This paper focuses on the problem of $\mathcal{L}_{2}$-$\mathcal{L}_{\infty}$ dynamic output feedback controller design for nonlinear switched systems with nonlinear perturbations in the Takagi-Sugeno fuzzy framework. First, the average dwell time approach is used to stabilize a nonlinear switched system exponentially under an arbitrary switching law. Then, based on the technique of piecewise Lyapunov functions, a fuzzy-rule-dependent dynamic output feedback controller is designed to ensure that the overall closed-loop system is exponentially stable with a weighted $\mathcal{L}_{2}$-$\mathcal{L}_{\infty}$ performance level $\left(\gamma,\alpha \right)$. The solvability condition for the desired dynamic output feedback controller is derived using a linearization technique. It is shown that the controller parameters can be obtained as solutions to a set of strict linear matrix inequalities that are numerically solvable with available standard software. Finally, two simulation examples illustrate effectiveness of the developed technique, including cognitive-radio systems.

A rolling bearing vibration signal fault feature enhancement method based on adaptive complete ensemble empirical mode decomposition with adaptive noise algorithm (CEEMDAN) and maximum correlated kurtosis deconvolution (MCKD) is proposed to address the issue that rolling bearings are prone to noise in the early stage and difficult to extract feature information accurately. The method uses the CEEMDAN algorithm to reduce the noise of the rolling bearing vibration signal in the first step; then, the MCKD algorithm is used to deconvolve the signal to enhance the weak shock components in the signal and improve the SNR. Finally, the envelope spectrum analysis is performed to extract the feature frequencies. Simulation and experimental results show that the CEEMDAN-MCKD method can highlight the fault characteristic frequency and multiplier frequency better than other methods and realize the characteristic enhancement of incipient fault vibration signals of rolling bearings under constant and variable operating conditions.

This paper investigates the adaptive control for a class of uncertain nonlinear systems under denial-of-service (DoS) attacks. We analyze the closed-loop system stability under DoS attacks in terms of attack duration, attack frequency and resting time duration respectively. Three scenarios of DoS attacks against the system stability are considered. Firstly, it is shown that if the duration of each attack is less than a given constant, asymptotical convergence of system output is still preserved. Secondly, if the bounds on the frequency and duration of attacks with respect to overall intervals meet certain conditions, the proposed event-triggered control scheme guarantees that all the closed-loop signals are globally bounded and the stabilization error converges to a ball with a radius arbitrarily small. Thirdly, if resting time duration meets certain conditions after an arbitrarily long attack, closed-loop boundedness is still preserved. Simulation results are shown to illustrate the effectiveness of the proposed control schemes.

Fault diagnosis is a challenging topic for complex industrial systems due to the varying environments such systems find themselves in. In order to improve the performance of fault diagnosis, this study designs a novel approach by using particle swarm optimization (PSO) with wavelet mutation and least square support (LSSVM). The implementation entails the following three steps. Firstly, the original signals are decomposed through an orthogonal wavelet packet decomposition algorithm. Secondly, the decomposed signals are reconstructed to obtain the fault features. Finally, the extracted features are used as the inputs of the fault diagnosis model established in this research to improve classification accuracy. This joint optimization method not only solves the problem of PSO falling easily into the local extremum, but also improves the classification performance of fault diagnosis effectively. Through experimental verification, the wavelet mutation particle swarm optimazation and least sqaure support vector machine ( WMPSO-LSSVM) fault diagnosis model has a maximum fault recognition efficiency that is 12% higher than LSSVM and 9% higher than extreme learning machine (ELM). The error of the corresponding regression model under the WMPSO-LSSVM algorithm is 0.365 less than that of the traditional linear regression model. Therefore, the proposed fault scheme can effectively identify faults that occur in complex industrial systems.

To solve the problem that fault diagnosis accuracy of complex equipment bearings is not high due to the complexity of its structure and the environment, a cooperative algorithm for fault diagnosis of complex equipment bearings based on EEMD and SVM is proposed. First, the vibration signal of the bearings is decomposed by EEMD. Second, the correlation coefficient and kurtosis value are selected as the evaluation indexes for the IMF components after decomposition, and the weights of the parameters are set dynamically by the mean-guided weight method. Then, the IMF components are filtered by an improved genetic algorithm to obtain the optimal IMF component combination, which can effectively eliminate redundant components and retain as much fault information as possible. Next, using the orthogonality of IMF components, the energy distribution of the selected IMF components is calculated as the eigenvector. Finally, using the advantage of accurate classification of SVM in small samples, the fault status of complex equipment bearings can be identified. The effectiveness of the algorithm model is proven by example simulation data, and the model has certain scalability and applicability in engineering.