This paper presents an adaptive conditioning-technique-based super-twisting algorithm aiming at improving the convergence speed and reducing the overshoot at the same time. Compared with a recently proposed method called new modified super-twisting algorithm, in which a linear acceleration factor and a damping factor are added to achieve this goal, the proposed method has several advantages. First, the proposed method enhances the convergence performance of the system by resorting to the characteristics of the conditioned super-twisting algorithm and the adaptive gains, without changing the basic structure of the classical super-twisting controller. Thus, stability proof of this method is much simpler and more concise. Furthermore, unlike the new modified super-twisting algorithm, in which an unnatural assumption on the Lipschitz disturbance is made for the stability proof, this method can counteract not only typical bounded Lipschitz disturbances but also square-root growth disturbances. Also, a set of less conservative control gains can be obtained with the proposed algorithm than with the compared algorithm. Apart from these benefits, several simulation results illustrate that the performance of the proposed method is even better in convergence and recovering from disturbance.

This paper presents an adaptive gain, finite-and fixedtime convergence super-twisting-like algorithm based on a revised barrier function, which is robust to perturbations with unknown bounds. It is shown that this algorithm can ensure a finite-and fixed-time convergence of the sliding variable to the equilibrium, no matter what the initial conditions of the system states are, and maintain it there in a predefined vicinity of the origin without violation. Also, the proposed method avoids the problem of overestimation of the control gain that exists in the current fixed-time adaptive control. Moreover, it shows that the revised barrier function can effectively reduce the computation load by obviating the need of increasing the magnitude of sampling step compared with the conventional barrier function. This feature will be beneficial when the algorithm is implemented in practice. After that, the estimation of the fixed convergence time of the proposed method is derived and the impractical requirement of the preceding fixed-time adaptive control that the adaptive gains must be large enough to engender the sliding mode at time is discarded. Finally, the outperformance of the proposed method over the existing counterpart method is demonstrated with a numerical simulation.

This article improves an enhanced predictor-corrector entry guidance method for hypersonic flight vehicle. To compensate for the shortcoming that the enhanced predictor-corrector guidance method sacrifices guidance precision to meet path constraints, this article develops an improved predictor-corrector guidance method. Through consuming more energy in the middle section of entry, the guidance method greatly reduces the load in the end section of entry, earning more time for the guidance bank angle which aims at ensuring entry precision to function in the end section, and thus, the entry precision is improved. The simulation results on the CAV-H flight vehicle show preliminarily that the guidance method effectively enhances the guidance precision for both orbital and suborbital entry missions and strictly constrains distance errors within the stipulated range. Furthermore, this method is also fairly effective for flight vehicles with lower lift-to-drag ratio (X-33). Then, the flight vehicle with a higher lift-to-drag ratio is chosen as the simulation target to further examine the guidance method's effectiveness and precision.

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