Optimizing aircraft collision avoidance and performing trajectory optimization are the key problems in an air transportation system. This paper is focused on solving these problems by using a stochastic optimal control approach. The major contribution of this paper is a proposed stochastic optimal control algorithm to dynamically adjust and optimize aircraft trajectory. In addition, this algorithm accounts for random wind dynamics and convective weather areas with changing size. Although the system is modeled by a stochastic differential equation, the optimal feedback control for this equation can be computed as a solution of a partial differential equation, namely, an elliptic Hamilton‐Jacobi‐Bellman equation. In this paper, we solve this equation numerically using a Markov Chain approximation approach, where a comparison of three different iterative methods and two different optimization search methods are presented. Simulations show that the proposed method provides better performance in reducing conflict probability in the system and that it is feasible for real applications.

A high-quality and secure touchdown run for an aircraft is essential for economic, operational, and strategic reasons. The shortest viable touchdown run without any skidding requires variable braking pressure to manage the friction between the road surface and braking tire at all times. Therefore, the manipulation and regulation of the anti-skid braking system (ABS) should be able to handle steady nonlinearity and undetectable disturbances and to regulate the wheel slip ratio to make sure that the braking system operates securely. This work proposes an active disturbance rejection control technique for the anti-skid braking system. The control law ensures action that is bounded and manageable, and the manipulating algorithm can ensure that the closed-loop machine works around the height factor of the secure area of the friction curve, thereby improving overall braking performance and safety. The stability of the proposed algorithm is proven primarily by means of Lyapunov-based strategies, and its effectiveness is assessed by means of simulations on a semi-physical aircraft brake simulation platform.

In this paper, a nonlinear model predictive control (NMPC) method based on mixed slipdeceleration (MSD) with runway identification is proposed to prevent the aircraft wheels from locking up and improve the braking performance under time-varied runway conditions. The MSD control algorithm reduces the dependence of control performance on slip rate estimation accuracy and retains a good slip rate control performance. The proposed NMPC control method guarantees optimal braking torque on each wheel by individually controlling the slip rate of each wheel near the optimal point. A nonlinear brake control model based on aircraft ground taxiing dynamics is derived. In this model, the tire-runway friction coefficient-slip rate model under different runway conditions and vertical force variation caused by brake are considered. A runway identification algorithm based on friction coefficient and friction coefficient slope is used to identify the real-time runway status, based on which the prediction model and optimization function of the proposed control scheme are modified. The wheel slip stable zone and the system maximum brake torque are regarded as time-domain constraints of the NMPC for safety considerations and physical limitations. The control objectives of the NMPC include longitudinal deceleration, braking performance, and preservation of crew comfort. The proposed MSD-based NMPC controller is verified by a tricyclegeared aircraft model using MATLAB/Simulink software. Simulation results of different control schemes on a specific mixed runway show good performances of the proposed control method. The proposed control method provides a new efficient solution for aircraft wheel braking on variable runway. INDEX TERMS NMPC, mixed slip-deceleration control, aircraft brake control, runway identificationThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

In order to deal with strong nonlinearity and external interference in the braking process, this paper proposes a robust self-learning PID algorithm based on particle swarm optimization, which does not depend on a precise mathematical model of the controlled object. The self-learning function is used to adapt to the diversity of the runway road surface friction, the particle swarm algorithm is used to optimize the rate of self-learning, and robust control is used to deal with the modeling uncertainty and external disturbance of the system. The convergence of the control strategy is proved by theoretical analysis and simulation experiments. The superiority and accuracy of the method are verified by NASA ground test results. The simulation results shows that the adverse effect of the external disturbance is suppressed, and the ideal trajectory is tracked.

In this paper, a relative threshold event-triggered based novel complementary sliding mode control (NSMCR) algorithm of all-electric aircraft (AEA) anti-skid braking system (ABS) is proposed to guarantee the braking stability and tracking precision of reference wheel slip control. First, a model of the braking system is established in strict-feedback form. Then a virtual controller with a nonlinear control algorithm is proposed to address the problem of constraint control regarding wheel slip rate with asymptotical stability. Next, a novel approaching law-based complementary sliding mode controller is developed to keep track of braking pressure. Moreover, the robust adaptive law is designed to estimate the uncertainties of the braking systems online to alleviate the chattering problem of the braking pressure controller. Additionally, to reduce the network communication and actuator wear of AEA-ABS, a relative threshold event trigger mechanism is proposed to transmit the output of NSMC in demand. The simulation results under various algorithms regarding three types of runway indicate that the proposed algorithms can improve the performance of braking control. In addition, the hardware-in-the-loop (HIL) experimental results prove that the proposed methods are practical for real-time applications.