Path planning based on model predictive control algorithm under moving threat

Combining with the prediction of fast moving threat, the model predictive control(MPC) algorithm is adopted in the dynamic path planning for uninhabited air vehicles(UAVs). By using the converted measurement Kalman filter(CMKF) algorithm, the states of moving targets are predicted, and then the threats against UAV are evaluated, together with the length of path, to establish the cost function. The path planning is accomplished by obtaining a series of on-line control values which are figured out by minimizing the cost function in receding horizon. Finally, the application efficiency of MPC in path planning is validated by the simulation results.