This academic paper addresses the challenges associated with trajectory planning for affordable and light-weight Unmanned Aerial Vehicle (UAV) swarms, despite limited computing resources and extensive cooperation requirements. Specifically, an imitation-based starling cluster cooperative trajectory planning technique is proposed for a fixed-wing model of a six-degree-of-freedom UAV cluster. To achieve this, dynamic trajectory prediction of the rapid random search tree is utilized to generate a track solution adapted to the terrain environment. Additionally, the Dubins aircraft path solution is applied as it is suitable for executing input track commands by the UAV model. Computational simulations on different cluster sizes show the approach can maintain the cluster state while navigating diverse terrains, with the track solution complying with the UAV’s physical model properties.

The design and analysis procedures for the thrust controller used in variable-thrust rocket engines are substantially different from those used in conventional engines due to the large-scale thrust adjustment capabilities that result in a wide range of operating situations. In this study, two control algorithms, h-infinity and adaptive linear quadratic regulator (ALQR), are constructed, examined, and contrasted utilizing the adaptive control system architecture. Both methods are capable of producing engines that respond in less than one second, have a steady-state error of less than two percent, and are robust.