In order to improve the global convergence performance of the super-twisting sliding mode control (STSMC) for the uncertain hybrid mechanism, especially in the high-speed scenario, and enhance the robustness of hybrid mechanism system to the uncertainties with a wide range of changes, an intelligent fixed-time super-twisting sliding mode control (IFTSTSMC) is proposed. Firstly, a fixed-time super-twisting sliding mode control (FTSTSMC) algorithm is designed by adding the exponential power terms with the fixed-time performance parameters in sliding variables and the transcendental function of the super-twisting algorithm in order to enhance the global convergence performance of the STSMC. Secondly, the existence condition of FTSTSMC for the uncertain hybrid mechanism is analyzed. The IFTSTSMC is designed by introducing RBF neural network to break through the limited range of uncertainties in FTSTSMC and enhance the robustness of hybrid mechanism system to the uncertainties with a wide range of changes. Then, the Lyapunov stability of the proposed method and the global fixed-time convergence of the system are proved theoretically. Finally, the effectiveness and superiority of the proposed control method are verified by the simulation and the automobile electro-coating conveying prototype experiment comparing with two classical finite-time sliding mode control methods.

Robust control of non-linear systems is a challenging task, notably in the presence of external disturbances and uncertain parameters. The main focus of this paper is to solve the trajectory tracking problem of an unconventional quadrotor with rotating arms (also known as a foldable drone), while overcoming some of the challenges associated with this type of vehicle. Therefore, in a first step, the model of this vehicle is presented, taking into account the change of the inertia, the centre of gravity, and the control matrix. The theoretical foundations of backstepping control, based on the finite time Lyapunov stability theory and enhanced by a Super-Twisting algorithm, are then discussed. Numerical simulations are performed to demonstrate the efficiency of the suggested control approach. Finally, a qualitative and quantitative comparative study of the proposed controller with the conventional backstepping controller is performed. Overall, the obtained results show that the proposed control strategy outperforms in terms of accuracy and resilience.

The cooperative guidance problem of multiple inferior missiles intercepting a hypersonic target with the specific impact angle constraint in the two-dimensional plane is addressed in this paper, taking into consideration variations in a missile’s speed. The guidance law is designed with two subsystems: the direction of line-of-sight (LOS) and the direction of normal to LOS. In the direction of LOS, by applying the algebraic graph theory and the consensus theory, the guidance command is designed to make the system convergent in a finite time to satisfy the goal of cooperative interception. In the direction of normal to LOS, the impact angle is constrained to transform into the LOS angle at the time of interception. In view of the difficulty of measuring unknown target acceleration information in real scenarios, the guidance command is designed by utilising a super-twisting algorithm based on a nonsingular fast-terminal sliding mode (NFTSM) surface. Numerical simulation results manifest that the proposed guidance law performs efficiently and the guidance commands are free of chattering. In addition, the overall performance of this guidance law is assessed with Monte Carlo runs in the presence of measurement errors. The simulation results demonstrate that the robustness can be guaranteed, and that overall efficiency and accuracy in intercepting the hypersonic target are achieved.