Compensation of significant parametric uncertainties using sliding mode online learning
Compensation of significant parametric uncertainties using sliding mode online learning
复制标题
使用滑模在线学习补偿显着参数不确定性
DOI：
10.1109/aero.2013.6497360
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Thomas Kruger
中科院分区：
文献类型：
--
作者：
P. Schnetter;Thomas Kruger
针对小型无人机系统（UAS）提出了一种基于滑模在线学习的增广非线性逆动力学（NID）飞行控制策略。由于这类飞机的参数识别在整个飞行包线内通常是无效的，用于基于模型的控制策略的空气动力参数可能会出现明显的偏差。对于反馈线性化的概念，这会导致反演误差，结合小型无人机对大气湍流的独特敏感性，对这些系统提出了苛刻的控制任务。在这项工作中，自适应飞行控制策略，采用前馈神经网络抵消这种非线性影响的滑模控制（SMC）的概念增加。SMC学习是从变结构理论中衍生出来的。它将神经网络及其训练视为控制问题。它表明，通过动态计算的学习率，稳定性可以得到保证，从而增加对外部干扰和系统故障的鲁棒性。由于收敛速度更快，可以补偿各种同时发生的干扰。SMC为基础的飞行控制器进行了测试和比较，标准的梯度下降（GD）反向传播算法的影响下，显着的模型不确定性和系统故障。
An augmented nonlinear inverse dynamics (NID) flight control strategy using sliding mode online learning for a small unmanned aircraft system (UAS) is presented. Because parameter identification for this class of aircraft often is not valid throughout the complete flight envelope, aerodynamic parameters used for model based control strategies may show significant deviations. For the concept of feedback linearization this leads to inversion errors that in combination with the distinctive susceptibility of small UAS towards atmospheric turbulence pose a demanding control task for these systems. In this work an adaptive flight control strategy using feedforward neural networks for counteracting such nonlinear effects is augmented with the concept of sliding mode control (SMC). SMC-learning is derived from variable structure theory. It considers a neural network and its training as a control problem. It is shown that by the dynamic calculation of the learning rates, stability can be guaranteed and thus increase the robustness against external disturbances and system failures. With the resulting higher speed of convergence a wide range of simultaneously occurring disturbances can be compensated. The SMC-based flight controller is tested and compared to the standard gradient descent (GD) backpropagation algorithm under the influence of significant model uncertainties and system failures.