Institute of Control, Robotics and Information Engineering, Poznan University of Technology, Piotrowo 3a, 60-965 Poznan, Poland.
Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Technicka 2, 166 27 Prague 6, Czech Republic.
Sensors (Basel). 2019 Jan 14;19(2):312. doi: 10.3390/s19020312.
The paper presents a novel autotuning approach for finding locally-best parameters of controllers on board of unmanned aerial vehicles (UAVs). The controller tuning is performed fully autonomously during flight on the basis of predefined ranges of controller parameters. Required controller properties may be simply interpreted by a cost function, which is involved in the optimization process. For example, the sum of absolute values of the tracking error samples or performance indices, including weighed functions of control signal samples, can be penalized to achieve very precise position control, if required. The proposed method relies on an optimization procedure using Fibonacci-search technique fitted into bootstrap sequences, enabling one to obtain a global minimizer for a unimodal cost function. The approach is characterized by low computational complexity and does not require any UAV dynamics model (just periodical measurements from basic onboard sensors) to obtain proper tuning of a controller. In addition to the theoretical background of the method, an experimental verification in real-world outdoor conditions is provided. The experiments have demonstrated a high robustness of the method to in-environment disturbances, such as wind, and its easy deployability.
本文提出了一种新颖的自动调谐方法，用于在无人机（UAV）上找到控制器的局部最佳参数。控制器调谐在飞行过程中完全自主进行，基于控制器参数的预定义范围。所需的控制器属性可以通过成本函数简单解释，该函数参与优化过程。例如，如果需要，可以对跟踪误差样本或性能指标的绝对值（包括控制信号样本的加权函数）进行惩罚，以实现非常精确的位置控制。所提出的方法依赖于使用 Fibonacci 搜索技术拟合到自举序列中的优化过程，从而能够获得单峰成本函数的全局最小值。该方法的特点是计算复杂度低，并且不需要任何无人机动力学模型（只需从基本的机载传感器进行周期性测量）即可实现控制器的适当调谐。除了该方法的理论背景外，还提供了在真实户外条件下的实验验证。实验表明，该方法对环境干扰（如风）具有很高的鲁棒性，并且易于部署。
