Online Learning-based Receding Horizon Control of Tilt-rotor Tricopter: A Cascade Implementation
Online Learning-based Receding Horizon Control of Tilt-rotor Tricopter: A Cascade Implementation
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基于在线学习的倾转旋翼三轴飞行器后退地平线控制：级联实现
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Erdal Kayacan
中科院分区：
文献类型：
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作者：
M. Mehndiratta;Erdal Kayacan
针对倾斜旋翼三旋翼无人机的轨迹跟踪问题，提出了一种基于学习的串级非线性模型预测控制(NMPC)算法，并结合非线性滚动地平线估计方法在线估计了推力和阻力矩系数这两个时变气动参数。由于基于模型的控制器的性能保证了要控制的系统的准确数学模型，因此估计变化的动态确实很重要，以便使NMPC对时变的操作扰动具有自适应能力，从而具有鲁棒性。为了进一步说明基于学习的串级NMPC的跟踪能力，以一个复杂的方形轨迹为例进行了飞行，并观察到其跟踪效果良好。据我们所知，这是基于在线学习的串级NMPC首次应用于复杂的航空航天系统。此外，由于有了SCARO工具包，整个闭环系统的执行时间不到4毫秒，这最终证明了所提出的控制框架的实时潜力。
This study manifests a learning-based cascade nonlinear model predictive control (NMPC) algorithm for the trajectory tracking of a tilt-rotor tricopter UAV; wherein two time-varying aerodynamic parameters, thrust and drag-moment coefficients, are estimated online incorporating nonlinear moving horizon estimation method. Since the performance of a model-based controller is guaranteed for an accurate mathematical model of the system to be controlled, it is indeed important to estimate the changing dynamics in order to make NMPC adaptive - and therefore robust - to the time-varying operational disturbances. To further illustrate the tracking capability of learning-based cascade NMPC, a complex square-shaped trajectory is flown and is observed to be well tracked. To the best of our knowledge, this is the first application of an online learning-based cascade NMPC to a complicated aerospace system. Moreover, owing to ACADO toolkit, the overall execution time of the closed-loop is below 4 milliseconds, which eventually demonstrates the real-time potential of the presented control framework.