A Novel SHLNN Based Robust Control and Tracking Method for Hypersonic Vehicle under Parameter Uncertainty

Li, Chuan‐Feng; Wu, Hao; Yang, Zhile; Wang, Yongji; Sun, Zeyu

doi:10.1155/2017/6034786

Cited by 4 publications

(4 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…NN is a simple abstraction of biological NN that stores function in the neurons and in the connections between them [50]. It learns to perform useful functions by training on data.…”

Section: B Neural Networkmentioning

confidence: 99%

Neural Network-Based Information Transfer for Dynamic Optimization

Liu

Zhan

et al. 2020

IEEE Trans. Neural Netw. Learning Syst.

View full text Add to dashboard Cite

In dynamic optimization problems (DOPs), as the environment changes through time, the optima also dynamically change. How to adapt to the dynamic environment and quickly find the optima in all environments is a challenging issue in solving DOPs. Usually, a new environment is strongly relevant to its previous environment. If we know how it changes from the previous environment to the new one, then we can transfer the information of the previous environment, e.g., past solutions, to get new promising information of the new environment, e.g., new high-quality solutions. Thus, in this paper, we propose a neural network (NN)-based information transfer method, named NNIT, to learn the transfer model of environment changes by NN and then use the learned model to reuse the past solutions. When the environment changes, NNIT first collects the solutions from both the previous environment and the new environment and then uses an NN to learn the transfer model from these solutions. After that, the NN is used to transfer the past solutions to new promising solutions for assisting the optimization in the new environment. The proposed NNIT can be incorporated into population-based evolutionary algorithms (EAs) to solve DOPs. Several typical state-of-theart EAs for DOPs are selected for comprehensive study and evaluated using the widely used moving peaks benchmark. The experimental results show that the proposed NNIT is promising and can accelerate algorithm convergence.

show abstract

“…NN is a simple abstraction of biological NN that stores function in the neurons and in the connections between them [50]. It learns to perform useful functions by training on data.…”

Section: B Neural Networkmentioning

confidence: 99%

Neural Network-Based Information Transfer for Dynamic Optimization

Liu

Zhan

et al. 2020

IEEE Trans. Neural Netw. Learning Syst.

View full text Add to dashboard Cite

show abstract

“…where k b2 is a positive constant. Differentiating equation (14) and according to equation 7, we have…”

Section: Control Of the Pitch Channelmentioning

confidence: 99%

“…Meanwhile, the intelligence-based control method has become an active research topic. In the literature, 14,15 the main idea is to use a fuzzy logic system FLS/NN for nonlinearity approximation, which can provide the feedback information. In this way, the controller design can be simplified since the nonlinearity is not required to be known exactly.…”

Section: Introductionmentioning

confidence: 99%

Robust attitude predictive control design for hypersonic vehicles with state constraints

Xingjie

Guo

Zhou

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part G:

View full text Add to dashboard Cite

In this paper, a novel robust predictive controller based on the tangent barrier Lyapunov function (BLF-Tan) is proposed for the attitude tracking problem of the hypersonic vehicles with full state constraints. The finite-time nonlinear disturbance observer is involved to improve the predictive controller’s property of disturbance rejection. It is strictly proved that with the proposed approach, all state constraints are not violated, all signals in the closed-loop systems are bounded and asymptotical tracking is achieved. Furthermore, the relationship among state constraints, initial state conditions, tracking performance and controller parameters is analyzed, which provides a guideline for the selection of controller parameters. And the solvability analysis of the constrained tracking problem provides a guideline for the selection of desired states trajectories. Finally, the numerical simulation shows that the proposed method for hypersonic vehicles can guarantee all state constraints are not violated compared with the classical predictive controller, and also has fast convergence rate and enough robustness.

show abstract

“…To this end, a comprehensive analytical model of waverider was developed in [3] using Newtonian impact theory to estimate the pressures that act on the vehicle. Based on the integration of computational fluid dynamics (CFD) and analytical techniques, the stability and control derivatives were determined in [4] to build a model of the longitudinal dynamics of the waverider and investigate the propulsion/airframe integration. A first principlebased dynamic model of the waverider was provided in [5] to incorporate aero-thermo-elastic-propulsion interactions: this model applied inviscid compressible oblique shock-expansion theory to estimate aerodynamic forces and moments, as well as a 1D Rayleigh flow model and an Euler-Bernoulli beam model determined scramjet propulsion and structural flexibility, respectively.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Trajectory Controller with Improved Performances for Waveriders

2019

View full text Add to dashboard Cite

This paper presents a nonlinear trajectory controller with improved performances for a general model of the waverider based on feedback linearization theory and composite nonlinear feedback (CNF) technique. First, a nonlinear controller is presented using the dynamic inversion and CNF technique for the MIMO Model, and the robust stability of the proposed controller is proved. Then, the nonlinear model is established on the basis of hypersonic aerodynamic principle, and the dynamic characteristics are analyzed accordingly, and the periodic trajectory is designed and optimized in combination with a fuel optimization problem. Furthermore, the nonlinear controller is applied to the trajectory tracking of the waverider model, and the general design steps are provided the flight controller using this nonlinear control method. Finally, an illustrative example is given to verify the effectiveness of the nonlinear controller of the waverider, and the flight performances are improved accordingly, including system stability, robustness, and tracking ability.

show abstract

A Novel SHLNN Based Robust Control and Tracking Method for Hypersonic Vehicle under Parameter Uncertainty

Cited by 4 publications

References 35 publications

Neural Network-Based Information Transfer for Dynamic Optimization

Neural Network-Based Information Transfer for Dynamic Optimization

Robust attitude predictive control design for hypersonic vehicles with state constraints

Nonlinear Trajectory Controller with Improved Performances for Waveriders

Contact Info

Product

Resources

About