不足的问题, 从无模型控制的角度出发, 提出了一种适用于AUV的基于自抗扰理论的无模型自适应控制(ADRC-MFAC)算法。该算法针对2阶系统模型特性, 结合视线角制导重新设计控制输入准则函数对无模型自适应控制(MFAC)进行了改进, 解决了MFAC只适用于自衡系统的问题。引入跟踪微分器对期望信号进行指令平滑, 考虑未知复合干扰的影响设计了线性扩张状态观测器, 并在控制器中对估计扰动进行补偿, 并证明了所提控制器的稳定性, 提升了系统鲁棒性。在同样的干扰情况下, 文中控制方案相比传统比例-积分-微分控制器(PID)抗干扰能力提升了42.37%, 控制精度提高了45%, 表明ADRC-MFAC能够明显改善AUV的抗干扰性能, 提高路径跟踪精度。Abstract: Aiming at the task requirements of accurate recovery of autonomous underwater vehicles (AUVs), a model-free adaptive path tracking control based on active disturbance rejection theory (ADRC-MFAC) is proposed from the perspective of modelless control in view of the insufficient path tracking accuracy caused by model uncertainty and vulnerability to environmental interference in AUV motion. According to the characteristics of second-order model system and line-of-sight guidance, the algorithm redesigned the control input criterion function to improve model free adaptive control(IMFAC),solved the problem that MFAC is applicable to self-balancing system. Introduced tracking dif-ferentiator to smooth the desired signal and designed linear extended state observer considering the influence of unknown compound interference, compensated the estimated disturbance in the controller, improved the stability of the control system and improved robustness. With the same of disturbance, the proposed control scheme can improve 42.37% of robustness and 45% of accuracy with PID. The result shows that ADRC-MFAC can significantly improve the anti-interference performance of AUV, and improve the path tracking accuracy.
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图 5 多航点路径跟踪偏距和航向角误差对比曲线
Figure 5. Comparison between tracking errors and heading angle errors of multiple waypoints path following
图 6 多航点路径跟踪实际值和估计值对比曲线
Figure 6. Comparison between true and estimation values of multiple waypoints path following
图 9 圆路径跟踪偏距和航向角误差曲线
Figure 9. Comparison between tracking errors and heading angle errors of circle path following
图 10 圆路径跟踪实际值和估计值曲线
Figure 10. Comparison between true and estimation values of circle path following
表 1 AUV模型参数列表
Table 1. Model Parameters of AUV
名称 参数 名称 参数 m/kg 30.48 $ {I_z} $/(kg m2) 3.45 $ {X_u} $/(Ns/m) −13.5 $ {N_{\dot r}} $/(kg m2/rad) −4.88 $ {Y_v} $/(Ns/m) −66.6 $ {X_{u|u|}} $/(Ns2/m2) −1.62 $ {N_r} $/(Ns/rad) −5.98 $ {Y_{v|v|}} $/(Ns2/m2) −131 $ {X_{\dot u}} $/kg −0.93 $ {N_{r|r|}} $/(Ns2/rad2) −94 $ {Y_{\dot v}} $/kg −35.5 — — 
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表 2 多航点路径跟踪性能指标
Table 2. Performance index of multiple waypoints path following
指标 PID IMFAC ADRC-MFAC 航向超调量/(°) 4.58 10.47 0.61 航向调节时间/s 11.96 22.08 2.51 航向稳态误差/(°) 0 0.07 0 偏距平均误差/m 0.350 0.420 0.32 偏距稳态误差/m 0.001 0.001 0
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表 3 圆路径跟踪性能指标
Table 3. Performance index of circle path following
指标 PID IMFAC ADRC-MFAC 航向超调量/(°) 20.13 27.87 11.62 航向调节时间/s 44.63 43.87 30.69 航向稳态误差/(°) 3.11 3.21 1.71 偏距平均误差/m 0.42 0.47 0.35 偏距稳态误差/m 0.17 0.23 0.13
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