scholarly journals Real-time Conflict Resolution Algorithm for Multi-UAV Based on Model Predict Control

Algorithms ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 47
Author(s):  
Hao-xiang Chen ◽  
Ying Nan ◽  
Yi Yang

A real-time conflict resolution algorithm based on model predictive control (MPC) is introduced to address the flight conflict resolution problem in multi-UAV scenarios. Using a low-level controller, the UAV dynamic equations are abstracted into simpler unicycle kinematic equations. The neighboring UAVs exchange their predicted trajectories at each sample time to predict the conflicts. Then, under the predesignated resolution rule and strategy, decentralized coordination and cooperation are performed to resolve the predicted conflicts. The controller structure of the distributed nonlinear model predictive control (DNMPC) is designed to predict potential conflicts and calculate control variables for each UAV. Numerical simulations of multi-UAV coordination are performed to verify the performance of the proposed algorithm. Results demonstrate that the proposed algorithm can resolve the conflicts sufficiently in real time, while causing no further conflicts.

Machines ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 105
Author(s):  
Zhenzhong Chu ◽  
Da Wang ◽  
Fei Meng

An adaptive control algorithm based on the RBF neural network (RBFNN) and nonlinear model predictive control (NMPC) is discussed for underwater vehicle trajectory tracking control. Firstly, in the off-line phase, the improved adaptive Levenberg–Marquardt-error surface compensation (IALM-ESC) algorithm is used to establish the RBFNN prediction model. In the real-time control phase, using the characteristic that the system output will change with the external environment interference, the network parameters are adjusted by using the error between the system output and the network prediction output to adapt to the complex and uncertain working environment. This provides an accurate and real-time prediction model for model predictive control (MPC). For optimization, an improved adaptive gray wolf optimization (AGWO) algorithm is proposed to obtain the trajectory tracking control law. Finally, the tracking control performance of the proposed algorithm is verified by simulation. The simulation results show that the proposed RBF-NMPC can not only achieve the same level of real-time performance as the linear model predictive control (LMPC) but also has a superior anti-interference ability. Compared with LMPC, the tracking performance of RBF-NMPC is improved by at least 43% and 25% in the case of no interference and interference, respectively.


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