The longitudinal automatic carrier landing system (ACLS) control law is designed based on nonlinear dynamic inversion (NDI), which can reject air wake, decouple lateral states, and track the dynamic desired touchdown point (DTP). First of all, the nonlinear landing model of F/A−18 aircraft in the final approach is established, in which the parameters of the aerodynamic, control surfaces, and limited states are acquired. Second, the strategy of tracking the desired longitudinal trajectory through pitch angle control is adopted. The automatic power compensation system (APCS), pitch angle rate, pitch angle, and vertical position control loops are developed based on the adaptive NDI. The stable analysis and the principal description are derived in detail. Deck motion compensation (DMC) algorithm is designed by frequency response method. Third, the control parameters are optimized through the genetic algorithm. A fitness function integrated with velocity, angle of attack (AOA), pitch rate, pitch angle, and vertical position of the aircraft are proposed. Finally, integrated simulations are conducted on a semi-physical simulation platform. The results indicate that the adopted automatic landing control law can achieve both excellent performance and the ability to reject the air wake and lateral coupling.

A global path-planning algorithm for robots is proposed based on the critical-node diffusion binary tree (CDBT), which solves the problems of large memory consumption, long computing time, and many path inflection points of the traditional methods. First of all, the concept of Quad-connected, Tri-connected, Bi-connected nodes, and critical nodes are defined, and the mathematical models of diverse types of nodes are established. Second, the CDBT algorithm is proposed, in which different planning directions are determined due to the critical node as the diffusion object. Furthermore, the optimization indices of several types of nodes are evaluated in real-time. Third, a path optimization algorithm based on reverse searching is designed, in which the redundant nodes are eliminated, and the constraints of the robot are considered to provide the final optimized path. Finally, on one hand, the proposed algorithm is compared with the A* and RRT methods in the ROS system, in which four types of indicators in the eight maps are analysed. On the other hand, an experiment with an actual robot is conducted based on the proposed algorithm. The simulation and experiment verify that the new method can reduce the number of nodes in the path and the planning time and is suitable for the motion constraints of an actual robot.