Hypersonic Flight Dynamics Research Articles

Uncertainty analysis of hypersonic flight dynamics under elastic effects

Abstract In addressing the aerodynamic-elastic coupling issues of hypersonic vehicles, this study delves into the analysis of the elastic deformation of the aircraft based on mechanistic understanding. It employs the Interval Analysis Method based on Polynomial Approximations to conduct uncertainty analysis of the aerodynamic model under elasticity, delineating the range of uncertainty in the aerodynamic model influenced by elastic deformation. The aerodynamic force of the aircraft under elasticity is connected with the rigid body, and then the aerodynamic layout of the aircraft fuselage and control surface after elastic deformation is calculated to study the influence of the elastic effect on the aerodynamic force. The results show that the change caused by uncertainty has a relatively small effect on lift but a relatively large effect on drag and pitching moment.

Design and Evaluation of a Hypersonic Waverider Vehicle Using DSMC

This work investigates the aerodynamic performance of a hypersonic waverider designed to operate at Mach 7, focusing on optimizing its design through advanced computational methods. Utilizing the Direct Simulation Monte Carlo (DSMC) method, the three-dimensional flow field around the specifically designed waverider was simulated to understand the shock wave interactions and thermal dynamics at an altitude of 90 km. The computational approach included detailed meshing around the vehicle’s critical leading edges and the use of three-dimensional iso-surfaces of the Q-criterion to map out the shock and vortex structures accurately. Additional simulation results demonstrate that the waverider achieved a lift–drag ratio of 2.18, confirming efficient aerodynamic performance at a zero-degree angle of attack. The study’s findings contribute to the broader understanding of hypersonic flight dynamics, highlighting the importance of precise computational modeling in developing vehicles capable of operating effectively in near-space environments.

Adaptive control of unknown fuzzy disturbance-based uncertain nonlinear systems: application to hypersonic flight dynamics

Adaptive control of unknown fuzzy disturbance-based uncertain nonlinear systems: application to hypersonic flight dynamics

Neural Learning Control of Strict-Feedback Systems Using Disturbance Observer.

This paper studies the compound learning control of disturbed uncertain strict-feedback systems. The design is using the dynamic surface control equipped with a novel learning scheme. This paper integrates the recently developed online recorded data-based neural learning with the nonlinear disturbance observer (DOB) to achieve good "understanding" of the system uncertainty including unknown dynamics and time-varying disturbance. With the proposed method to show how the neural networks and DOB are cooperating with each other, one indicator is constructed and included into the update law. The closed-loop system stability analysis is rigorously presented. Different kinds of disturbances are considered in a third-order system as simulation examples and the results confirm that the proposed method achieves higher tracking accuracy while the compound estimation is much more precise. The design is applied to the flexible hypersonic flight dynamics and a better tracking performance is obtained.

DOB-Based Neural Control of Flexible Hypersonic Flight Vehicle Considering Wind Effects

This paper investigates the disturbance observer (DOB)-based neural adaptive control on the longitudinal dynamics of a flexible hypersonic flight vehicle (HFV) in the presence of wind effects. The coupling effect between flexible states and rigid body, and the accessional angle of attack (AOA) due to wind, is modeled as unknown disturbance, where the nonlinear DOB is constructed using the neural approximation. For the weight update in neural networks (NNs), a novel algorithm is proposed with the additional prediction error derived from the serial–parallel estimation model (SPEM) using both neural approximation and disturbance estimation. Different from previous work, the wind effect is taken into the hypersonic flight dynamics for realistic analysis, and the novel controller is designed using compound estimation, where the NN and the DOB are constructed to deal with aerodynamic uncertainty and unknown disturbance. Simulation studies of a flexible HFV with wind effects show that the proposed controller can achieve high tracking accuracy, while the compound estimation can closely follow the system uncertainty with fast convergence.

Online Recorded Data-Based Composite Neural Control of Strict-Feedback Systems With Application to Hypersonic Flight Dynamics.

This paper investigates the online recorded data-based composite neural control of uncertain strict-feedback systems using the backstepping framework. In each step of the virtual control design, neural network (NN) is employed for uncertainty approximation. In previous works, most designs are directly toward system stability ignoring the fact how the NN is working as an approximator. In this paper, to enhance the learning ability, a novel prediction error signal is constructed to provide additional correction information for NN weight update using online recorded data. In this way, the neural approximation precision is highly improved, and the convergence speed can be faster. Furthermore, the sliding mode differentiator is employed to approximate the derivative of the virtual control signal, and thus, the complex analysis of the backstepping design can be avoided. The closed-loop stability is rigorously established, and the boundedness of the tracking error can be guaranteed. Through simulation of hypersonic flight dynamics, the proposed approach exhibits better tracking performance.

Neural network based dynamic surface control of hypersonic flight dynamics using small-gain theorem

This paper analyzed the neural control for longitudinal dynamics of a generic hypersonic aircraft in presence of unknown dynamics and actuator fault. For the attitude subsystem, direct adaptive design is presented with the dynamic surface approach and the singularity problem is removed. For actuator fault, the unknown dynamics caused by fault is approximated by neural networks. The highlight is that the minimal-learning-parameter technique is applied on the dynamics and the simple adaptive algorithm is easy to implement since the online updating computation burden is greatly reduced. The uniformly ultimate boundedness stability is guaranteed via small-gain theorem. Simulation result shows that the controller could achieve good tracking performance with minimal learning parameter in case of actuator fault.

Neural control of hypersonic flight dynamics with actuator fault and constraint

This paper deals with the control problem of actuator fault and saturation for hypersonic flightvehicles. Different from previous back-stepping design, the scheme is on transforming the dynamics into the“prediction function”. The controller is constructed with high gain observer, while the effect of fault andsaturation is compensated by neural networks. For the input saturation, the auxiliary dynamics is included todesign the adaptive learning law. The neural weights and filtered tracking error are guaranteed to be boundedvia Lyapunov approach. The effectiveness of the proposed method is verified by simulation of winged-conemodel.

Composite dynamic surface control of hypersonic flight dynamics using neural networks

This paper addresses the composite neural tracking control for the longitudinal dynamics of hy-personic flight dynamics. The dynamics is decoupled into velocity subsystem, altitude subsystem, and attitude subsystem. For the altitude subsystem, the reference command of flight path angle is derived for the attitude subsystem. To deal with the system uncertainty and provide efficient neural learning, the composite law for neural weights updating is studied with both tracking error and modeling error. The uniformly ultimate bound-edness stability is guaranteed via Lyapunov approach. Under the dynamic surface control with novel neural design, the neural system converges in a faster mode and better tracking performance is obtained. Simulation results are presented to show the effectiveness of the design.

Minimal-learning-parameter technique based adaptive neural control of hypersonic flight dynamics without back-stepping

This paper investigates one robust adaptive controller for hypersonic flight dynamics. The altitude tracking is transformed into the control problem of the attitude subsystem, which is composed of flight path angle, pitch angle and pitch rate. Different from previous design using back-stepping related technique, this paper analyzed the tracking control without back-stepping where the controller is synthesized with high gain observer and minimal-learning-parameter technique. The highlight is that the design procedure is greatly simplified and the computation burden of parameter updating is reduced. It is proved that the filtered tracking error is guaranteed in the semiglobal sense. Simulation results are presented to demonstrate the effectiveness of the design.

An overview on flight dynamics and control approaches for hypersonic vehicles

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.

Polynomial Chaos-Based Analysis of Probabilistic Uncertainty in Hypersonic Flight Dynamics

In this paper, we present a novel computational framework for analyzing the evolution of the uncertainty in state trajectories of a hypersonic air vehicle due to the uncertainty in initial conditions and other system parameters. The framework is built on the so-called generalized polynomial chaos expansions. In this framework, stochastic dynamical systems are transformed into equivalent deterministic dynamical systems in higher dimensional space. Here, the evolution of uncertainty due to initial condition, ballistic coefficient, lift over drag ratio, and atmospheric density is analyzed. The problem studied here is related to the Mars entry, descent, and landing problems. We demonstrate that the polynomial chaos framework is able to predict evolution of uncertainty, in hypersonic flight, with the same order of accuracy as the Monte-Carlo methods but with more computational efficiency.