Abstract
A robust adaptive neural control scheme based on a back-stepping technique is developed for the longitudinal dynamics of a flexible hypersonic flight vehicle, which is able to ensure the state tracking error being confined in the prescribed bounds, in spite of the existing model uncertainties and actuator constraints. Minimal learning parameter technique–based neural networks are used to estimate the model uncertainties; thus, the amount of online updated parameters is largely lessened, and the prior information of the aerodynamic parameters is dispensable. With the utilization of an assistant compensation system, the problem of actuator constraint is overcome. By combining the prescribed performance function and sliding mode differentiator into the neural back-stepping control design procedure, a composite state tracking error constrained adaptive neural control approach is presented, and a new type of adaptive law is constructed. As compared with other adaptive neural control designs for hypersonic flight vehicle, the proposed composite control scheme exhibits not only low-computation property but also strong robustness. Finally, two comparative simulations are performed to demonstrate the robustness of this neural prescribed performance controller.
Highlights
Research effort of hypersonic flight vehicle (HFV) has drawn considerable attention during the past several years, because it can provide large time reductions within both civil and military flight activities.[1,2,3] The success of the experimental aircraft NASA’s X-43A has affirmed the feasibility of this technique.[3]
The control of HFV is still confronted with a large amount of intractable issues, such as the famous vibrational effects caused by slender geometry and special structures of HFV, intensely coupling between engine system and aerodynamic force and the variation of vehicle characteristics with different flight conditions.[4]
Compared to previous constraint control schemes,[36,37] a composite adaptive neural prescribed performance control (PPC) method, which is capable of handling actuator and state constraints as well as guaranteeing prescribed performance on transient and steady-state behaviour of the output tracking errors, is first presented, and a new type of adaptive law is constructed by synthesizing the PPC and minimal learning parameter (MLP) technique in the back-stepping design procedures
Summary
Research effort of hypersonic flight vehicle (HFV) has drawn considerable attention during the past several years, because it can provide large time reductions within both civil and military flight activities.[1,2,3] The success of the experimental aircraft NASA’s X-43A has affirmed the feasibility of this technique.[3]. 2. Compared to previous constraint control schemes,[36,37] a composite adaptive neural PPC method, which is capable of handling actuator and state constraints as well as guaranteeing prescribed performance on transient and steady-state behaviour of the output tracking errors, is first presented, and a new type of adaptive law is constructed by synthesizing the PPC and MLP technique in the back-stepping design procedures. The proposed controller is able to ensure the state tracking errors confined in the desired performance sets and owns low-computation since there is only one parameter online to be adjusted for each NNs. where V is an assistant signal to compensate the saturation effect, and the additional auxiliary system is constructed as follows.
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