Xuegang Huang

Robust stability analysis of fractional-order uncertain singular nonlinear system with external disturbance

This paper investigates robust stability for fractional-order (FO) singular nonlinear systems. The FO system is disturbed by external uncertainty and disturbance. A central analysis technique is enabled by proposing a fundamental boundedness lemma, for the first time. This lemma is used for robust stability analysis of FO systems, especially for Mittag-Leffler stability analysis of FO nonlinear systems. More importantly, how to obtain a more accurate bound is given to reduce conservative. An FO proportional-derivative (PD) controller is proposed to normalize the FO singular system. Furthermore, a criterion for stability of the normalized FO nonlinear systems is provided by linear matrix inequalities (LMIs). Finally, two illustrative simulation examples are presented to illustrate effectiveness of the proposed stability notion.

Delay-partitioning approach design for stochastic stability analysis of uncertain neutral-type neural networks with Markovian jumping parameters

This paper investigates the problem of stability analysis for uncertain neutral-type neural networks with Markovian jumping parameters and interval time-varying delays. By separating the delay interval into multiple subintervals, a Lyapunov-Krasovskii methodology is established, which contains triple and quadruple integrals. The time-varying delay is considered to locate into any subintervals, which is different from existing delay-partitioning methods. Based on the proposed delay-partitioning approach, a stability criterion is derived to reduce the conservatism. Numerical examples show the effectiveness of the proposed methods. HighlightsNovel Lyapunov functions are constructed involving triple and quadruple integrals.The delay interval is decomposed into m equivalent subintervals.Newton-Leibniz formulas apply in each subinterval and derive weight-free matrices.A new inequality is used to reduce conservatism by reciprocally convex inequality.

Design of optimal lighting control strategy based on multi-variable fractional-order extremum seeking method

Abstract In recent years, the light-energy consumption accounts for quite a large proportion of total electricity consumption. In this paper, an optimal lighting control strategy is designed for a lighting system with multiple lighting sources, to decrease electric energy consumption and increase energy efficiency. In the proposed control strategy, a novel multi-variable fractional-order extremum seeking control (FO ESC) strategy is implemented in minimizing the light-energy consumption by separately regulating the brightness of multi-lighting sources, while a PID method is applied to guarantee the desired lighting level. The proposed scheme is presented to not only raise the convergence rate and enhance the control accuracy, but also to improve the search efficiency of the minimum light-energy consumption by manipulating the fractional-order. Experimental results including comparison with the corresponding integer-order (IO) ESC show that the light-energy consumption under the proposed strategy can approach a smaller neighborhood of the minimum lighting-energy point more quickly.

Design and stability analysis of multivariate extremum seeking with Newton method

Abstract This paper proposes a multivariate extremum seeking with the Newton method (ES-NM) to improve the control performance for multivariable static and dynamic systems. The structure of the proposed ES-NM is designed to speed up the convergence of the scheme without increasing the oscillation. The influence of unknown Hessian matrix on the convergence speed existed in conventional methods is effectively eliminated in the proposed ES-NM approach. The stability analysis of the proposed ES-NM is given in detail for static and dynamic systems. Comparisons to the existing Gradient based extremum seeking control (ESC) and the Newton based ESC reveal that the proposed ES-NM has a higher probability of improving the convergence speed as well as reducing the chattering performance. Simulation results show advantages of the proposed ES-NM by comparing the multivariate Gradient based and Newton based ESC.

Research on crack detection applications of improved PCNN algorithm in moi nondestructive test method

Abstract On the basis of the Faraday Magneto-optical Effect (FMoE) method, that the polarized light would rotate its polarizing direction when there is the magnetic in its moving direction, a NDT method based on Magnetic optic imaging (MOI) is proposed to detect and identify the crack. In order to identify the crack, the pulsed-couple neural network (PCNN) model based this method is developed and improved to select the threshold dynamically. The output image of the PCNN model is pulsed image and this kind of image is processed by the magnetic domain spots filter. This kind of filter is based on the connection law which can detect the crack in the pulsed image. The detecting system could be used to identify the crack accurate by the above two steps, which would be confirmed by the results.

Fault diagnostics of rolling bearings using feature fusion based BP, RBF and PNN neural networks

This paper employs three neural networks that are BP, RBF and PNN for rolling bearing fault diagnosis and com- pares their performance. The preprocessed vibration signals of rolling bearing provide fused feature vectors after the process of wavelet package decomposition and feature fusion. Then the fused feature vectors serve as the inputs of networks. The fault diagnostic aims to recognize health condition, fault types and fault severity of rolling bearings. The simulation results demon- strate that BP has the best accuracy and very complex computation efforts, and RBF has the fastest classification with the lowest precision. Meanwhile, PNN achieves perfect accuracy and speed that can be received.

Radar emitter identification with bispectrum and hierarchical extreme learning machine

Radar Emitter Identification (REI) has been broadly used in military and civil fields. In this paper, a novel method is proposed for radar emitter signal identification, where the bispectrum estimation of radar signal is extracted and the recent hierarchical extreme learning machine (BS + H-ELM) is adopted for further feature learning and recognition. Conventional REI methods generally rely on the time-difference-of-arrival, carrier frequency, pulse width, pulse amplitude, direction-of-arrival, etc., for signal representation and recognition. However, the increasingly violent electronic confrontation and the emergence of new types of radar signals generally degrade the recognition performance. With this objective, we explore radar emitter signal representation and classification method with the high order spectrum and deep network based H-ELM. After extracting the bispectrum of radar signals, the sparse autoencoder (AE) in H-ELM is employed for feature learning. Simulations on four representative radar signals, namely, the continuous wave (CW), linear frequency modulation wave(LFM), nonlinear frequency modulation wave(NLFM) and binary phase shift keying wave (BPSK), are conducted for performance validation. In comparison to the existing multilayer ELM algorithm and the popular histogram of gradient (HOG) based feature extraction method are proved that the proposal is feasible and potentially applicable in real applications.

Design of an Automatic Defect Identification Method Based ECPT for Pneumatic Pressure Equipment

In this paper, in order to achieve automatic defect identification for pneumatic pressure equipment, an improved feature extraction algorithm eddy current pulsed thermography (ECPT) is presented. The presented feature extraction algorithm contains four elements: data block selection; variable step search; relation value classification; and between-class distance decision function. The data block selection and variable step search are integrated to decrease the redundant computations in the automatic defect identification. The goal of the classification and between-class distance calculation is to select the typical features of thermographic sequence. The main image information can be extracted by the method precisely and efficiently. Experimental results are provided to demonstrate the capabilities and benefits (i.e., reducing the processing time) of the proposed algorithm in automatic defect identification.

Adaptive backstepping control of block-strict-feedback systems with unknown parameters

In this paper, we establish the adaptive backstepping controller designed using the standard technique aiming at the class of block-strict-feedback nonlinear system subject to unknown constant parameters. We have given the tuning function at every step of the design which used for parameters estimation. An illustration example is proposed and then used in the intelligent lighting test platform to prove the practical application of the adaptive backstepping control method.

Robust stability of fractional-order nonlinear systems under sliding mode controller with fractional-order reaching law

In this paper, the problem of robust stability of fractional-order nonlinear systems under sliding mode control (SMC) with fractional-order (FO) switching law is discussed. The proposed FO switching law, involving an FO derivative function, is proven to guarantee that the reaching phase can happen in finite time. The calculation formula of the reaching time is computed. The comparisons between FO and integer-order (IO) switching laws reveal the potential advantages of one controller over the other. The stability criterion of the sliding mode dynamics is derived in terms of linear matrix inequalities (LMIs). The tradeoff between control performance and parameters selection is discussed and visualized. Simulation results are presented to illustrate the effectiveness of the designed FO SMC.