Insensitive Mechanism-Based Nonlinear Model Predictive Guidance for UAVs Intercepting Maneuvering Targets with Input Constraints - Danpeng Huang; Mingjie Zhang; Taideng Zhan; Jianjun Ma (Drones)
This paper proposed an innovative guidance strategy, denoted as NMPC-IM, which integrates the Insensitive Mechanism (IM) with Nonlinear Model Predictive Control (NMPC) for Unmanned Aerial Vehicle (UAV) pursuit-evasion scenarios, with the aim of effectively intercepting maneuvering targets with consideration of input constraints while minimizing average energy expenditure. Firstly, the basic principle of IM is proposed, and it is transformed into an additional cost function in NMPC. Secondly, in order to estimate the states of maneuvering target, a fixed-time sliding mode disturbance observer is developed. Thirdly, the UAV’s interception task is formulated into a comprehensive Quadratic Programming (QP) problem, and the NMPC-IM guidance strategy is presented, which is then improved by the adjustment of parameters and determination of maximum input. Finally, numerical simulations are carried out to validate the effectiveness of the proposed method, and the simulation results show that the NMPC-IM guidance strategy can decrease average energy expenditure by mitigating the impact of the target’s maneuverability, optimizing the UAV’s trajectory during the interception process.