Aerodynamic Effects Compensation on Multi-Rotor UAVs Based on a Neural Network Control Allocation Approach

This paper shows that the aerodynamic effects can be compensated in a quadrotor system by means of a control allocation approach using neural networks.Thus,the system performance can be improved by replacing the classic allocation matrix,without using the aerodynamic inflow equations directly.The network training is performed offline,which requires low computational power.The target system is a Parrot MAMBO drone whose flight control is composed of PD-PID controllers followed by the proposed neural network control allocation algorithm.Such a quadrotor is particularly susceptible to the aerodynamics effects of interest to this work,because of its small size.We compared the mechanical torques commanded by the flight controller,i.e.,the control input,to those actually generated by the actuators and established at the aircraft.It was observed that the proposed neural network was able to closely match them,while the classic allocation matrix could not achieve that.The allocation error was also determined in both cases.Furthermore,the closed-loop performance also improved with the use of the proposed neural network control allocation,as well as the quality of the thrust and torque signals,in which we perceived a much less noisy behavior.

Rotor cross-tilt optimization for yaw control improvement of multi-rotor eVTOL aircraft

Manned multi-rotor electric Vertical Takeoff and Landing(eVTOL)aircraft is prone to actuator saturation due to its weak yaw control efficiency.To address this inherent problem,a rotor cross-tilt configuration is applied in this paper,with an optimization method proposed to improve the overall control efficiency of the vehicle.First,a flight dynamics model of a 500-kg manned multi-rotor eVTOL aircraft is established.The accuracy of the co-axial rotor model is verified using a single arm test bench,and the accuracy of the flight dynamics model is verified by the flight test data.Then,an optimization method is designed based on the flight dynamics model to calculate an optimal rotor cross-tilt mounting angle,which not only improves the yaw control efficiency,but also basically maintains the efficiency of other control channels.The ideal rotor cross-tilt mounting angle for the prototype is determined by comprehensively considering the optimal results with different payloads,forward flight speeds,and rotor mounting angle errors.Finally,the feasibility of the rotor cross-tilt mounting angle is proved by analyzing the control derivatives of the flight dynamics model,the test data of a ground three Degree-of-Freedom(3DOF)platform,and the actual flight data of the prototype.The results show that a fixed rotor cross-tilt mounting angle can achieve ideal yaw control effectiveness,improving yaw angle tracking and hold ability,increasing endurance time,and achieving good yaw control performance with different payloads and forward speeds.

Adaptive Attitude Control for Multi-MUAV Systems With Output Dead-Zone and Actuator Fault

Many mechanical parts of multi-rotor unmanned aerial vehicle(MUAV)can easily produce non-smooth phenomenon and the external disturbance that affects the stability of MUAV.For multi-MUAV attitude systems that experience output dead-zone,external disturbance and actuator fault,a leader-following consensus anti-disturbance and fault-tolerant control(FTC)scheme is proposed in this paper.In the design process,the effect of unknown nonlinearity in multi-MUAV systems is addressed using neural networks(NNs).In order to balance out the effects of external disturbance and actuator fault,a disturbance observer is designed to compensate for the aforementioned negative impacts.The Nussbaum function is used to address the problem of output dead-zone.The designed fault-tolerant controller guarantees that the output signals of all followers and leader are synchronized by the backstepping technique.Finally,the effectiveness of the control scheme is verified by simulation experiments.

无人机物资运输与智能避障系统设计

该文在开源飞控的基础上,探讨多旋翼无人机在多传感器的配合下实现自主避障功能。传感器与飞控之间通过Mavlink协议进行沟通与数据传输,通过人工势场法建立数学模型对传感器反馈的数据进行综合处理,经过系列假设与运算找到多旋翼障碍物与航标点对多旋翼作用的平衡位置,同时找出对于多旋翼规避障碍的影响参数,最后通过Dronekit-python工具对多旋翼路线进行标定。