AUTOMATIC CONTROL AND COMPUTER SCIENCES最新文献

In this paper, we propose a novel robust state-feedback ({{mathcal{H}}_{infty }}) control design method for active seat suspension systems, aiming to enhance passenger comfort in uncertain road conditions. Our approach minimizes the impact of road disturbances on vertical acceleration experienced by the human body, while explicitly considering constraints on suspension stroke deflection to ensure system reliability. We model the suspension system as a three degree-of-freedom (3-DOF) system and derive new synthesis conditions in terms of Linear Matrix Inequalities (LMIs). Simulation results demonstrate the superior performance of our strategy compared to a passive suspension system and a pole-placement controller, achieving a reduction in the peak of the maximum singular value from 57.107 to 4.554. Additionally, our controller reduces the maximum control force from 1000 N (for the pole-placement controller) to 640.5 N, indicating improved energy efficiency. These results highlight significant improvements in passenger comfort, suspension deflection management, and control effort.

In this article, a novel nonsingular fast sliding mode surface is presented for the control issue of fixed-time trajectory tracking of the manipulator. Faced with the uncertainty of the manipulator’s own parameters and the complexity of external disturbances, the adaptive technology is utilized to estimate their upper limits values, and the technology does not need to rely on rich prior knowledge. In addition, the hyperbolic tangent function is applied to suppress the torque chattering in the control process of the manipulator. Ultimately, the fixed-time stability of the system is demonstrated through the application of Lyapunov theory, along with some simulation evaluations to verify the efficacy and merit of the proposed controller.

The prediction of the probability of default can help banks and other financial institutions to effectively identify and assess the potential default risk associated with family farms, thereby reducing losses due to bad debts. Although many methods are available for constructing models for the probability of default, the choice of optimal models is still inconclusive. Taking the survey data of 722 family farms in China Inner Mongolia as the empirical objects, 4 machine learning methods, including binary classification logistic regression, decision tree CART algorithm, random forest, and kernel support vector machine, were used to construct the default probability prediction model for family farms. By comparing and analyzing the four models, we found a better default probability prediction model to help financial institutions better audit the qualifications of family farms and reduce borrowing risks. The results showed that (1) the three models except logistic regression had strong prediction ability, which was higher than 90%, and the classification effect was good; and (2) the random forest model had the best prediction effect, the decision tree was the second, and the logistic regression was the worst.

The continuous progress of modern science and technology pushes the development of agricultural machinery in the direction of automation, and the design of agricultural machinery has become an important means to improve the efficiency and quality of agricultural production. In order to achieve accurate and efficient transplanting operations, the study carried out a corresponding design of the seedling picking claw device of the transplanting machine, and at the same time, in view of the control requirements of the device, a double closed-loop control algorithm based on the position-velocity proportional-integral-derivative was introduced to achieve the precise positioning control of the opening and closing of the seedling picking claw. Results show that the motor plate of the seedling picking claw with the control strategy proposed by the Institute reaches and stabilizes at the desired displacement in about 0.1 s, which reduces the response time by 0.2 and 0.12 s, respectively, compared with the traditional PID control method and the fuzzy PID control method. It shows that the transplanting machine device designed by the Institute has a good effect of practical application, which helps to increase the transplanting precision as well as efficiency, and provides important technical support for the development of modern agriculture. Provide important technical support for the development of modern agriculture.

A composite control scheme is proposed for an air-breathing hypersonic vehicle at the presence of multisource uncertainties and actuator faults. Firstly, the dynamic model of the vehicle is transformed into five control-oriented subsystems. Then, the improved fixed-time sliding mode observer is used to estimate the uncertainties caused by elastic effects, external disturbances and actuator faults, these uncertainties are further compensated in the integral terminal sliding mode control laws. Finally, the stability of the closed-loop system is analyzed, the result shows that the designed control system is asymptotically stable. Compared with the disturbance observer-based robust backstepping control system, the designed control system has great advantages in settling time and control efforts, and has strong robustness to multisource disturbances and actuator faults.

To solve the problems of inaccurate wind speed, uncertainty, and interference in maximum power point tracking (MPPT), a novel MPPT control method according to neural network model reference adaptive control is put forward in this paper. First, under the premise of Betz’s theorem, the transmission chain model of a wind turbine is established, an effective wind velocity estimator is obtained by training the neural network through wind farm operation and maintenance data. Then, a composite controller composed of model reference adaptive controller (MRAC) and neural network controller is designed to make up for the uncertainties and disturbances in the system effectively. Finally, the update rate of the controller is adjusted according to Lyapunov stability theorem to ensure the asymptotic convergence of the variables in the system. MATLAB is used to simulate different wind speeds, and results show that compared with the traditional MRAC controller, the proposed method has better anti-interference ability and robustness, and can further enhance the wind energy utilization efficiency of the wind machine.

The high time consumption and storage requirements are the main obstacles for the current applications of point cloud data, especially for high-density data. To alleviate the computational burden while preserving the important geometric features, an effective point cloud simplification method based on the Harris-SIFT feature and improved voxel filtering is proposed, which divides the point cloud points into strong feature points and weak feature points. Firstly, the key points are extracted by the improved Harris algorithm from the original point cloud data, and the SIFT algorithm is used to divide them into the strong feature points and weak feature points. Then, the weak feature points are further simplified via the improved voxel filtering by introducing the scale factor, and finally, the strong and weak feature points are integrated to be the new data according to the structure of the point cloud. Experimental results of three well-known methods under four models of point cloud demonstrate that not only the important geometric features will be well preserved, but also the higher simplification rate will be obtained in terms of the information entropy and average information entropy.

The study describes the business logic and results of the steganalysis software, a steganalytical complex that operates on the basis of the author’s image steganalysis algorithms, which make it possible to detect embeddings even with low filling of the stego container (10–25% of the total volume). This solution is aimed at increasing the information security of an organization by identifying media files (images) containing an embedding and preventing unauthorized forwarding of such files or viewing and extraction of the received hidden message, as well as preventing the installation of malicious programs, whose module is embedded in the image using a steganographic method. The software package works with embeddings performed using the Koch–Zhao and least-significant-bit substitution (LSB-substitution) methods.

This paper studies issues related to the implementation of intellectual synthesis of cyber-resilient structures. Methods are proposed that implement synthesis both at the stage of designing the network structure and at the stage of its recovery during a cyberattack or failure. The experimental results confirming the efficiency of the proposed methods are presented. The architecture of the system for detecting cyberattacks and intellectual synthesis of cyber-resilient network structures is described.