Jiaqi Huang

Tracking differentiator design for the robust backstepping control of a flexible air-breathing hypersonic vehicle

Abstract This paper is concerned with the robust backstepping controller design for a flexible air-breathing hypersonic vehicle. Due to the extreme complexity of the vehicle dynamics, only the longitudinal model is adopted and rewritten as a feedback form for the backstepping design. Then, a new tracking differentiator (TD) is designed based on hyperbolic sine function to solve the problem of “explosion of term” in the traditional backstepping control. Furthermore, to enhance the controller׳s robustness, a new nonlinear disturbance observer is constructed using the proposed TD to estimate the model uncertainties and varying disturbances. More specially, owing to the measurement difficulties of angle of attack and flight-path angle in practice, the developed TD is utilized to reconstruct them based on the measurable states. Finally, several numerical simulations are given to demonstrate the effectiveness of the proposed control strategy.

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

In this paper, an adaptive neural controller is exploited for a constrained flexible air-breathing hypersonic vehicle (FAHV) based on high-order tracking differentiator (HTD). By utilizing functional decomposition methodology, the dynamic model is reasonably decomposed into the respective velocity subsystem and altitude subsystem. For the velocity subsystem, a dynamic inversion based neural controller is constructed. By introducing the HTD to adaptively estimate the newly defined states generated in the process of model transformation, a novel neural based altitude controller that is quite simpler than the ones derived from back-stepping is addressed based on the normal output-feedback form instead of the strict-feedback formulation. Based on minimal-learning parameter scheme, only two neural networks with two adaptive parameters are needed for neural approximation. Especially, a novel auxiliary system is explored to deal with the problem of control inputs constraints. Finally, simulation results are presented to test the effectiveness of the proposed control strategy in the presence of system uncertainties and actuators constraints.

Novel auxiliary error compensation design for the adaptive neural control of a constrained flexible air-breathing hypersonic vehicle

This paper investigates the design of auxiliary error compensation for adaptive neural control of the longitudinal dynamics of a flexible air-breathing hypersonic vehicle (FAHV) with magnitude constraints on actuators. The control objective pursued is to steer velocity and altitude to follow their respective reference trajectories in the presence of actuator saturation and system uncertainties. To guarantee the exploited controllers robustness with respect to parametric uncertainties, neural network (NN) is applied to approximate the lumped uncertainty of each subsystem of FAHV model. Different from the traditional parameter updating technique, in this paper, the minimal-learning-parameter (MLP) scheme is introduced to estimate the norm rather than the elements of NNs weight vector while the computational load is reduced. The special contribution is that novel auxiliary systems are developed to compensate both the tracking errors and desired control laws, based on which the explored controller can still provide effective tracking of velocity and altitude commands when the actuators are saturated. Finally, numerical simulations are performed to illustrate the command tracking performance of the proposed strategy.

Minimal-learning-parameter based simplified adaptive neural back-stepping control of flexible air-breathing hypersonic vehicles without virtual controllers

In this paper, a novel adaptive neural control methodology is addressed for a flexible air-breathing hypersonic vehicle (FAHV) by a fusion of improved back-stepping and a minimal-learning-parameter (MLP) scheme. To facilitate the control design, the vehicle dynamics is decomposed into the altitude subsystem and the velocity subsystem. Different from the traditional back-stepping design, in this paper, the virtual control laws for the altitude dynamics are artificial intermediate variables required only for analytic purpose while only the final actual controller is needed to be implemented. For each subsystem, only one neural network is employed to approximate the lumped uncertainty. Moreover, by the merit of the MLP technique, only one learning parameter is required for neural approximation in each subsystem. The novel contribution with respect to the existing literatures is that the proposed control strategy is concise and the computational load is low. Finally, the effectiveness of the exploited control approach is verified by simulation results in the presence of uncertain parameters.

Novel prescribed performance neural control of a flexible air-breathing hypersonic vehicle with unknown initial errors

A novel prescribed performance neural controller with unknown initial errors is addressed for the longitudinal dynamic model of a flexible air-breathing hypersonic vehicle (FAHV) subject to parametric uncertainties. Different from traditional prescribed performance control (PPC) requiring that the initial errors have to be known accurately, this paper investigates the tracking control without accurate initial errors via exploiting a new performance function. A combined neural back-stepping and minimal learning parameter (MLP) technology is employed for exploring a prescribed performance controller that provides robust tracking of velocity and altitude reference trajectories. The highlight is that the transient performance of velocity and altitude tracking errors is satisfactory and the computational load of neural approximation is low. Finally, numerical simulation results from a nonlinear FAHV model demonstrate the efficacy of the proposed strategy.

Robust estimation-free prescribed performance back-stepping control of air-breathing hypersonic vehicles without affine models

ABSTRACT This paper investigates the design of a novel estimation-free prescribed performance non-affine control strategy for the longitudinal dynamics of an air-breathing hypersonic vehicle (AHV) via back-stepping. The proposed control scheme is capable of guaranteeing tracking errors of velocity, altitude, flight-path angle, pitch angle and pitch rate with prescribed performance. By prescribed performance, we mean that the tracking error is limited to a predefined arbitrarily small residual set, with convergence rate no less than a certain constant, exhibiting maximum overshoot less than a given value. Unlike traditional back-stepping designs, there is no need of an affine model in this paper. Moreover, both the tedious analytic and numerical computations of time derivatives of virtual control laws are completely avoided. In contrast to estimation-based strategies, the presented estimation-free controller possesses much lower computational costs, while successfully eliminating the potential problem of parameter drifting. Owing to its independence on an accurate AHV model, the studied methodology exhibits excellent robustness against system uncertainties. Finally, simulation results from a fully nonlinear model clarify and verify the design.

A neural approximation-based novel back-stepping control scheme for air-breathing hypersonic vehicles with uncertain parameters

In this article, a novel back-stepping control strategy is proposed for the longitudinal dynamics of air-breathing hypersonic vehicles subject to parameter uncertainties based on neural approximation. To facilitate the control design, the vehicle dynamics is reasonably decomposed into subsystems including the altitude subsystem and the velocity subsystem. Different from the existing studies, the presented improved back-stepping control approach for altitude dynamics only contains one actual controller while all the virtual controllers are artificial intermediate states needed only for stability analysis purpose. Hence, the problem of “explosion of terms” is completely avoided. By utilizing neural networks to approximate the lumped uncertainty of each subsystem, the robustness of the explored controller against uncertain aerodynamic coefficients is guaranteed. Moreover, by the merit of the minimal-learning-parameter method, only one learning parameter is required to be updated in each subsystem. The novelty of this article is that the exploited control scheme is simplified and exhibits low computational cost. Finally, the simulation results show that the proposed control methodology can provide robust tracking of velocity and altitude reference trajectories in the presence of system uncertainties.

Guaranteeing preselected tracking quality for air-breathing hypersonic non-affine models with an unknown control direction via concise neural control

Abstract A simplified neural controller is addressed for the longitudinal dynamics of an air-breathing hypersonic vehicle (AHV) with a completely unknown control direction by utilizing the prescribed performance control scheme. Unlike the existing literatures, the exploited methodology does not require an affine AHV model or any prior information about the sign of control gains. Moreover, the proposed strategy can provide preselected bounds on the transient and steady performance of velocity and altitude tracking errors. The altitude dynamics is converted into a pure feedback formulation with an unknown control direction, based on which, a novel adaptive neural controller that is quite simpler than the ones derived from back-stepping designs is achieved. For the problem of the unknown control direction, a Nussbaum-type function is introduced to handle it. By employing the minimal-learning parameter (MLP) technique to regulate the norm instead of the elements of the ideal weight vector, only one learning parameter is required for neural approximation. Thus, a low computational burden design is obtained. Finally, simulations are performed to verify the presented control approach.