Trajectory Planning for UAV Swarm Tracking Moving Target Based on an Improved Model Predictive Control Fusion Algorithm

A method based on deep neural network (DNN) optimized model predictive control (MPC) and standoff fusion is proposed to address the problem of tracking moving target trajectory planning for UAV swarms in uncertain environments. Firstly, online UAV trajectory planning is carried out based on the optimised MPC algorithm, and the standoff algorithm is introduced to achieve formation keeping and online obstacle avoidance targets, which in turn solves the problem of high probability of tracking target loss due to the limitation of the detection range of the UAV swarm. To avoid the local saturation problem of the control method, an improved MPC model is constructed by combining anti-windup algorithms. At the same time, the integration of DNN with the improved MPC algorithm effectively addresses the large-scale, multi-constraint optimization problem of UAV swarm control. Based on an approximate optimal control strategy, a dynamic control system model is constructed, which can rapidly adapt to external disturbances and adjust the control inputs. This approach compensates for the limitations of traditional MPC, which relies on precise prior models. Simulation results show that the improved MPC fusion algorithm is more stable in formation maintenance, faster in convergence, and more effective in monitoring targets compared to the single MPC algorithm. It enables more precise control with higher robustness. This approach is more aligned with the real-world flight requirements of UAV swarms and serves as an effective pathway for applying improved MPC methods to multi-agent control.