Exploring mission planning method for a team of carrier aircraft launching

High-level efficiency and safety are of great significance for improving the fighting capability of an aircraft carrier. One way to enhance efficiency and safety level is to organize the carrier aircraft into combat effectively. This paper studies the mission planning problem for a team of carrier aircraft launching, and a novel distributed mission planning architecture is proposed. The architecture is hierarchical and is comprised of four levels, namely, the input level, the coordination level,the path planning level and the execution level. Realistic constraints in each level of the distributed architecture, such as the vortex flow effect, the crowd effect and the motion of aircraft, are considered in the model. To solve this problem, a distributed path planning algorithm based on the asynchronous planning strategy is developed. The proposed Mission Planning Approach for Carrier Aircraft Launching(MPACAL) is validated using the setups of the Nimitz-class aircraft carrier.Compared to the isolated planning architecture and the centralized planning architecture, the proposed distributed planning architecture has advantages in coordinating the launch tasks not only belonging to the same catapult but also when all different catapults are considered. The proposed MPACAL provides a modeling method for the flight deck operation on aircraft carrier.

Time-varying linear control for tiltrotor aircraft

Tiltrotor aircraft have three flight modes： helicopter mode, airplane mode, and transition mode. A tiltrotor has characteristics of highly nonlinear, time-varying flight dynamics and inertial/-control couplings in its transition mode. It can transit from the helicopter mode to the airplane mode by tilting its nacelles, and an effective controller is crucial to accomplish tilting transition missions. Longitudinal dynamic characteristics of the tiltrotor are described by a nonlinear Lagrangeform model, which takes into account inertial/control couplings and aerodynamic interferences.Reference commands for airspeed velocity and attitude in the transition mode are calculated dynamically by visiting a command library which is founded in advance by analyzing the flight envelope of the tiltrotor. A Time-Varying Linear（TVL） model is obtained using a Taylorexpansion based online linearization technique from the nonlinear model. Subsequently, based on an optimal control concept, an online optimization based control method with input constraints considered is proposed. To validate the proposed control method, three typical tilting transition missions are simulated using the nonlinear model of XV-15 tiltrotor aircraft. Simulation results show that the controller can be used to control the tiltrotor throughout its operating envelop which includes a transition flight, and can also deal with vertical gust disturbances.

Prediction of nonlinear pilot-induced oscillation using an intelligent human pilot model

This paper proposes a method to predict nonlinear Pilot-Induced Oscillation(PIO)using an intelligent human pilot model.This method is based on a scalogram-based PIO metric,which uses wavelet transforms to analyze the nonlinear characteristics of a time-varying system.The intelligent human pilot model includes three modules:perception module,decision and adaptive module,and execution module.Intelligent and adaptive features,including a neural network receptor,fuzzy decision and adaptation,are also introduced into the human pilot model to describe the behavior of the human pilot accommodating the nonlinear events.Furthermore,an algorithm is proposed to describe the procedure of the PIO prediction method with nonlinear evaluation cases.The prediction results obtained by numerical simulation are compared with the assessments of flight test data to validate the utility of the method.The flight test data were generated in the evaluation of the Smart-Cue/Smart-Gain,which is capable of reducing the PIO tendencies considerably.The results show that the method can be applied to predict the nonlinear PIO events by human pilot model simulation.