A guaranteed transient performance-based adaptive neural control scheme with low-complexity computation for flexible air-breathing hypersonic vehicles

2016 ◽  
Vol 84 (4) ◽  
pp. 2175-2194 ◽  
Author(s):  
Xiangwei Bu ◽  
Xiaoyan Wu ◽  
Jiaqi Huang ◽  
Daozhi Wei
Keyword(s):  
Neural Control ◽  
Low Complexity ◽  
Hypersonic Vehicles ◽  
Transient Performance ◽  
Adaptive Neural Control ◽  
Air Breathing ◽  
Control Scheme

scholarly journals Constrained Adaptive Neural Control for Air-Breathing Hypersonic Vehicles without Backstepping

2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Shili Tan ◽  
Humin Lei ◽  
Pengfei Wang
Keyword(s):  
Neural Control ◽  
Control Structure ◽  
Parameter Tuning ◽  
Auxiliary System ◽  
Hypersonic Vehicles ◽  
Input Constraints ◽  
Adaptive Neural Control ◽  
Air Breathing ◽  
Order Form ◽  
Control Scheme

An adaptive neural control scheme without backstepping is proposed for the air-breathing hypersonic vehicle subject to input constraints. To estimate the unknown nonlinearity of velocity subsystem and altitude subsystem, two radial basis function neural networks (RBFNNs) are constructed. Since the complex backstepping design steps are not needed, the proposed control structure is quite concise and the problem of “explosion of terms” is avoided. Moreover, a novel nonlinear auxiliary system is constructed to solve the problem of input constraints. The advantage of the proposed auxiliary system is that its high-order form has good performance and the parameter tuning is relatively easy. Simulation results show that the designed controllers achieve stable tracking of reference commands with good performance.

scholarly journals The Adaptive Neural Control for a Class of High-Order Uncertain Stochastic Nonlinear Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Xiaoyan Qin
Keyword(s):  
Nonlinear Systems ◽  
Neural Control ◽  
Closed Loop ◽  
Control Method ◽  
High Order ◽  
Stochastic Nonlinear Systems ◽  
Closed Loop System ◽  
Adaptive Neural Control ◽  
Control Scheme ◽  
Approximation Capability

This paper studies the problem of the adaptive neural control for a class of high-order uncertain stochastic nonlinear systems. By using some techniques such as the backstepping recursive technique, Young’s inequality, and approximation capability, a novel adaptive neural control scheme is constructed. The proposed control method can guarantee that the signals of the closed-loop system are bounded in probability, and only one parameter needs to be updated online. One example is given to show the effectiveness of the proposed control method.

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 Fuzzy-approximation-based prescribed performance control of air-breathing hypersonic vehicles with input constraints

2019 ◽  
Vol 103 (1) ◽  
pp. 003685041987735
Author(s):  
Xingge Li ◽  
Gang Li ◽  
Yan Zhao ◽  
Xuchao Kang
Keyword(s):  
Control Method ◽  
Hypersonic Vehicle ◽  
Hypersonic Vehicles ◽  
Input Constraints ◽  
Air Breathing ◽  
Performance Control ◽  
Prescribed Performance ◽  
Fuzzy Approximation ◽  
Prescribed Performance Control ◽  
Control Scheme

In this article, aiming at the longitudinal dynamics model of air-breathing hypersonic vehicles, a fuzzy-approximation-based prescribed performance control scheme with input constraints is proposed. First, this article presents a novel prescribed performance function, which does not depend on the sign of initial tracking error. And combining prescribed performance control method with backstepping control, the control scheme can ensure that system can converge at a prescribed rate of convergence, overshoot, and steady-state error. In order to solve the problem that backstepping control method needs to be differentiated multiple times, fuzzy approximators are used to estimate the unknown functions, and norm estimation approach is used to simplify the computation of fuzzy approximator. Aiming at the problem of input saturation of actuator in subsystem of air-breathing hypersonic vehicle, the new auxiliary system is designed to ensure the stability and robustness of air-breathing hypersonic vehicle system under input constraints. Finally, the effectiveness of the proposed control strategy is verified by simulation analysis.

A direct adaptive neural control scheme with integral terms

2004 ◽  
Vol 20 (2) ◽  
pp. 213-224 ◽  
Author(s):  
Ieroham S. Baruch ◽  
Ruben Garrido
Keyword(s):  
Neural Control ◽  
Adaptive Neural Control ◽  
Control Scheme

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 Adaptive Neural Control for a Class of Outputs Time-Delay Nonlinear Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Ruliang Wang ◽  
Jie Li
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Nonlinear Systems ◽  
Neural Control ◽  
Closed Loop ◽  
Continuous Functions ◽  
Rbf Neural Networks ◽  
Adaptive Neural Control ◽  
Control Scheme ◽  
Simulation Results

This paper considers an adaptive neural control for a class of outputs time-delay nonlinear systems with perturbed or no. Based on RBF neural networks, the radius basis function (RBF) neural networks is employed to estimate the unknown continuous functions. The proposed control guarantees that all closed-loop signals remain bounded. The simulation results demonstrate the effectiveness of the proposed control scheme.

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