The contribution of this work is the numerical simulation and the experimental assessment of a network distributed control system using an unmanned aerial vehicle and a remote master controller connected by a wireless network. A novel multi-input multi-output long-range predictive horizon minimum variance control approach is developed and implemented in altitude, heading, lateral and longitudinal velocities control problems under the influence of process noise, measurement noise and time-delay. The proposed autopilots exhibited damped and fast response speeds with optimal control and output variance minimization, outperforming a classic model predictive control approach in convincing experimental field tests in real-world environments and scenarios.

这项工作的贡献是使用无人飞行器和通过无线网络连接的远程主控制器的网络分布式控制系统的数值模拟和实验评估。在过程噪声，测量噪声和时延的影响下，针对海拔，航向，横向和纵向速度控制问题，开发并实现了一种新颖的多输入多输出远程预测地平线最小方差控制方法。所提出的自动驾驶仪在最佳控制和输出方差最小化的情况下表现出阻尼和快速的响应速度，在说服现实环境和场景中的实验现场测试方面优于经典的模型预测控制方法。