This article investigates adaptive fuzzy bipartite consensus control for multi-agent systems (MASs) with guaranteed performance. By simplifying the bipartite consensus control problem as a local tracking problem, the prescribed performance of the bipartite consensus error is ensured. In addition, distributed adaptive fuzzy bipartite consensus control scheme is developed for each agent using a series of smooth functions and the fuzzy logic system (FLS). It is essential to have only one adaptive method in each controller. With the proposed control scheme, it is proved that all the signals in the closed-loop system are bounded and the bipartite consensus error converges to a user-prescribed interval asymptotically as time goes to infinity. Finally, the effectiveness of the proposed scheme is validated by a networked pendulum system.
This paper discusses the drag-free attitude control problem of two low-earth orbit drag-free satellites with two test masses, which are used for the detection of space gravitational waves. The control schemes of the science mode are designed, and a robust controller capable of resisting disturbances are proposed. First of all, the whole system dynamics model of the satellite with two test masses is established. Decoupling the complex system dynamics model based on characteristics of dynamic coupling and control time-frequency, three loops called the spacecraft attitude control loop, the drag-free control loop and the suspension control loop are produced. Then, taking consideration of the control requirements in frequency domain and the spectral models of various external disturbances and sensor noises, constrains for sensitivity function and complementary sensitivity function of each control loop are derived utilizing the mixed-sensitivity method of $H_{\infty}$ robust control theory. With selecting appropriate weight functions, an $H_{\infty}$ robust controller is designed. Finally, simulation results indicate that not only the inter-satellites pointing error but also the pose errors and residual acceleration of test masses satisfy the science performance specification, which verifies the effectiveness of the designed controller.
Spare parts life circle value is sensitive to various factors and has the traits of obvious uncertainty, so the comprehensive analysis of the cost-effectiveness becomes more complex. When procuring and selecting spare parts, it is necessary not only to consider efficiency, but also to consider costs; It is necessary not only to consider whether it is “affordable” but also to consider whether it is “affordable”, and strive to achieve the principle of harmonizing the advanced nature of spare parts with the sustainability. In this paper, A type of solution to the comprehensive assessment on spare parts life circle cost-effectiveness which is gray analysis method has been applied. On this basis, the corresponding weight according to relative importance of the primary factor of efficiency function to spare parts life circle cost has been determined. Finally, the assessment method is presented, and a practical example is analyzed. There are two points as for conclusions. (1) the analysis method to cost-effectiveness is an excellent mode on the science basis between the optimal usage of spare parts guarantee budget and the usage standard of its budgets, and it can satisfy the comprehensive balance relationship between a guarantee budget-usage of mission request for spare parts of an aviation department (2) the method of gray system valuation can handle the indetermination information within the analysis of the expenses-effect problem nicely, the analyzing result which has been synthesized can provides a type of path performances for the guarantee effects of spare parts such as good or bad, so as to have a better military and economy value.
This paper presents a novel approach for detecting the presence of valve stiction in control loops using a convolutional neural network (CNN) model. The proposed approach uses a combination of wavelet reconstruction and filtering techniques to preprocess the process variable versus output variable (PV(OP)) plot data before feeding it into the CNN model. The model was trained using simulated data and evaluated using data from the ISDB dataset with the F1 score as the primary evaluation metric. With our proposed approach, the model achieved the F1 score of 0.90, with a precision of 0.87 and recall of 0.93, on the test set. We also investigated the effects of different parameters on the model's performance, such as the number of epochs and output filters of convolutional layers, as well as the use of different model inputs. The results showed that the threshold for wavelet reconstruction had a significant impact on the model's performance and using both PV(OP) plots from filtered and wavelet reconstruction data as input provided better performance. Our findings demonstrate the potential of using CNN models with preprocessed PV(OP) plot data to improve the detection of valve stiction in control loops, and our approach may have broader applications in the field of process control.
The problem of synchronization in networks of neural mass model populations with discrete couplings is consid-ered. The considered network is hybrid one, therefore Mikheev approach is applied to transform it to the network with time-varying delayed couplings. Thus the problem of hybrid network synchronization is reduced to the studying of synchronization in networks with delayed couplings, which was previously solved by analytical means. It is showed that the Laplace matrix spectrum and maximum sampling interval are defining for networks dynamics. The dynamics of 5 neural mass model populations with discrete couplings was simulated for 3 different situations. The first case deal with the asymptotic synchronization, when both maximum eigenvalue of Laplacian and maximum sampling interval are small enough. The second case is about $\varepsilon$ -synchronization, which is achieved for small enough maximum eigenvalue of Laplacian and big sampling intervals. And the last case is desynchronization of oscillations, which has been observed for big values of Laplacian eigenvalues and sampling intervals.
