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The latest science news, in brief.

The latest science news, in brief.

The latest science news, in brief.

The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. However, when compared to an established deterministic controller, it consistently falls short in terms of landing distance accuracy. To address this issue, the HYDESTOC Hybrid Deterministic-Stochastic (a combination of DDPG/deep deterministic policy gradient and PID/proportional-integral-derivative) algorithm was introduced to improve terminal distance accuracy while keeping propellant consumption low. Results from extensive cross-validated Monte Carlo simulations show that a miss distance of less than 0.02 meters, landing speed of less than 0.4 m/s, settling time of 20 seconds or fewer, and a constant crash-free performance is achievable using this method.

For the diverse payloads of Reusable Launch Vehicles and the inevitable problem of change in the center of mass, this paper proposes an incremental nonlinear dynamic inversion considering centroid variation control. Regarding the trans-atmosphere flight environment, the six-degree-of-freedom dynamics model considering centroid shift, Earth rotation, and the Clairaut Ellipsoid Model is established to improve model accuracy. An incremental nonlinear dynamic inversion considering a centroid variation controller with excellent dynamic performance and adjustment under the centroid variation is designed for the model, which fully meets the safety requirements of RLV reentry. An extended state observer considering centroid variation is proposed to solve the problem with difficult direct measurement of angular acceleration, which incorporates the influence of centroid variation into the known part to improve estimation accuracy and speed. Finally, the simulation results are provided to verify the robustness of the change of centroid position and good control quality with the proposed controller.

This article presents fault-tolerant dynamic allocation strategies designed to mitigate propulsion and actuation failures in launch vehicles using a clustered engine configuration. In particular, it addresses engine thrust loss and thrust vector control (TVC) jamming faults during the atmospheric ascent flight of a five-engine launch vehicle. Three different strategies are introduced: a fault-tolerant pseudo-inverse solution, a convex optimization-based approach, and a constrained nonlinear optimization one. These approaches are analyzed and compared at a linear design point and further evaluated using a nonlinear simulator of the launcher. The results demonstrate that these three dynamic allocation techniques are able to provide successful recovery from engine thrust loss failures (up to a certain level depending on the engine throttling capability), TVC actuator jamming failures, and simultaneous engine and actuator failures.

This paper investigates the coupling of successive convex optimization guidance with robust structured H∞ control for the descent and precise landing of Reusable Launch Vehicles (RLVs). More particularly, this Guidance and Control (G&C) system is foreseen to be integrated into a nonlinear six-degree-of-freedom RLV controlled dynamics simulator which covers the aerodynamic and powered descent phase until vertical landing of a first-stage rocket equipped with a thrust vector control system and steerable planar fins. A cost function strategy analysis is performed to find out the most efficient one to be implemented in closed-loop with the robust control system and the vehicle flight mechanics involved. In addition, the controller synthesis via structured H∞ is thoroughly described. The latter are built at different points of the descent trajectory using Proportional-Integral-Derivative (PID)-like structures with feedback on the attitude angles, rates, and lateral body velocities. The architecture is verified through linear analyses as well as nonlinear cases with the aforementioned simulator, and the G&C approach is validated by comparing the performance and robustness with a baseline system in nominal conditions as well as in the presence of perturbations. The overall results show that the proposed G&C system represents a relevant candidate for realistic descent flight and precise landing phase for reusable launchers.

The latest science news, in brief. The latest science news, in brief. Healthcare professionals run serological tests for Covid-19 in Luanda, Angola