Wang Binglin, Duan Xiaojun, Yan Liang, Deng Juan, Chen Jiangtao
College of Liberal Arts and Sciences, National University of Defense Technology, Changsha 410073, China.
China Aerodynamics Research and Development Center, Mianyang 621000, China.
Entropy (Basel). 2020 May 6;22(5):527. doi: 10.3390/e22050527.
The leader-follower structure is widely used in unmanned aerial vehicle formation. This paper adopts the proportional-integral-derivative (PID) and the linear quadratic regulator controllers to construct the leader-follower formation. Tuning the PID controllers is generally empirical; hence, various surrogate models have been introduced to identify more refined parameters with relatively lower cost. However, the construction of surrogate models faces the problem that the singular points may affect the accuracy, such that the global surrogate models may be invalid. Thus, to tune controllers quickly and accurately, the regional surrogate model technique (RSMT), based on analyzing the regional information entropy, is proposed. The proposed RSMT cooperates only with the successful samples to mitigate the effect of singular points along with a classifier screening failed samples. Implementing the RSMT with various kinds of surrogate models, this study evaluates the Pareto fronts of the original simulation model and the RSMT to compare their effectiveness. The results show that the RSMT can accurately reconstruct the simulation model. Compared with the global surrogate models, the RSMT reduces the run time of tuning PID controllers by one order of magnitude, and it improves the accuracy of surrogate models by dozens of orders of magnitude.
领导者-跟随者结构在无人机编队中被广泛应用。本文采用比例-积分-微分（PID）控制器和线性二次调节器来构建领导者-跟随者编队。通常，PID控制器的调谐是凭经验进行的；因此，人们引入了各种代理模型，以便用相对较低的成本识别更精确的参数。然而，代理模型的构建面临奇异点可能影响精度的问题，从而导致全局代理模型可能无效。因此，为了快速准确地调谐控制器，提出了基于分析区域信息熵的区域代理模型技术（RSMT）。所提出的RSMT仅与成功样本协作，以减轻奇异点的影响，同时利用分类器筛选失败样本。通过使用各种代理模型实现RSMT，本研究评估了原始仿真模型和RSMT的帕累托前沿，以比较它们的有效性。结果表明，RSMT能够准确地重构仿真模型。与全局代理模型相比，RSMT将调谐PID控制器的运行时间减少了一个数量级，并将代理模型的精度提高了几十个数量级。
