论文标题
具有模型不确定性的己型的基于学习的耐故障控制
Learning-Based Fault-Tolerant Control for an Hexarotor with Model Uncertainty
论文作者
论文摘要
在本文中，我们为基于高斯流程（GP）提供了基于学习的跟踪控制器，用于恢复操作中的耐断层己型。特别是，要估计出现在己旋车辆中的某些不确定性，具有重新配置转子以补偿故障的能力。转子重新配置引入了使车辆动态模型与名义模型不同的干扰。该控制算法旨在通过使用GP作为基于学习的预测模型来学习和补偿控制分配重新配置失败后的建模不确定性的数量。特别是，所提出的方法可以保证具有高概率的概率有限跟踪误差。通过使用己型无人机进行实验测试评估基于学习的缺陷耐受性控制器的性能。
In this paper we present a learning-based tracking controller based on Gaussian processes (GP) for a fault-tolerant hexarotor in a recovery maneuver. In particular, to estimate certain uncertainties that appear in a hexacopter vehicle with the ability to reconfigure its rotors to compensate for failures. The rotors reconfiguration introduces disturbances that make the dynamic model of the vehicle differ from the nominal model. The control algorithm is designed to learn and compensate the amount of modeling uncertainties after a failure in the control allocation reconfiguration by using GP as a learning-based model for the predictions. In particular the presented approach guarantees a probabilistic bounded tracking error with high probability. The performance of the learning-based fault-tolerant controller is evaluated through experimental tests with an hexarotor UAV.
