Because the state of a free-floating space robot model is uncertain and sudden changes in the model parameters might undermine the stability of the system, this paper proposes a control strategy based on a variable structure neural integrated controller. This scheme does not need a precise space robot model, making use of the radial basis function neural network ability approach to learn about an uncertain model. The network weights are adjusted online in real-time. During the early period of the control phase and parameter changes, the variable structure controller compensates for the uncertain model which the neural network could not learn well. It also creates global asymptotic stability for the whole closed-loop system. Simulation results show that the controller can handle bad changeable conditions and has important application value for defense, aerospace and other major security fields.Keywords neural network, variable structure, adaptive control, global asymptotical stability CitationZhang W H, Qi N M, Ma J, et al. Neural integrated control for a free-floating space robot with suddenly changing parameters.

Due to the fact that vastly different variables and constraints are simultaneously considered, truss layout optimization is a typical difficult constrained mixed-integer nonlinear program. Moreover, the computational cost of truss analysis is often quite expensive. In this paper, a novel fitness estimation based particle swarm optimization algorithm with an adaptive penalty function approach (FEPSO-AP) is proposed to handle this problem. FEPSO-AP adopts a special fitness estimate strategy to evaluate the similar particles in the current population, with the purpose to reduce the computational cost. Further more, a laconic adaptive penalty function is employed by FEPSO-AP, which can handle multiple constraints effectively by making good use of historical iteration information. Four benchmark examples with fixed topologies and up to 44 design dimensions were studied to verify the generality and efficiency of the proposed algorithm. Numerical results of the present work compared with results of other state-of-the-art hybrid algorithms shown in the literature demonstrate that the convergence rate and the solution quality of FEPSO-AP are essentially competitive.

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Lunar surface disturbances by spacecraft engine jets are of particular concern in the current new phase of lunar exploration, when dozens of landing missions and permanent bases are being planned. Some of these exploration efforts will involve multiple landings and liftoffs around the same lunar site; thus, it is essential to evaluate their effect on astronauts and assets on the lunar surface. Here, we assess the surface disturbances during the Chang'e‐5 landing and liftoff procedures through the photometric analysis of high‐resolution multi‐temporal surface and orbital images. Centimeter‐scale surface images reveal a four‐stage evolution of the landing plume impingement over a period of ∼50 s, which involves phenomena such as dust devils and streaks, and displacement of cobbles. Temporal‐ratio calculation of orbital images (including one acquired between landing and liftoff) enables the first direct observation of ascent plume effects. The ascent blast zone consists of two separated sub‐areas (∼3,400 km2 in total), which is nearly twice larger than that of the landing blast zone. The final disturbed surface is characterized by a central main zone (∼2,300 m2) surrounded by a marginal diffuse zone (∼15,300 m2). Phase‐ratio analyses suggest that plume impingement destroys the micro‐porous structure of the uppermost regolith. We estimate that future lunar landers (e.g., SpaceX's Starship) may cause significant lunar surface disturbances over an area of square kilometers. Our results provide unique insights into Chang'e‐5 mission activities, and instructive references for the planning of future lunar endeavors, including the design and construction of surface experiment packages and permanent lunar bases.