Abstract
Adaptive neural back-stepping control approach is presented for a generic air-breathing hypersonic vehicle (AHV) with prescribed performance. By prescribed performance, a novel performance function characterizing the error convergence rate, maximum overshoot and steady-state error is designed for the output error transformation without initial errors. In order to enhance the robustness of controller, the radial basis function neural networks are applied to approximate the lumped uncertainty of AHV non-affine dynamics model. To eliminate the “explosion of term” of back-stepping control strategy, tangent sigmoid tracking differentiators (TSTDs) are employed to obtain the derivatives of virtual control laws. Finally, the tracking performance the proposed control approach is testified by simulation results.
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Wang, P. (2022). Adaptive Neural Back-Stepping Control with Prescribed Performance for Air-Breathing Hypersonic Vehicles. In: Yan, L., Duan, H., Yu, X. (eds) Advances in Guidance, Navigation and Control . Lecture Notes in Electrical Engineering, vol 644. Springer, Singapore. https://doi.org/10.1007/978-981-15-8155-7_402
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DOI: https://doi.org/10.1007/978-981-15-8155-7_402
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