High-order tracking differentiator based adaptive neural control of a flexible air-breathing hypersonic vehicle subject to actuators constraints

Bu, Xiangwei; Wu, Xiaoyan; Tian, Mingyan; Huang, Jiaqi; Zhang, Rui; Ma, Zhen

doi:10.1016/j.isatra.2015.06.004

Cited by 59 publications

(42 citation statements)

References 25 publications

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“…Considering the altitude subsystem (7), if the adaptive neural PPC laws are chosen as equations (22), (32), (45), and (53), and updated laws as (33), (34), (54), (55) as well as the parameters satisfied (65). Thus, the boundness of i¼1;2;3;4 ,' i¼2;4 andd i¼2;4 can be ensured.…”

Section: By Substituting Equation (45) Into Equation (43) We Havementioning

confidence: 99%

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

Shi

et al. 2017

International Journal of Advanced Robotic Systems

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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.

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Section: By Substituting Equation (45) Into Equation (43) We Havementioning

confidence: 99%

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

Shi

et al. 2017

International Journal of Advanced Robotic Systems

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show abstract

“…It is obvious that the estimation errors e i i = 1, 2, 3, 4 can converge to zero by designing an adequately large R. In choosing an infinitely large but bounded R for the THD in simulation study, we know that there exist positive constants e i i = 1, 2, 3, 4 such that e i ≥ e i i = 1, 2, 3, 4 [23].…”

Section: High-order Tracking Differentiatormentioning

confidence: 99%

“…Based on the results of [21], a new NDO is constructed, which guarantees that the proposed backstepping controller can provide robust tracking of velocity and altitude reference trajectories in the presence of parametric uncertainties and external disturbances [19]. It has been demonstrated that neural networks (NNs) have the ability of global approximation [22][23][24][25]. Thus, improved backstepping control methodologies with robust performance can be achieved if the unknown nonlinearities are approximated by NNs [26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Concise Neural Nonaffine Control of Air-Breathing Hypersonic Vehicles Subject to Parametric Uncertainties

Bu¹,

Wang²,

Zhao³

et al. 2017

International Journal of Aerospace Engineering

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In this paper, a novel simplified neural control strategy is proposed for the longitudinal dynamics of an air-breathing hypersonic vehicle (AHV) directly using nonaffine models instead of affine ones. For the velocity dynamics, an adaptive neural controller is devised based on a minimal-learning parameter (MLP) technique for the sake of decreasing computational loads. The altitude dynamics is rewritten as a pure feedback nonaffine formulation, for which a novel concise neural control approach is achieved without backstepping. The special contributions are that the control architecture is concise and the computational cost is low. Moreover, the exploited controller possesses good practicability since there is no need for affine models. The semiglobally uniformly ultimate boundedness of all the closed-loop system signals is guaranteed via Lyapunov stability theory. Finally, simulation results are presented to validate the effectiveness of the investigated control methodology in the presence of parametric uncertainties.

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“…However, due to the change of switching function, the local signal quality cannot be guaranteed. A fourth-order tracking differentiator is introduced to adaptively estimate system states in [18]. In [19], a fuzzy variable parameter is proposed to adjust the parameter of tracking differentiator.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Self-tuning Tracking Differentiator for Motion Measurement Sensors and Application in Wide-Bandwidth High-accuracy Servo Control

Gao

Tian

Wang

2020

Sensors

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Sensor differential signals are widely used in many systems. The tracking differentiator (TD) is an effective method to obtain signal differentials. Differential calculation is noise-sensitive. There is the characteristics of low-pass filter (LPF) in the TD to suppress the noise, but phase lag is introduced. For LPF, fixed filtering parameters cannot achieve both noise suppression and phase compensation lag compensation. We propose a fuzzy self-tuning tracking differentiator (FSTD) capable of adaptively adjusting parameters, which uses the frequency information of the signal to achieve a trade-off between the phase lag and noise suppression capabilities. Based on the frequency information, the parameters of TD are self-tuning by a fuzzy method, which makes self-tuning designs more flexible. Simulations and experiments using motion measurement sensors show that the proposed method has good filtering performance for low-frequency signals and improves tracking ability for high-frequency signals compared to fixed-parameter differentiator.

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High-order tracking differentiator based adaptive neural control of a flexible air-breathing hypersonic vehicle subject to actuators constraints

Cited by 59 publications

References 25 publications

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

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

Concise Neural Nonaffine Control of Air-Breathing Hypersonic Vehicles Subject to Parametric Uncertainties

Fuzzy Self-tuning Tracking Differentiator for Motion Measurement Sensors and Application in Wide-Bandwidth High-accuracy Servo Control

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