水下滑翔机航向控制（英文）

发布时间：2019-03-24 20:17

【摘要】：水下滑翔机动力学特性随有效载荷及外形变化而变化,其航向控制富有挑战性。解决方法是使用在线系统辨识算法捕捉当前动力学特性,更新运动模型。为此,我们设计了一个在线多项式辨识器,不断更新当前动力学模型,同时用一个自适应模型预测控制器计算并输出最优化的控制指令。该控制器根据用户自定义的指标函数,使用二次规划方法得到最优控制指令。该指标函数由两项组成,一项用来表达轨迹跟踪性能,一项用来表达输入指令抑制性能。输入指令抑制性能一定程度上可以代表该控制器的能量消耗性能。设计师可以通过调节这两项的权重,平衡两个控制器的性能。比较二次与五次多项式模型的控制效果,发现:二次多项式模型不足以表达无人机的动力学特性,且控制结果易发生剧烈波动。硬件在环模拟以及湖试结果验证了控制器性能。
[Abstract]:The mechanical characteristics of underwater gliding maneuver vary with the change of payload and shape, and its course control is challenging. The solution is to use the on-line system identification algorithm to capture the current dynamic characteristics and update the motion model. To this end, we design an on-line polynomial identifier to update the current dynamic model constantly, and use an adaptive model predictive controller to calculate and output optimal control instructions. According to the user-defined index function, the optimal control instructions are obtained by quadratic programming method. The index function is composed of two terms, one is used to express the trajectory tracking performance, the other is used to express the input instruction suppression performance. The input instruction suppression performance can represent the energy consumption performance of the controller to some extent. Designers can balance the performance of both controllers by adjusting the weights of the two items. Comparing the control effect of the quadratic polynomial model with the quintic polynomial model, it is found that the quadratic polynomial model is not enough to express the dynamic characteristics of the UAV, and the control results are prone to violent fluctuations. The results of hardware-in-loop simulation and lake test verify the performance of the controller.
【作者单位】： School
【基金】：supported by Beihang University and Institution of China Academy of Aerospace Aerodynamics
【分类号】：U664.82

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