Cooperative Path Following Control of Fixed-wing Unmanned Aerial Vehicles with Collision Avoidance

2020 ◽  
Vol 100 (3-4) ◽  
pp. 1569-1581
Author(s):  
Shulong Zhao ◽  
Xiangke Wang ◽  
Hao Chen ◽  
Yajing Wang
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Zhengyang Cui ◽  
Yong Wang

With growing worldwide interests in commercial, scientific, and military issues, there has been a corresponding rapid growth in demand for the development of unmanned aerial vehicles (UAVs) with more reliable and safer motion control abilities. This paper presents a new nonlinear path following scheme integrated with a heading control law for achieving accurate and reliable path following performance. Both backstepping and finite-time techniques are employed for developing the path following and heading control strategies capable of minimizing cross-track errors in finite-time with elegant transient performance, while the barrier Lyapunov function scheme is adopted to limit turning rates of the UAV for preventing it from capsizing which may be induced by overquick steering actions. A fixed-time nonlinear estimator, based on UAV kinematics, is designed for estimating the uncertainties with sideslip angles caused by external disturbances and inertial motions. To avoid the complicated calculation of derivatives of virtual control terms in backstepping, command filters and auxiliary systems are likewise introduced in the design of control laws. Extensive numerical simulation studies on a nonlinear UAV model are conducted to demonstrate the effectiveness of the proposed methodologies.


Robotica ◽  
2021 ◽  
pp. 1-20
Author(s):  
Daegyun Choi ◽  
Anirudh Chhabra ◽  
Donghoon Kim

Summary This paper proposes an intelligent cooperative collision avoidance approach combining the enhanced potential field (EPF) with a fuzzy inference system (FIS) to resolve local minima and goal non-reachable with obstacles nearby issues and provide a near-optimal collision-free trajectory. A genetic algorithm is utilized to optimize parameters of membership function and rule base of the FISs. This work uses a single scenario containing all issues and interactions among unmanned aerial vehicles (UAVs) for training. For validating the performance, two scenarios containing obstacles with different shapes and several UAVs in small airspace are considered. Multiple simulation results show that the proposed approach outperforms the conventional EPF approach statistically.


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