This article proposes an output-feedback fixed-time trajectory tracking control methodology for hypersonic flight vehicles subject to asymmetric output constraints. In contrast to the state of the art, the most distinguishing feature of our control design lies in avoiding using conventional recursive design methods (e.g., backstepping technique, dynamic surface control, etc.) and in not relying on full-state availability. In the velocity control loop, an asymmetric integral barrier Lyapunov function is adopted to confine velocity variable within a well-defined compact set all the time. In the altitude control loop, after utilizing its cascaded property and proposing a novel scaling function, the original constrained system is transformed to an unconstrained one, which facilitates the control design and stability analysis. Moreover, the proposed control algorithm only involves one fuzzy logic approximator as well as one fixed-time differentiator in the transformed system and guarantees that the tracking errors of velocity and altitude converge into the user-defined residual sets within fixed time. Several comparative simulations have been conducted to highlight the superiorities of the developed method.

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This article solves the fixed-time trajectory tracking problem for hypersonic flight vehicles (HFVs) encountered with diverse actuator faults and asymmetric envelope constraints. In contrast to the state of the art, the crucial characteristics of our design lie in obviating the explosion of complexity of the conventional recursive design, and in realizing satisfactory preselected tracking qualities for flight states in the sense of guaranteeing asymmetric envelope constraints. More precisely, by exploiting the fixed-time command filters to produce certain command signals and their derivatives, a modified command-filtered control algorithm is formulated to circumvent heavy computation burden caused by repetitive derivative of intermediate control laws. A two-step control methodology is devised based on an auxiliary compensating dynamics, which is capable of compensating for the actuator faults completely without the need for prior knowledge about the lumped disturbances and the actuator faults. Time-varying asymmetric barrier Lyapunov functions are introduced to confine the flight state tracking errors within the corresponding time-varying compact sets all the time provided their initial values remain therein. The effectiveness of the proposed method is validated by comparative simulation results.

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This paper presents an adaptive neural control to solve the tracking problem of a class of pure-feedback systems with non-differentiable non-affine functions in the presence of unknown periodically time-varying disturbances. To handle with the design difficulty from non-affine structure of pure-feedback system, a continuous and positive control gain function is constructed to model the periodically disturbed non-affine function as a form that facilitates the control design. As a result, the non-affine function is not necessary to be differentiable with respect to control variables or input. In addition, the bounds of non-affine function are unknown functions, and some appropriate compact sets are introduced to investigate the bounds of non-affine function so as to cope with the difficulty from these unknown bounds. It is proven that the closed-loop control system is semi-globally uniformly ultimately bounded by choosing the appropriate design parameters. Finally, comparative simulations are provided to illustrate the effectiveness of the proposed control scheme.

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A novel accurate tracking controller is developed for the longitudinal dynamics of Hypersonic Flight Vehicles (HFVs) in the presence of large model uncertainties, external disturbances and actuator nonlinearities. Distinct from the state-of-the-art, besides being continuity, no restrictive conditions have been imposed on the HFVs dynamics. The system uncertainties are skillfully handled by being seen as bounded “disturbance terms”. In addition, by means of back-stepping adaptive technique, the accurate tracking (i.e. tracking errors converge to zero as time approaches infinity) rather than bounded tracking (i.e. tracking errors converge to residual sets) has been achieved. What's more, the accurate tracking problems for HFVs subject to actuator dead-zone and hysteresis are discussed, respectively. Then, all signals of closed-loop system are verified to be Semi-Global Uniformly Ultimate Boundness (SGUUB). Finally, the efficacy and superiority of the developed control strategy are confirmed by simulation results.

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This work attempts to achieve precise tracking control for the longitudinal dynamics of hypersonic flight vehicles (HFVs) without adopting universal approximators (e.g., fuzzy logic systems, neural networks, etc.). To this purpose, the system nonlinearities are well constrained to be bounded within some compact sets and are tackled by means of adaptation technique. Apart from achieving precise tracking, some prescribed performances (e.g., convergence rate, maximum steady tracking error, etc.) of the tracking errors of velocity, altitude, flight path angle, angle of attack, and pitch rate are also preserved. The proposed method is complexity-reduced and structurally simple in the sense that the time derivatives of the virtual control laws are no longer required and that the number of adaptation parameters have been reduced to only five due to the absence of universal approximators. Barbalat lemma is combined with Lyapunov theory to prove the closed-loop stability, while guaranteeing that the tracking errors eventually converge to zero through choosing appropriate design parameters. The effectiveness of the proposed control scheme is demonstrated by comparative numerical simulations.

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This work for the first time develops a neuro-adaptive control strategy for an extended class of longitudinal dynamics of hypersonic flight vehicles (HFVs). To handle with the design difficulty that the uncertain time-varying disturbances appear implicitly in HFVs dynamics, a new function approximator is designed by incorporating the fourier series expansion (FSE) into the radial basis function neural networks (RBFNNs). An integral term and a linear term are, respectively, constructed to speed up the convergence rate and compensate for the negative effects caused by approximation errors. It is rigorously proved that all closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB). Simulation results verify the effectiveness of the proposed control methodology.@en

It is well known that backstepping method suffer from the problem of "explosion of terms", which is solved by dynamic surface control (DSC) method. This paper presents an improved dynamic surface control (IDSC) method, which constructs the state errors by directly using virtual control signals, rather than using the signals produced by first-order filters in DSC method. The signals produced by first-order filters will only be used to construct the virtual control laws and actual control law. This modification makes the state errors to be more free from the influence of first-order filters. The stability of systems controlled by the proposed IDSC method is proved based on Lyapunov theorem. Finally, the advantage of IDSC method has been shown by simulation results, and it can be seen in the simulation results that IDSC method has better tracking performance and is more stable than DSC method.@en