In this research, the design of a robust curved-line path-following control system for fixed-wing unmanned aerial vehicles (FWUAVs) affected by uncertainties on the latitude plane is studied. This is undertaken to enhance closed-loop system robustness under unknown uncertainties and derive the control surface deflection angle directly used to control FWUAVs, which has rarely been studied in previous works. The system is formed through the mass center position control (MCPC) and yaw angle control (YAC) subsystems. In the MCPC, the desired yaw angle, which is treated as the reference signal for the YAC subsystem, is calculated analytically using path-following errors, current flow angles, and the yaw angle. In the YAC, a disturbance estimator is designed to estimate uncertainties such as nonlinearities, couplings, time variations, model parameter perturbations, and unmodeled dynamics. Predictive functional controllers are designed to target nominal systems in the absence of uncertainties, such that the estimations of the uncertainties can be incorporated through feedback for closed-loop system robustness enhancement. The simulation results show that higher path-following precision and stronger robustness for the FWUAVs based on the proposed approach can be achieved using only rough model parameters compared with the conventional nonlinear dynamic inversion, which requires detailed model information.

This paper studies the straight-line path-following problem on the lateral plane for fixed-wing unmanned aerial vehicles (FWUAVs) which are susceptible to uncertainties. Firstly, based on the natural frame’s location on the prescribed reference paths, the command yaw angle (which is the basis for yaw angle control system design) is solved analytically by combining it with the errors of path following, attack angle, sideslip angle, attitude angles, and geometric parameters of the prescribed reference paths. Secondly, by considering complicated dynamic characteristics, a linear extended state observer is designed to estimate uncertainties such as nonlinearities, couplings, and unmodeled dynamics whose estimated values are incorporated into the continuous-time predictive controllers for feedback compensation. Finally, numerical simulations are conducted to demonstrate the advantages of the proposed method, including reduced tracking errors and enhanced robustness in the closed-loop system, as compared to the conventional nonlinear dynamic inversion and sliding mode control approaches.

The layout of the wing's internal structure not only dramatically influences the wing's strength stiffness but also directly affects the aerodynamic characteristics of the aircraft. Based on the original wing structure, a more flexible spatial design layout to achieve improved overall structural load-bearing performance, and a reasonable structural lightweight design are the research priorities to be considered for the development of future aircraft. Therefore, this paper attempted to design and analyze a lightweight airfoil that meets the performance requirements. Combining the strategy of hierarchical optimization design with the advantages of engineering bionics, the diatom Arachnoidiscus bionic structure, topological optimization, partial least squares regression (PLSR), and multi-objective particle swarm algorithm (PSO) are applied to optimize the placement and size of wing’s internal components. The simulation results show that the weight of the optimized wing structure is reduced by 6% while satisfying the requirements of maximum stress and maximum deformation.

Aiming at the problems that it is difficult to calculate and establish accurate mathematical model in the structure optimization with discrete variables, the Lagrange multiplier method is proposed to deal with the constrain conditions in the improved genetic algorithm which solved the problems of "premature" and low efficiency in the simple genetic algorithm. The improved genetic algorithm is achieved by MATLAB, and it invoked the ANSYS software to conduct finite element calculations and data exchange. This collaborative algorithm is verified by two truss examples in this paper. Compared with other research results, the collaborative optimization computation using improved genetic algorithm and ANSYS based on Lagrange multiplier method can get better global optimal solution, and it also has high accuracy and good reliability.