This paper presents an efficient approach for safe landing trajectory generation of an airplane with structural damage to its wing flying in proximity to local terrain. A damaged airplane maneuvering flight envelope is estimated by analyzing the local stability and flying quality at each trim condition. Trim state distance to a flight envelope boundary provides a novel criterion defined as the safety value index to prioritize choice of trim conditions for postdamage flight. A library of trajectory segments including trim states and transition maneuvers between trim conditions in the local neighborhood are generated using a linear quadratic regulator controller. A potential field strategy is used to rapidly define emergency landing trajectories composed as trim state sequences based on criteria including terrain avoidance, safety value index, and safe landing requirements such as touchdown heading and position, airspeed, glide slope, and bank angle. Cost criteria are balanced by adjusting weights. Landing scenarios are presented to demonstrate the capability of the proposed approach to autonomously plan safe landing trajectories.
PurposeUnderstanding the performance and flight envelope of a damaged aircraft is a preliminary requirement to recover the aircraft after damage. This paper aims to provide a comprehensive understanding of wing damage effect on airplane performance, local stability, and flying quality of each trim state inside the achievable flight envelope.Design/methodology/approachThis paper demonstrates the use of attainable equilibrium points which are referred as trim states in order to estimate a damaged airplane manoeuvring flight envelope using a numerical computation method.FindingsWing damaged airplane manoeuvring flight envelope is estimated for different portions of the wing tip loss. Local stability at each trim condition inside the estimated flight envelope is analysed, and also motion flight modes and flying quality sensitivity to the wing damage are explored.Originality/valueLocal stability and flying quality analysis at each trim condition inside the flight envelope which demonstrate the effect of damage provides a criterion to prioritize the choice of trimmed flight condition as motion primitives for the airplane post‐damage flight and safe landing.
This paper investigates a dual-timescale autopilot for a wing-damaged airplane applying nonlinear adaptive sliding mode approach. The adaptive flight control strategy is used to track outer-loop angle commands while accommodating wing damage effect. Two distinct adaptive sliding mode control strategies are designed for the inner- and outer-loop dynamics. The airplane nonlinear model is developed considering center of gravity shift and aerodynamic changes due to the asymmetric wing damage. The performance of the proposed nonlinear adaptive sliding mode controller is evaluated through numerical simulation on NASA generic transport model and is compared with two adaptive algorithms: model reference adaptive control and a robust adaptive control strategy. The results demonstrate that the proposed control law achieves closed-loop stability in the presence of wing damage and accelerometers bias, and also provides satisfactory tracking performance.
This paper investigates multi-objective optimization of coordinated patrolling flight of multiple unmanned aerial vehicles in the vicinity of terrain, while respecting their performance parameters. A new efficient modified A-star (A*) algorithm with a novel defined criterion known as individual revisit time cell value is introduced and extended to the whole area of the 3D mountainous environment. As a contribution to solving trade-offs in the optimization problem, revisit time is conjugated with other contrary costs effective in flight planning through Pareto analysis. By introducing the revisit time and applying a specific setup to mitigate computational complexity, the proposed algorithm efficiently revisits the desired zones, which are more important to be revisited during the patrolling mission. The results of the introduced modified A* algorithm are compared in various scenarios with two different algorithms including a complete and optimal algorithm known as Dijkstra, and an evolutionary algorithm known as the genetic algorithm. Simulation results demonstrate that the proposed algorithm generates faster and more efficient trajectories in complex multiagent scenarios due to the introduced cell selection method and dynamic-based simplifications applied in this research.
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