Adaptive Neural Network Control of Zero-Speed Vessel Fin Stabilizer Based on Command Filter

This paper proposes a zero-speed vessel fin stabilizer adaptive neural network control strategy based on a command filter for the problem of large-angle rolling motion caused by adverse sea conditions when a vessel is at low speed down to zero. In order to avoid the adverse effects of the high-frequency part of the marine environment on the vessel rolling control system, a command filter is introduced in the design of the controller and a command filter backstepping control method is designed. An auxiliary dynamic system (ADS) is constructed to correct the feedback error caused by input saturation. Considering that the system has unknown internal parameters and unmodeled dynamics, and is affected by unknown disturbances from the outside, the neural network technology and nonlinear disturbance observer are fused in the proposed design, which not only combines the advantages of the two but also overcomes the limitations of the single technique itself. Through Lyapunov theoretical analysis, the stability of the control system is proved. Finally, the simulation results also verify the effectiveness of the control method.

Command-Filtered Adaptive Neural Network Backstepping Quantized Control for Fractional-order Nonlinear Systems with Asymmetric Actuator Dead-zone via Disturbance Observer

An adaptive neural network (NN) backstepping quantized control based on command filter and disturbance observer is proposed for fractional-order nonlinear systems with asymmetric actuator dead-zone and unknown external disturbance in this paper. An adaptive NN mechanism is designed to estimate unknown functions, and a command filter is introduced to estimate the virtual control variable as well as its derivative, so the ``explosion of complexity" issue can be avoided existed in the classical backstepping method. To handle the unknown external disturbance, a fractional-order disturbance observer is developed. Moreover, a hysteresis-type quantizer is used to quantify the final input signal to overcome the system performance damage caused by the actuator dead-zone. The quantized input signal can ensure that all the involved signals keep bounded and the tracking error converges to an arbitrarily small region of the origin. Finally, two examples are presented to verify the effectiveness of the proposed method.

Adaptive neural network–based fault estimation for nonlinear systems with actuator faults in simultaneous multiplicative and additive forms

Non-linearities and actuator faults often exist in practical systems which may degrade system performance or even lead to catastrophic accidents. In this article, a fault-tolerant compensation control strategy is proposed for a class of non-linear systems with actuator faults in simultaneous multiplicative and additive forms. First, radial basis function neural network is employed to approximate the system non-linearity. The approximation is achieved by only one adaptive parameter, which simplifies the computation burden. Then, by means of the backstepping technique, an adaptive neural controller is developed to cope with the adverse effects brought by the system non-linearity and actuator faults in multiplicative and additive forms. Meanwhile, the proposed control design scheme can guarantee that the considered closed-loop system is stable. The novelty of the article lies in that the system non-linearity, the additive actuator faults, and the multiplicative actuator faults that often exist in practical engineering are catered for simultaneously. Furthermore, compared with some existing works, the approximation of the system non-linearity is achieved by only one adaptive parameter for the purpose of reducing the computation burden. Therefore, its applicability is more general. Finally, a numerical simulation and a comparative simulation are carried out to show the effectiveness of the developed controller.

Machine Learning-Based Gynecologic Tumor Diagnosis and Its Postoperative Incisional Infection Influence Factor Analysis

Various factors influencing postoperative incisional infection in gynecologic tumors were analyzed, and the value of quality nursing intervention was studied. In this study, 74 surgically treated gynecologic tumor patients were randomly selected from within the hospital as the study population and were divided into study and control groups. For this purpose, the whole-group random sampling method is utilized to compare the postoperative incisional infection rates of the two groups, analyze their influencing factors, and develop quality nursing interventions. In this paper, a breast cancer diagnosis prediction model was developed by combining the self-attentive mechanism. The preprocessing work such as data quantification and normalization was performed first which is followed by adding the preprocessed data to the self-attentive mechanism. This model has solved the problem that recurrent neural networks (RNNs) could not extract and calculate the features at the same time. Likewise, it has solved the drawback that the RNN could not consider global features at the same time when extracting the features, and then, the feature matrix extracted by the self-attentive mechanism was added to the adaptive neural network. The adaptive neural network model for breast cancer diagnosis prediction was constructed and, finally, relevant parameters of the adaptive neural network model were adjusted according to different tasks to make the model performance optimal. Experimental results showed that the postoperative incision infection rate of patients in the study group was 2.70%, which was significantly lower than that of 21.62% in the control group ( P < 0.05 ). Likewise, operation time, operation method, hospitalization time, preoperative fever, diabetes mellitus, and anemia were the main influencing factors of postoperative incision infection in women with gynecologic tumors. The time of surgery, surgical method, long hospital stay, preoperative fever, diabetes, and anemia are the main factors that lead to postoperative incisional infection in female gynecologic tumor patients.