Design of extended Kalman filtering neural network control system based on particle swarm identification of nonlinear U-model

This paper studies the modelling of a class of nonlinear plants with known structures but unknown parameters and proposes a general nonlinear U-model expression. The particle swarm optimization algorithm is used to identify the time-varying parameters of the nonlinear U-model online, which solves the identification problem of the nonlinear U-model system. Newton iterative algorithm is used for nonlinear model transformation. Extended Kalman filter (EKF) is used as the learning algorithm of radial basis function (RBF) neural network to solve the interference problem in a nonlinear system. After determining the number of network nodes in the neural network, EKF can simultaneously determine the network threshold and weight matrix, use the online learning ability of the neural network, adjust the network parameters, make the system output track the ideal output, and improve the convergence speed and anti-noise capability of the system. Finally, simulation examples are used to verify the identification effect of the particle swarm identification algorithm based on the U-model and the effectiveness of the extended Kalman filtering neural network control system based on particle swarm identification

The Design of Robust Controller for Networked Control System with Time Delay

This paper considers the stability and H∞ control problem of networked control systems with time delay. Taking into account the influence of network with delay, unknown input disturbance, and uncertainties of the system modeling, meanwhile we establish a precise, closed-loop model for networked control systems with time delay. By selecting a proper Lyapunov-Krasovskii function and using Lyapunov theorem, a sufficient condition for stability of the system in the form of LMI is demonstrated, corresponding controller parameters are acquired, and the convergence of the control algorithm is proved. The simulation example shows that the construction of the network robust control system with time delay indeed improves the stability performance of the system, which indicates the effectiveness of the design

Fault Detection for Interval Type-2 T-S Fuzzy Networked Systems via Event-Triggered Control

This paper investigates the event-triggered fault diagnosis (FD) problem for interval type-2 (IT2) Takagi–Sugeno (T-S) fuzzy networked systems. Firstly, an FD fuzzy filter is proposed by using IT2 T-S fuzzy theory to generate a residual signal. This means that the FD filter premise variable needs to not be identical to the nonlinear networked systems (NNSs). The evaluation functions are referenced to determine the occurrence of system faults. Secondly, under the event-triggered mechanism, a fault residual system (FRS) is established with parameter uncertainty, external disturbance and time delay, which can reduce signal transmission and communication pressure. Thirdly, the progressive stability of the fault residual system is guaranteed by using the Lyapunov theory. For the energy bounded condition of external noise interference, the performance criterion is established using linear matrix inequalities. The matrix parameters of the target FD filter are obtained by the convex optimization method. A less conservative fault diagnosis method can be obtained. Finally, the simulation example is provided to illustrate the effectiveness and the practicalities of the proposed theoretical method

Fault Detection for Interval Type-2 T-S Fuzzy Networked Systems via Event-Triggered Control

This paper investigates the event-triggered fault diagnosis (FD) problem for interval type-2 (IT2) Takagi–Sugeno (T-S) fuzzy networked systems. Firstly, an FD fuzzy filter is proposed by using IT2 T-S fuzzy theory to generate a residual signal. This means that the FD filter premise variable needs to not be identical to the nonlinear networked systems (NNSs). The evaluation functions are referenced to determine the occurrence of system faults. Secondly, under the event-triggered mechanism, a fault residual system (FRS) is established with parameter uncertainty, external disturbance and time delay, which can reduce signal transmission and communication pressure. Thirdly, the progressive stability of the fault residual system is guaranteed by using the Lyapunov theory. For the energy bounded condition of external noise interference, the performance criterion is established using linear matrix inequalities. The matrix parameters of the target FD filter are obtained by the convex optimization method. A less conservative fault diagnosis method can be obtained. Finally, the simulation example is provided to illustrate the effectiveness and the practicalities of the proposed theoretical method

Nonfragile H∞ Filter Design for Nonlinear Continuous-Time System with Interval Time-Varying Delay

This paper investigates nonfragile H∞ filter design for a class of continuous-time delayed Takagi-Sugeno (T-S) fuzzy systems with interval time-varying delays. Filter parameters occur multiplicative gain variations according to the filter’s implementation, to handle this variations, a nonfragile H∞ filter is presented and a novel filtering error system is established. The nonfragile H∞ filter guarantees the filtering error system to be asymptotically stable and satisfies given H∞ performance index. By constructing a novel Lyapunov-Krasovskii function and using the linear matrix inequality (LMI), delay-dependent conditions are exploited to derive sufficient conditions for nonfragile designing H∞ filter. Using new matrix decoupling method to reduce the computational complexity, the filter parameters can be obtained by solving a set of linear matrix inequalities (LMIs). Finally, numerical examples are given to show the effectiveness of the proposed method

Experimental Investigation of the Technical Performances of SRX-Stabilized Graded Macadam

The technical performances of Solution Road RomixSoilfix- (SRX-) stabilized graded macadam (SSGM) are investigated to promote its application. The specimen curing conditions were proposed to improve the test efficiency by analyzing the influence of curing temperature on the unconfined compressive strength and moisture content variation. Moreover, the mechanical properties (e.g., CBR, strength, and resilient modulus) and pavement performances (e.g., temperature shrinkage, water stability, and freezing stability) of SSGM were evaluated through laboratory tests. The results show that the recommended curing temperature in the drying oven should be 100°C ± 2°C and the recommended curing time should not be less than 16 h. Furthermore, the CBR, unconfined compressive and splitting strengths, and resilient modulus of SSGM increase with the content of SRX stabilizer. The temperature shrinkage coefficient is approximately 15% of the cement-stabilized grading crushed stone. The dry-wet recovery strength ratio is approximately 96% after four dry-wet cycles. The freeze-thaw recovery strength ratio is approximately 58% after five freeze-thaw cycles. The freezing stability of SSGM can be improved by increasing the content of SRX stabilizer. The technical performances of SSGM should fulfill the technical requirements

A comparative study of volatile components in green, oolong and black teas by using comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry and multivariate data analysis

The difference of volatile components in green, oolong and black teas was studied by using comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC x GC-TOFMS). Simultaneous distillation extraction was proved to be a suitable technique to extract the analytes with interest. A total of 450 compounds were tentatively identified with comparison to the standard mass spectra in available databases, retention index on the first dimension and structured chromatogram. 33 tea samples, including 12,12 and 9 samples of green, oolong and black tea were analyzed by using GC x GC-TOFMS. After peak alignment, around 3600 peaks were detected. Partial least squares - discriminant analysis and hierarchical cluster analysis were used to classify these samples, then non-parametric hypothesis test (Mann-Whitney U test) and the variable importance in the projection (VIP) were applied to discover the key components to distinguish the three types of tea with significant difference amongst them. 74 differential compounds are defined to interpret the chemical differences of 3 types of tea. This study shows the power of GC x GC-TOFMS method combined with multivariate data analysis to investigate natural products with high complexity for information extraction. (c) 2013 Elsevier B.V. All rights reserved