MPC-based control strategy for conflict resolution between vehicles and aircraft on airport

To address the frequent conflicts between airport ground service vehicles and aircraft at intersections of taxiways and roadways, a model predictive control (MPC)-based conflict resolution method was proposed for airport autonomous vehicles and aircraft. The typical operation scenarios of ground vehicle-aircraft intersections on the airport were analyzed. The longitudinal dynamic constraints of vehicles and the safety boundary requirements of aircraft in conflict situations were clarified. On this basis, the period when the aircraft and its safety clearance passed through the conflict area was defined as a dynamic red light time window constraint. With the objectives of minimizing vehicle energy consumption and maximizing traffic efficiency, an MPC model for vehicle was established. A sequential quadratic programming method was employed to solve the nonlinear constrained optimization problem in a rolling manner, thereby generating optimal speed and acceleration control sequences for vehicles in real time. Simulation experiments were conducted using real-world scenario data from Tianjin Binhai International Airport. Multiple sets of random operating conditions were also designed for comparative analysis. Research results show that, with the proposed MPC strategy, the vehicle can avoid potential conflicts with aircraft. Compared with conventional human driving and idealized human driving, the average energy consumption is reduced by 14.81% and 14.27%, respectively, and the average travel time is shortened by 6.48% and 5.70%. The trajectory smoothness and control performance are both significantly superior. These findings indicate that the proposed conflict resolution control strategy not only enhances the safety and efficiency of airport ground vehicle operations but also provides practical theoretical support and technical reference for the future application and popularization of autonomous vehicles on airports.