Novel prescribed performance neural control of a flexible air-breathing hypersonic vehicle with unknown initial errors

Bu, Xiangwei; Wu, Xiaoyan; Zhu, Fujing; Huang, Jiaqi; Ma, Zhen; Zhang, Rui

doi:10.1016/j.isatra.2015.09.007

Cited by 123 publications

(108 citation statements)

References 28 publications

Supporting

Mentioning

108

Contrasting

Order By: Relevance

“…To manage the problem of output constraint, a number of attempts regarding model predictive control [17], barrier Lyapunov function (BLF) [18][19][20][21][22][23][24], and prescribed performance control (PPC) [25][26][27][28] techniques have been investigated from both academia and industries. Using the property of BLF, a constrained back-stepping control approach is proposed for nonlinear strict-feedback systems to ensure that the static constraint is not transgressed [20].…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Constrained Adaptive Neural Control of Nonlinear Strict‐Feedback Systems with Input Dead‐Zone

Shi

2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

This paper focuses on a single neural network tracking control for a class of nonlinear strict-feedback systems with input dead-zone and time-varying output constraint via prescribed performance method. To release the limit condition on previous performance function that the initial tracking error needs to be known, a new modified performance function is first constructed. Further, to reduce the computational burden of traditional neural back-stepping control approaches which require all the virtual controllers to be necessarily carried out in each step, the nonlinear items are transmitted to the last step such that only one neural network is required in this design. By regarding the input-coefficients of the dead-zone slopes as a system uncertainty and introducing a new concise system transformation technique, a composite adaptive neural state-feedback control approach is developed. The most prominent feature of this scheme is that it not only owes low-computational property but also releases the previous limitations on performance function and is capable of guaranteeing the output confined within the new form of prescribed bound. Moreover, the closed-loop stability is proved using Lyapunov function. Comparative simulation is induced to verify the effectiveness.

show abstract

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Constrained Adaptive Neural Control of Nonlinear Strict‐Feedback Systems with Input Dead‐Zone

Shi

2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Neural network (NN) is employed to estimate such unknown dynamics while novel NN-based output-feedback controller with strong robustness is developed for AHVs. 17 Viewing each subsystem of AHV as a lumped unknown system, only one radial basis function (RBF) NN is needed to approach such unknown system 16 while the problem of ''explosion of differentiation'' that is encountered by Bu et al 18 is eliminated. Although the above works provide effective and efficient methodologies for the flight control design of AHVs, the following challenging problems have not been fully investigated.…”

Section: Introductionmentioning

confidence: 99%

Adaptive neural back-stepping control of flexible air-breathing hypersonic vehicles with parametric uncertainties

Lei

et al. 2018

Advances in Mechanical Engineering

Self Cite

View full text Add to dashboard Cite

Control system is significant for making flight safety. In this study, a novel adaptive neural back-stepping controller is exploited for the longitudinal dynamics of a flexible air-breathing hypersonic vehicle. A combined neural network approach and back-stepping scheme is utilized for developing an output-feedback controller that provides robust tracking of the velocity and altitude commands. For each subsystem, only one neural network is employed to approximate the lumped system uncertainty by updating its weight vector adaptively while the problem of possible control singularity is eliminated. The uniformly ultimately boundedness is guaranteed for the closed-loop control system by means of Lyapunov stability theory. The main contribution is that the design complexity is reduced and less neural networks are required. Finally, simulation results illustrate that the proposed control strategy achieves satisfying tracking performance in spite of flexible effects and system uncertainties.

show abstract

“…Although the previous adaptive neural control methods have the ability to guarantee the steady-state performance converging to a small residual set, few results consider the transient performance related to overshoot and convergence rate [21]. More recently, the prescribed performance control (PPC) scheme which denotes that the tracking error should converge to a predefined bound with convergence rate no less than a certain value has been an active research area [21][22][23][24][25][26][27][28][29][30]. Based on an error transformation technique that incorporates the desirable performance, Bechlioulis and Rovithakis firstly presented an adaptive PPC method for strict-feedback nonlinear systems [22].…”

Section: Introductionmentioning

confidence: 99%

“…Subsequently, the PPC approach has been extended to deal with MIMO state and output feedback nonlinear control problems [27][28][29][30]. Moreover, this tool has also been successfully applied to flight control area [24,31].…”

Section: Introductionmentioning

confidence: 99%

“…The MLP and FOSD are incorporated into the neural back-stepping design to reduce the online updating parameters of NN and to conquer the problem of "explosion of the items," thus deriving a lowcomputational scheme. (2) In contrast to traditional neural control schemes [21][22][23][24][26][27][28][29][30]] whose stability analysis relies on the condition that the NNs should always be kept within the neural active region, a smooth switching function based composite control scheme, presented to manipulate the exchange of control authorities between normal neural controller working in the active region and a robust controller out of this scope, is constructed to relax this constraint, and a new kind of adaptive laws is proposed. Note that it is also the first low-computational neural control scheme which can efficiently address the prescribed performance issue and relax the constraint on NN simultaneously.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust Adaptive Neural Control of Morphing Aircraft with Prescribed Performance

Shi

et al. 2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

This study proposes a low-computational composite adaptive neural control scheme for the longitudinal dynamics of a swept-back wing aircraft subject to parameter uncertainties. To efficiently release the constraint often existing in conventional neural designs, whose closed-loop stability analysis always necessitates that neural networks (NNs) be confined in the active regions, a smooth switching function is presented to conquer this issue. By integrating minimal learning parameter (MLP) technique, prescribed performance control, and a kind of smooth switching strategy into back-stepping design, a new composite switching adaptive neural prescribed performance control scheme is proposed and a new type of adaptive laws is constructed for the altitude subsystem. Compared with previous neural control scheme for flight vehicle, the remarkable feature is that the proposed controller not only achieves the prescribed performance including transient and steady property but also addresses the constraint on NN. Two comparative simulations are presented to verify the effectiveness of the proposed controller.

show abstract

Novel prescribed performance neural control of a flexible air-breathing hypersonic vehicle with unknown initial errors

Cited by 123 publications

References 28 publications

Constrained Adaptive Neural Control of Nonlinear Strict‐Feedback Systems with Input Dead‐Zone

Constrained Adaptive Neural Control of Nonlinear Strict‐Feedback Systems with Input Dead‐Zone

Adaptive neural back-stepping control of flexible air-breathing hypersonic vehicles with parametric uncertainties

Robust Adaptive Neural Control of Morphing Aircraft with Prescribed Performance

Contact Info

Product

Resources

About