RESUMO
Inspired by the exceptional flight ability of birds and insects, a bio-inspired neural adaptive flight control structure of a small unmanned aerial vehicle was presented. Eight pressure sensors were elaborately installed in the leading-edge area of the forward wing. A back propagation neural network was trained to predict the aerodynamic moment based on pressure measurements. The network model was trained, validated, and tested. An adaptive controller was designed based on a radial basis function neural network. The new adaptive laws guaranteed the boundedness of the adaptive parameters. The closed-loop stability was analyzed via Lyapunov theory. The simulation results demonstrated the robustness of the bio-inspired flight control system when subjected to measurement noise, parametric uncertainties, and external disturbance.
RESUMO
This paper proposes a prescribed-time cooperative guidance law (PTCGL) against maneuvering target with variable line-of-sight (LOS) angle constraint for leader-following missiles, where the convergence times of the state errors can be arbitrarily set. The leader missile against the maneuvering target is provided as the modified proportional navigation (MPN) guidance law. The proposed PTCGL for follower missiles consist of two parts, in LOS direction, the range-to-go (Rgo) is selected as a co-variable, avoiding the estimation of time-to-go (Tgo), and a novel second-order nonlinear consensus protocol is developed to design the PTCGL; in normal LOS direction, considering the variable LOS angle constraint, the cooperative guidance law is designed with the proposed prescribed-time sliding model control (PTSMC) method. Besides, the prescribed-time convergence of Rgo and LOS errors are proved. Finally, the effectiveness and superiority of the proposed PTCGL with leader-following strategy is illustrated by numerical simulation results.