Event-triggered attitude control for hypersonic morphing vehicles using fixed-time control technique

This paper aims at introducing H2 and H∞ robustness into the well-known characteristic model-based golden-section adaptive control law, and applying the robust adaptive control scheme to the attitude control of hypersonic cruise vehicles that are subject to external disturbances and aerodynamic coefficients uncertainties. When maneuvering at ultra high speeds, the attitude system of the hypersonic cruise vehicle is extremely sensitive to external disturbances and aerodynamic coefficients variations, and therefore the adaptiveness and the robustness of the attitude system are crucial during the controller design. To enhance the robustness of the existing golden-section adaptive control law, a golden-section robust adaptive control law is proposed. Compared to the existing control law where the design of the parameter λ depends on experience and is carried out offline, linear matrix inequality-based synthesis of λ is proposed such that the closed-loop system is stable with guaranteed H2 and H∞ performance. It is suitable for online computing and provides a time-varying λ(k) that is adjusted towards the optimal H2 and H∞ performance. When being applied to the attitude control of hypersonic vehicles during re-entry, the adaptive nature of the proposed control law provides the attitude system the capability to accommodate large flight conditions, and its H2 and H∞ robustness brought by λ(k) guarantees satisfying tracking performance in the presence of disturbances including both external disturbance and absolute aerodynamic coefficients errors.

Low-order diving integrated guidance and control for hypersonic vehicles

Back-stepping robust trajectory linearization control for hypersonic reentry vehicle via novel tracking differentiator

Tracking control for air-breathing hypersonic cruise vehicle based on tangent linearization approach

A novel adaptive neural network dynamic surface control is proposed for hypersonic vehicle. Based on the hypersonic vehicle model characteristics, the adaptive neural network dynamic surface control attitude controller and the neural networks velocity controller are designed, respectively. By combining Nussbaum gain function with decoupled backstepping, the problems of unknown control directions and control singularity from hypersonic vehicles model in strict-feedback form are simultaneously solved. Furthermore, the norm of the ideal weighting vector in neural network systems is considered as the estimation parameter, such that only one parameter is adjusted at each recursive step. Based on decoupled backstepping method and Lyapunov stability theorem, the semi-global stability of the flight control system is proved. Simulation results show that the new controller can guarantee the expected tracking performance.

Sliding mode based trajectory linearization control for hypersonic reentry vehicle via extended disturbance observer

Active disturbance rejection based trajectory linearization control for hypersonic reentry vehicle with bounded uncertainties

For the longitudinal model of an air-breathing hypersonic vehicle, its pitch dynamics is characterized by nonminimum phase when the term "elevator to lift" is not negligible. Nonminimum-phase characteristic is one of the main challenges for control design. In this paper, the control design problem is investigated for this kind of hypersonic vehicle with uncertain parameters and a novel design philosophy is presented based on analysis of the plant's dynamics. Firstly, this model is separated as velocity subsystem and pitch dynamics subsystem. For the former, an adaptive dynamic inversion controller is derived. For the latter, a high-gain adaptive dynamic surface controller is derived. Furthermore, the stability analysis represents the states and tracking errors are ultimately uniformly boundary. Finally, simulation results validate the proposed method.

This paper presents a longitudinal trajectory tracking scheme with [Formula: see text] adaptive control for hypersonic reentry vehicles (HRVs). A linear time-varying (LTV) multiple input multiple output (MIMO) model, in which influences of lateral states, earth rotation, and linearization are considered as model uncertainties, is derived based on state and input errors of longitudinal model. The normalization of error model is used to reduce differences of magnitude orders in state and input matrix elements which may affect the stability of [Formula: see text] adaptive controller. In order to achieve an accurate tracking performance, a linear quadratic regulator (LQR) controller is employed as the baseline controller, augmented with an [Formula: see text] adaptive controller to attenuate the matched and unmatched uncertainties. Based on the augmented controller, the optimization process is executed with the estimate of uncertainties at the same time. The simulation results of LQR controller, [Formula: see text] augmentation controller and robust [Formula: see text] controller show that the [Formula: see text] adaptive control method can reduce the terminal and integral of squared state errors validly. Terminal state errors in all simulation scenarios are less than 2.5m/s, 1e-3 and 10m, respectively, which reflects its effectiveness in increasing robustness of baseline controller.

According to the air-breathing hypersonic vehicle(AHV) model with perturbation of aerodynamic coefficients and uncertain parameters in the system, taking into account the problems of aerodynamic co-efficients approximation and uncertain parameter identification, fuzzy function(FF) and Radial Basis Neural Network(RBNN) based sliding mode control is proposed. AHV control model has the characteristics of multivariable, strong coupling and nonlinear. Applying the powerful function approximation function of fuzzy function to approach the aerodynamic coefficient, using RBNN self-learning identification ability to identify the system uncertain parameters, combined with sliding mode variable structure control, to eliminate the aircraft's buffeting problem to some extent and improve the robustness of the system. Simulation results show that the system can maintain stability after adding velocity step instructions and altitude step instructions, and has strong robustness to uncertain parameters, and overcome the chattering problem.

A novel backstepping control scheme based on the fixed-time observer is proposed for the attitude control of hypersonic morphing vehicles. A fixed-time observer is used to estimate the inertial matrix uncertainty and dynamic moments caused by span variation, aerodynamic coefficient uncertainty and external environmental disturbance. It ensures that the above disturbance estimation errors can converge to zero within a fixed time, and the convergence time is independent of the initial conditions of the hypersonic morphing vehicles. Furthermore, taking into account the effects of unmatched unknown nonlinear dynamics on the attitude tacking performance, a backstepping controller is designed to compensate for the above disturbances in the system. The stability of the closed-loop system is proved based on the Lyapunov function technique. Finally, the simulation results verify the effectiveness, reliability and accuracy of the proposed scheme.

This paper presents a novel fixed-time adaptive tracking control scheme for the hypersonic flight vehicles (HFVs) subject to asymmetric time-varying constraints, uncertain dynamics and unknown external disturbances. By incorporating the back-stepping technique and radial basis function neural networks (RBFNNs), the uncertain dynamics of HFVs are estimated. Note that most existing results only achieve practical fixed-time stability but not fixed-time stability, or require specific knowledge of all the dynamics of HFVs. To remove such restrictions, a fixed-time controller is newly constructed by means of a tuning functions and a projection operator-based adaptation mechanism. In consequence, the tracking errors can asymptotically converge to the preassigned compact set within fixed-time and the asymmetric time-varying constraints of HFVs never are violated. Finally, the effectiveness and superiority of the proposed control strategy is demonstrated by numerical simulations.

Nonlinear Flight Control Systems Design for Hypersonic Vehicles: Results and Open Problems

For a class of multi-input multi-output nonlinear systems, a disturbance observer-based control is proposed to solve the tracking problem in the presence of mismatched disturbances. By designing a novel compensation gain matrix, the disturbances can be removed from the output channel completely as well as retaining the nominal performance. Compared with the state of the art, the gain matrix reduces to be constant; therefore, the complexity of the controller is simplified greatly. This method is applied to the control of hypersonic flight vehicles to demonstrate its effectiveness.

Static stability margin is a critical parameter in flight control design. The feasible range of it must cover the uncertainty through the flight. To reasonably identify the feasible range of static stability margin in advance, an approach based on guardian maps is proposed for flight control of hypersonic flight vehicles with input saturation. First, the model of hypersonic flight vehicle (HFV) is established as a parametric plant. Then, flying quality requirements for the closed-loop system are formulated as inequality constraints using guardian maps. Moreover, by using linear matrix inequality, the saturation of elevators is taken into account in the integrated control of attitude control. The prescribed minimum of static stability margin that ensures the flying quality of hypersonic flight vehicles with input saturation is obtained. Furthermore, from the prospective of integrated control, it is shown that the feasible range of static stability margin can be enlarged by changing aerodynamic characteristics. The effectiveness of the proposed approach is validated by numerical simulation.