With the capability of high speed flying, a more reliable and cost efficient way to access space is provided by hypersonic flight vehicles. Controller design, as key technology to make hypersonic flight feasible and efficient, has numerous challenges stemming from large flight envelope with extreme range of operation conditions, strong interactions between elastic airframe, the propulsion system and the structural dynamics. This paper briefly presents several commonly studied hypersonic flight dynamics such as winged-cone model, truth model, curve-fitted model, control oriented model and re-entry motion. In view of different schemes such as linearizing at the trim state, input-output linearization, characteristic modeling, and back-stepping, the recent research on hypersonic flight control is reviewed and the comparison is presented. To show the challenges for hypersonic flight control, some specific characteristics of hypersonic flight are discussed and the potential future research is addressed with dealing with actuator dynamics, aerodynamic/reaction-jet control, flexible effects, non-minimum phase problem and dynamics interaction.

An overview on flight dynamics and control approaches for hypersonic vehicles

This paper studies the problem of prescribed performance adaptive neural control for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems in the presence of external disturbances and input saturation based on a disturbance observer. The system uncertainties are tackled by neural network (NN) approximation. To handle unknown disturbances, a Nussbaum disturbance observer is presented. By incorporating the disturbance observer and NNs, an adaptive prescribed performance neural control scheme is further developed. Then, the expected asymptotically convergent tracking errors between system output signals and desired signals are achieved. Numerical simulation results demonstrate the effectiveness of the proposed control scheme.

Adaptive Neural Control of Uncertain Nonlinear Systems Using Disturbance Observer

An adaptive PID neural network for complex nonlinear system control

Summary

In this paper, an actuator robust fault-tolerant control is proposed for ocean surface vessels with parametric uncertainties and unknown disturbances. Using the backstepping technique and Lyapunov synthesis method, the adaptive tracking control is first developed by incorporating the actuator configuration matrix and considering actuator saturation constraints. The changeable actuator configuration matrix caused by rotatable propulsion devices is considered. Next, the actuator fault-tolerant control is developed for the case when faults occur in propulsion devices of the ocean surface vessel. Rigorous stability analysis is carried out to show that the proposed fault-tolerant control can guarantee the stability of the closed-loop system under certain actuator failure. Finally, simulation studies are given to illustrate the effectiveness of the proposed adaptive tracking control and fault-tolerant control. Copyright © 2015 John Wiley & Sons, Ltd.

Actuator fault-tolerant control of ocean surface vessels with input saturation

In this paper, we aim to solve the optimal tracking control problem for the Henon Mapping chaotic system using the direct heuristic dynamic programming (DHDP) setting with filtered tracking error. The fuzzy logic system is used to approximate the long-term utility function. Compared with the results for chaotic discrete-time system, the cost of the controller is reduced. The Lyapunov analysis approach is utilized to prove the stability of the chaotic system. It is shown that the tracking error, the adaptation law and the control input retain the property of uniformly ultimate boundedness. A simulation example is given to demonstrate the effectiveness of the proposed approach.

Adaptive fuzzy optimal control using direct heuristic dynamic programming for chaotic discrete-time system

In this paper, a robust adaptive neural network synchronization controller is proposed for two chaotic systems with input time delay and uncertainty. The studied chaotic system may possess a wide class of nonlinear time-delayed input uncertainty. The radial basis function (RBF) neural network is used to approximate the unknown continuous bounded function item of the time delay uncertainty via appropriate weight value updated law. With the output of RBF neural network, a robust adaptive synchronization control scheme is presented for the time delay uncertain chaotic system. Finally, a simulation example is used to illustrate the effectiveness of the proposed synchronization control scheme.

Robust adaptive neural network synchronization controller design for a class of time delay uncertain chaotic systems

In this paper, an adaptive L2 -gain control scheme is presented for a class of uncer- tain nonlinear system with backstepping method and neural network. Based on the output of neural network, the given controller can not only guarantee robust stability of the closed loop system, but also make it has L2-gain performance index which less than or equal to ∞ > 0.

/pdf/backstepping-control-for-a-class-of-uncertain-nonlinear-54gc4doqgs.pdf

Backstepping Control for a Class of Uncertain Nonlinear Systems with Neural Network

Disturbance-Observer-Based Robust Flight Control for Hypersonic Vehicles Using Neural Networks

A decentralized feedback control scheme is proposed to synchronize linearly coupled identical neural networks with time-varying delay and parameter uncertainties. Sufficient condition for synchronization is developed by carefully investigating the uncertain nonlinear synchronization error dynamics in this article. A procedure for designing a decentralized synchronization controller is proposed using linear matrix inequality (LMI) technique. The designed controller can drive the synchronization error to zero and overcome disruption caused by system uncertainty and external disturbance.

Maintaining synchronization by decentralized feedback control in time delay neural networks with parameter uncertainties.

In this paper, a robust adaptive feedback controller is proposed based on backstepping method and neural network for a class of uncertain nonlinear systems with deadzone. The subsystem uncertainty is approximated using radial basis function (RBF) neural network and weight value update law is given for approximating the subsystem uncertainty. Based on the output of the neural network, the robust adaptive control scheme is presented with backstepping method. The designed controller can not only guarantee robust stability of the uncertain nonlinear system, but also make it has L2-gain performance index which less than or equal to Gt 0.

Adaptive Feedback Control for a Class of Uncertain Nonlinear Systems with Dead-Zone

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