scholarly journals Tracking error constrained robust adaptive neural prescribed performance control for flexible hypersonic flight vehicle

2017 ◽  
Vol 14 (1) ◽  
pp. 172988141668270 ◽  
Author(s):  
Zhonghua Wu ◽  
Jingchao Lu ◽  
Jingping Shi ◽  
Qing Zhou ◽  
Xiaobo Qu
Keyword(s):  
Neural Control ◽  
Tracking Error ◽  
Flight Vehicle ◽  
Model Uncertainties ◽  
Adaptive Neural Control ◽  
Prescribed Performance ◽  
Hypersonic Flight Vehicle ◽  
Hypersonic Flight ◽  
Control Scheme ◽  
Robust Adaptive

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.

scholarly journals Neural Network-Based Adaptive Backstepping Control for Hypersonic Flight Vehicles with Prescribed Tracking Performance

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Zhu Guoqiang ◽  
Liu Jinkun
Keyword(s):  
Neural Control ◽  
Closed Loop ◽  
Tracking Error ◽  
Closed Loop System ◽  
Adaptive Backstepping ◽  
Adaptive Neural Control ◽  
Hypersonic Flight ◽  
Flight Vehicles ◽  
Control Scheme ◽  
Constraint Variable

An adaptive neural control scheme is proposed for a class of generic hypersonic flight vehicles. The main advantages of the proposed scheme include the following: (1) a new constraint variable is defined to generate the virtual control that forces the tracking error to fall within prescribed boundaries; (2) RBF NNs are employed to compensate for complex and uncertain terms to solve the problem of controller complexity; (3) only one parameter needs to be updated online at each design step, which significantly reduces the computational burden. It is proved that all signals of the closed-loop system are uniformly ultimately bounded. Simulation results are presented to illustrate the effectiveness of the proposed scheme.

scholarly journals Robust Adaptive Neural Control of Morphing Aircraft with Prescribed Performance

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Zhonghua Wu ◽  
Jingchao Lu ◽  
Jingping Shi ◽  
Yang Liu ◽  
Qing Zhou
Keyword(s):  
Neural Control ◽  
Switching Function ◽  
Parameter Uncertainties ◽  
Adaptive Neural Control ◽  
Remarkable Feature ◽  
Performance Control ◽  
Prescribed Performance ◽  
Prescribed Performance Control ◽  
Control Scheme ◽  
Smooth Switching

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.

scholarly journals Prescribed Performance Tracking Control for Hypersonic Flight Vehicles with Model Uncertainties

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Siyuan Zhao ◽  
Xiaobing Li ◽  
Xiangwei Bu ◽  
Dongyang Zhang
Keyword(s):  
Tracking Control ◽  
Tracking Error ◽  
Longitudinal Motion ◽  
Model Uncertainties ◽  
Prescribed Performance ◽  
Hypersonic Flight ◽  
Flight Vehicles ◽  
Performance Tracking ◽  
Control Objective ◽  
Proportional Integral Controller

This paper proposes a novel prescribed performance tracking control for a hypersonic flight vehicle (HFV) with model uncertainties. Firstly, a HFV longitudinal motion model is decomposed into a velocity subsystem and an altitude subsystem. Meanwhile, considering the uncertainties of the model, the velocity subsystem and altitude subsystem are directly expressed as the forms with unknown nonaffine functions. Secondly, a novel performance function without initial error is proposed for limiting the tracking error into a prescribed range. Then, for the altitude subsystem, the control objective is changed by model transformation and the prescribed performance backstepping controller is designed. For the velocity subsystem, a prescribed performance proportional-integral controller is proposed which has better engineering practicability. The designed controller is not only simple in form but also has few calculating parameters. Finally, the simulation results show that the proposed controller has good practicability.

scholarly journals Disturbance observer-based L1 robust tracking control for hypersonic vehicles with T-S disturbance modeling

2016 ◽  
Vol 13 (6) ◽  
pp. 172988141667111 ◽  
Author(s):  
Yang Yi ◽  
Lubing Xu ◽  
Hong Shen ◽  
Xiangxiang Fan
Keyword(s):  
Disturbance Observer ◽  
Tracking Error ◽  
Flight Vehicle ◽  
Longitudinal Models ◽  
Robust Tracking Control ◽  
Hypersonic Flight Vehicle ◽  
Hypersonic Flight ◽  
Flight Vehicles ◽  
Unknown Disturbances ◽  
Exogenous Disturbances

This article concerns a disturbance observer-based L1 robust anti-disturbance tracking algorithm for the longitudinal models of hypersonic flight vehicles with different kinds of unknown disturbances. On one hand, by applying T-S fuzzy models to represent those modeled disturbances, a disturbance observer relying on T-S disturbance models can be constructed to track the dynamics of exogenous disturbances. On the other hand, L1 index is introduced to analyze the attenuation performance of disturbance for those unmodeled disturbances. By utilizing the existing convex optimization algorithm, a disturbance observer-based proportional-integral-controlled input is proposed such that the stability of hypersonic flight vehicles can be ensured and the tracking error for velocity and altitude in hypersonic flight vehicle models can converge to equilibrium point. Furthermore, the satisfactory disturbance rejection and attenuation with L1 index can be obtained simultaneously. Simulation results on hypersonic flight vehicle models can reflect the feasibility and effectiveness of the proposed control algorithm.

scholarly journals Tracking performance and global stability guaranteed neural control of uncertain hypersonic flight vehicle

2016 ◽  
Vol 14 (1) ◽  
pp. 172988141667814 ◽  
Author(s):  
Tao Teng ◽  
Chenguang Yang ◽  
Shi-Lu Dai ◽  
Min Wang
Keyword(s):  
Neural Control ◽  
Transient State ◽  
Dynamic Surface Control ◽  
Tracking Performance ◽  
Flight Vehicle ◽  
State Variables ◽  
Transformation Mechanism ◽  
Dynamic Surface ◽  
Hypersonic Flight Vehicle ◽  
Hypersonic Flight

In this article, a global adaptive neural dynamic surface control with predefined tracking performance is developed for a class of hypersonic flight vehicles, whose accurate dynamics is hard to obtain. The control scheme developed in this paper overcomes the limitations of neural approximation region by employing a switching mechanism which incorporates an additional robust controller outside the neural approximation region to pull the transient state variables back when they overstep the neural approximation region, such that globally uniformly ultimately bounded stability can be guaranteed. Especially, the developed global adaptive neural control also improves the tracking performance by introducing an error transformation mechanism, such that both transient and steady-state performance can be shaped according to the predefined bounds. Simulation studies on the hypersonic flight vehicle validate that the designed controller has good velocity modulation and velocity stability performance.

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