In this work, the stochastic anti-disturbance control issue is studied for helicopter systems under multiple random disturbances, in which part disturbances are induced by random noises and others are described by Markovian jump models. Combining the feedback linearization method, backstepping control scheme with nonlinear disturbance observerbased control (NDOBC) technology, a stochastic anti-disturbance flight controller is constructed for full-degree-of-freedom helicopter systems to reject the influence of these disturbances and achieve predefined tracking tasks. Meanwhile, the boundedness in probability of tracking errors is guaranteed via the stochastic Markovian jump control system theory. Finally, an example is given to verify the effectiveness of proposed control methods.

Classification of coronary artery stenosis is essential in assisting physicians in diagnosing cardiovascular diseases. However, due to the complexity of medical diagnosis and the confidentiality of medical images, it is difficult to obtain many coronary artery stenosis image samples for scientific research in general. In addition, the degree, location, and morphology of coronary artery stenosis in different patients, as well as the noise in computed tomography angiography (CTA) images, make it challenging to extract typing features effectively. To address the above problems, first, a joint segmentation method is proposed based on maximum between-class variance and region growing to extract key regions from CTA images to facilitate further feature extraction. Then, a coronary artery stenosis classification model based on convolutional block attention module (CBAM) and transfer learning is constructed, which can effectively improve the model training effect under the insufficient samples. Finally, a dataset of CTA images from actual patients is applied for experimental verification. The experimental results show that the accuracy (Acc) of the proposed method is up to 98.99%, which is greatly improved compared with several machine learning algorithms and neural network methods. It can be concluded that the accuracy of coronary stenosis classification can be considerably improved, and a reasonable scientific basis is provided for clinical medical diagnosis.

In this article, the resilient anti-disturbance control is studied for uncertain turbofan system subject to unmeasured states and multiple disturbances. Based on four kinds of disturbances in the addressed system, some disturbances are described as an external system by using available information while the others are assumed to be energy-bounded, which are included in the system dynamics, output measurement, and controlled output, simultaneously. Initially, a state observer and a disturbance observer are jointly constructed to estimate the unmeasured state and unknown disturbance. The estimation on disturbance is used in the feedforward controller to reject the disturbances and the state estimation is applied to the resilient output feedback controller, which guarantee that the closed-loop system is asymptotically stable with the L2-L∞ performance, and enhance the robustness of the uncertain turbofan system. Then, the Lyapunov stability theory and linear matrix inequality (LMI) technology are combined to obtain the algorithms on checking the controller gain and observer one. Finally, the effectiveness of our proposed methods is shown by using some numerical simulations.

In this paper, a fault diagnosis method is proposed based on multi-sensor fusion information for a single fault and composite fault of train braking systems. Firstly, the single mass model of the train brake is established based on operating environment. Then, the pre-allocation and linear-weighted summation criterion are proposed to fuse the monitoring data. Finally, based on the improved expectation maximization, the braking modes and braking parameters are identified, and the braking faults are diagnosed in real time. The simulation results show that the braking parameters of systems can be effectively identified, and the braking faults can be diagnosed accurately based on the identification results. Even if the monitoring data are missing or abnormal, compared with the maximum fusion, the accuracies of parameter identifications and fault diagnoses can still meet the needs of the actual systems, and the effectiveness and robustness of the method can be verified.