This article investigates the distributed formation control problem for multiple unmanned surface vessels (USVs) with model uncertainties, exogenous disturbance, and input saturation. First, a distributed finite-time sliding mode observer is developed to obtain the desired trajectory of each USV. Then, a second-order differentiable continuous fixed-time prescribed performance function is applied to reconstruct the error model based on the reference signal. In addition, the unknown part and disturbance are simultaneously handled by the composite learning control method as well as input saturation, where the acrlong NN, disturbance observer, auxiliary system, and nonsingular fast terminal sliding mode technique are integrated. Moreover, it is proved that the formation error converges to a small neighbor of the origin in a finite time. Finally, the computational simulation examples are conducted to validate the feasibility and effectiveness of the proposed method.

Mechanical faults of high-voltage circuit breakers (HVCBs) always occur during long-term operation; thus, the recognition of such fault types has become a key issue. Now, most related research involves improvements to the classification method for higher precision based on a single sensor, ignoring the multi-sensor information fusion of the vibration signal for the fault diagnosis of HVCB. Classical evidence theory is a common approach for solving multi-sensor fusion. However, it fails upon encountering conflicting evidence. Therefore, this study proposes an improved multi-sensor evidence combined rule to optimize the evidence gathered from different sensors, especially for the highly conflicting and invalid evidence. A numerical simulation example indicates that the proposed method achieves a better performance. On this basis, the proposed method is applied to diagnose HVCB faults with multi-sensor. Further, the superiority of the multi-sensor fusion is proved via a comparison with the diagnostic results of five typical classifiers under a single sensor; specifically, by comparing it with traditional Dempster’s combination rules, it is verified that the proposed method is better than other multi-sensor fusion methods, which can be used in the field of HVCB fault diagnosis.

Automatic Carrier Landing System With Fixed Time Control

The reliability of gas insulated switchgear (GIS) is very important for the safe operation of power systems. However, the research on potential faults of GIS is mainly focused on partial discharge, and the research on the intelligent detection technology of the mechanical state of GIS is very scarce. Based on the abnormal vibration signals generated by a GIS fault, a fault diagnosis method consisting of a frequency feature extraction method based on coherent function (CF) and a multi-layer classifier was developed in this paper. First, the Fourier transform was used to analyze the differences and consistency in the frequency spectrum of signals. Secondly, the frequency domain commonalities of the vibration signals were extracted by using CF, and the vibration characteristics were screened twice by using the correlation threshold and frequency threshold to further select the vibration features for diagnosis. Then, a multi-layer classifier composed of two one-class support vector machines (OCSVMs) and one support vector machine (SVM) was designed to classify the faults of GIS. Finally, the feasibility of the feature extraction method was verified by experiments, and compared with other classification methods, the stability and reliability of the proposed classifier were verified, which indicates that the fault diagnosis method promotes the development of an intelligent detection technology of the mechanical state in GIS.

The shipboard landing problem of a quadrotor without prior knowledge of the ship is investigated in this article. The relative motion model of the quadrotor and the ship is established and transformed into an affine form for the landing controller design. To improve the adaptability to different ships, a neuron-adaptive neural network is adopted to estimate the model uncertainties caused by unknown ship parameters, and the disturbance observer is developed to measure the lumped disturbance, including the external disturbances and the approximation error. Moreover, a novel backstepping integral evolution sliding mode controller is developed for the relative position, and nonsingular fast terminal sliding mode control is adopted for others. Combined with the auxiliary systems, the input saturation and filter errors are considered in the closed-loop system. The theoretical analysis illustrates that the states of the relative motion system are guaranteed to be bounded. The simulation examples demonstrate the effectiveness and advantages of the proposed method.

A distributed cooperative control algorithm of multiple unmanned aerial vehicles (UAVs) with directed topology is investigated in this brief. To overcome the actuator faults and external disturbance, incremental sliding mode formation controller is developed to realize the distributed coordination of the UAVs system, where an adaptive mechanism is utilized to deal with the chattering problem. Moreover, to reduce the adverse influence of input saturation, an auxiliary system is introduced to regulate the control signals. It is shown by Lyapunov method that the stability of the proposed algorithm can be guaranteed. A numerical example is given to illustrate that all follower UAVs can track the leader UAV.