Neural Networks Approximator Based Robust Adaptive Controller Design of Hypersonic Flight Vehicles Systems Coupled with Stochastic Disturbance and Dynamic Uncertainties

Zhu, Guoqiang; Sun, Lingfang; Zhang, Xiuyu

doi:10.1155/2017/7864375

Cited by 3 publications

(1 citation statement)

References 31 publications

(37 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [22], a second-order nonsingular terminal sliding mode control approach was developed for a hypersonic vehicle by using recurrent neural networks to approximate the parameter uncertainties and external disturbances. Zhu et al [23] proposed a robust adaptive control strategy for a hypersonic flight vehicle subject to stochastic disturbances and dynamic uncertainties based on neural network approximation. In [24], radial basis function neural networks are utilized to estimate the unknown additive faults and external disturbances of the hypersonic vehicle.…”

Section: Introductionmentioning

confidence: 99%

Chebyshev Neural Network-Based Adaptive Nonsingular Terminal Sliding Mode Control for Hypersonic Vehicles

Zhang

Chen

Guo

2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

This paper presents an adaptive nonsingular terminal sliding mode control approach for the attitude control of a hypersonic vehicle with parameter uncertainties and external disturbances based on Chebyshev neural networks (CNNs). First, a new nonsingular terminal sliding surface is proposed for a general uncertain nonlinear system. Then, a nonsingular sliding mode control is designed to achieve finite-time tracking control. Furthermore, to relax the requirement for the upper bound of the lumped uncertainty including parameter uncertainties and external disturbances, a CNN is used to estimate the lumped uncertainty. The network weights are updated by the adaptive law derived from the Lyapunov theorem. Meanwhile, a low-pass filter-based modification is added into the adaptive law to achieve fast and low-frequency adaptation when using high-gain learning rates. Finally, the proposed approach is applied to the attitude control of the hypersonic vehicle and simulation results illustrate its effectiveness.

show abstract

Section: Introductionmentioning

confidence: 99%

Chebyshev Neural Network-Based Adaptive Nonsingular Terminal Sliding Mode Control for Hypersonic Vehicles

Zhang

Chen

Guo

2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

Extended Statement of the Optimal Control Problem and Machine Learning Approach to Its Solution

Shmalko

Diveev

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Engineers strive to realize the goal of control in the best possible way based on a given quality criterion when developing control system. The well-known optimal control problem requires finding a solution as a function of time. Such a function cannot be directly used to control a real object because its application corresponds to an open-loop control system and engineers usually complete it with a feedback stabilization system. Hence, there is an obvious need to reformulate the optimal control problem so that its solution can be directly applied to real objects. The paper presents a new extended statement of the optimal control problem. An additional requirement for the control function is introduced to give the system describing the control object properties that will ensure the stability of solutions. The desired control function must provide for the optimal trajectory given the properties of the attractor in the neighbourhood. The solution to the extended optimal control problem can be directly used to control a real object. The paper presents a computational machine learning approach to solving the extended problem of optimal control based on the application of a synthesized optimal control technique. Examples of the practical solution to the stated problem are given to illustrate the efficiency of the approach, where the solution to the conventional optimal control problem is compared with the proposed extended one in the presence of perturbations in models and initial conditions.

show abstract

Intelligent Trajectory Prediction Algorithm for Reentry Glide Target Based on Intention Inference

Zhou

Shao

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

Aimed at the problem of the insufficient utilization of intention information and flight data for the trajectory prediction of a reentry glide target, an intelligent trajectory prediction algorithm based on intention inference was proposed. Firstly, a control parameter prediction network was developed to predict the variation of the control parameter values. Secondly, based on the Gaussian mixture model and Dubins circle, the importance of strategic places was modeled, the functional relationship between the strategic places and trajectories was established, and the influence factors of target threat were analyzed. Subsequently, an intention inference network was designed, which can accurately identify the key point the target intends to attack and realize the nonlinear calculation of the target’s threat value. Finally, according to the common guidance law of reentry glide target, the lateral sign-variation rule was designed to carry out the target trajectory prediction based on intention inference. Simulation revealed that the parameter prediction network designed in this paper can realize the modification of filtering control parameters and effectively predict the value of these parameters. Moreover, taking the result of intention inference network, trajectory prediction in different prediction cases was accomplished. The maximal error of spatial distance (MESD) of the proposed method was less than 9 km when the prediction time was 100 s, and the best result was obtained in the long-term prediction. Compared with other mainstream prediction methods, the proposed method obtained the best prediction accuracy.

show abstract

Neural Networks Approximator Based Robust Adaptive Controller Design of Hypersonic Flight Vehicles Systems Coupled with Stochastic Disturbance and Dynamic Uncertainties

Cited by 3 publications

References 31 publications

Chebyshev Neural Network-Based Adaptive Nonsingular Terminal Sliding Mode Control for Hypersonic Vehicles

Chebyshev Neural Network-Based Adaptive Nonsingular Terminal Sliding Mode Control for Hypersonic Vehicles

Extended Statement of the Optimal Control Problem and Machine Learning Approach to Its Solution

Intelligent Trajectory Prediction Algorithm for Reentry Glide Target Based on Intention Inference

Contact Info

Product

Resources

About