In this paper, a novel neural network-based error-track iterative learning control scheme is proposed to tackle trajectory tracking problem for tank gun control systems. Firstly, the system modeling for tank gun control systems is introduced as a preparation of controller design. Then, the reference error trajectory is constructed to deal with the nonzero initial error of iterative learning control. The adaptive iterative learning controller for tank gun control systems is designed by using Lyapunov approach. Adaptive learning neural network is adopted to approximate nonlinear uncertainties, with robust control technique being used compensate the approximation error and external disturbances. As the iteration number increases, the system error can follow the desired error trajectory over the whole time interval, which makes the system state accurately track the reference error trajectory during the predetermined part time interval. Numerical simulations demonstrate the effectiveness of the proposed iterative learning control scheme. INDEX TERMS Tank gun control systems, iterative learning control, neural network, Lyapunov approach.
Alloy‐based anodes have shown great potential to be applied in sodium‐ion batteries (SIBs) due to their high theoretical capacities, suitable working potential, and abundant earth reserves. However, their practical applications are severely impeded by large volume expansion, unstable solid‐electrolyte interfaces (SEI), and sluggish reaction kinetics during cycling. Herein, a surface engineering of tin nanorods via N‐doped carbon layers (Sn@NC) and an interface engineering strategy to improve the electrochemical performance in SIBs are reported. In particular, the authors demonstrate that uniform surface modification can effectively facilitate electron and sodium transport kinetics, confine alloy pulverization, and simultaneously synergize interactions with the ether‐based electrolyte to form a robust organic‐inorganic SEI. Moreover, it is discovered that the diethylene glycol dimethyl ether electrolyte with strong stability and an optimized Na+ solvation structure can co‐embed the carbon layer to achieve fast reaction kinetics. Consequently, Sn@NC anodes deliver extra‐long cycling stability of more than 10 000 cycles. The full cell of Na3V2(PO4)3║Sn@NC exhibits high energy density (215 Wh kg−1), excellent high‐rate capability (reaches 80% capacity in 2 min), and long cycle life over a wide temperature range of −20 to 50 °C.
A numerical method based on the uniform and hexahedral grids generated from computational fluid dynamics is presented for the analysis of aero-optical performance. A single grid is taken as a cell with isotropy and homogeneity inside, and it is assumed that the light rays transmit grid by grid. Ray tracing is employed to track the transmission through the flow of supersonic fluids, and a recursive algorithm is derived. The line-of-sight errors and optical path differences produced by the mean density fields were calculated, the phase variances brought from the density fluctuations were computed, and the Strehl ratios were figured out. This method potentially provides a solution for the prediction of aero-optical effects.
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