Volume 12, Number 6, 2015
Cyber-physical systems (CPSs) are integrations of networks, computation and physical processes, where embedded computing devices continually sense, monitor, and control the physical processes through networks. Networked industrial processes combining internet, real-time computer control systems and industrial processes together are typical CPSs. With the increasingly frequent cyber-attack, security issues have gradually become key problems for CPSs. In this paper, a cyber-physical system security protection approach for networked industrial processes, i.e., industrial CPSs, is proposed. In this approach, attacks are handled layer by layer from general information technology (IT) security protection, to active protection, then to intrusion tolerance and physical security protection. The intrusion tolerance implemented in real-time control systems is the most critical layer because the real time control system directly affects the physical layer. This novel intrusion tolerance scheme with a closed loop defense framework takes into account the special requirements of industrial CPSs. To illustrate the effectiveness of the CPS security protection approach, a networked water level control system is described as a case study in the architecture analysis and design language (AADL) environment. Simulation results show that 3 types of injected attacks can be quickly defended by using the proposed protection approach.
Face recognition has attracted great interest due to its importance in many real-world applications. In this paper, we present a novel low-rank sparse representation-based classification (LRSRC) method for robust face recognition. Given a set of test samples, LRSRC seeks the lowest-rank and sparsest representation matrix over all training samples. Since low-rank model can reveal the subspace structures of data while sparsity helps to recognize the data class, the obtained test sample representations are both representative and discriminative. Using the representation vector of a test sample, LRSRC classifies the test sample into the class which generates minimal reconstruction error. Experimental results on several face image databases show the effectiveness and robustness of LRSRC in face image recognition.
This paper presents a novel guidance law to intercept non-maneuvering targets with impact angle and lateral acceleration command constraints. Firstly, we formulate the impact angle control to track the desired line-of-sight (LOS) angle, which is achieved by selecting the missile s lateral acceleration to enforce the sliding mode on a sliding surface at impact time. Secondly, we use the Lyapunov stability theory to prove the stability and finite time convergence of the proposed nonlinear sliding surface. Thirdly, we introduce the wavelet neural network (WNN) to adaptively update the additional control command and reduce the high-frequency chattering of sliding mode control (SMC). The proposed guidance law, denoted WNNSMC guidance law with impact angle constraint, combines the SMC methodology with WNN to improve the robustness and reduce the chattering of the system. Finally, numerical simulations are performed to demonstrate the validity and effectiveness of the WNNSMC guidance law.
An accurate and robust approach for tracking and guiding multiple laser beams is developed, which can be applied to the task of beam and target alignment. Multiple laser spots are firstly detected and recognized from the image sequences of the target and laser spots. Then, the contour tracking algorithm based on the chain code is investigated, in which the shape matching scheme based on the invariant moments is employed to distinguish different spots. When occlusion occurs in the multiple spots tracking procedure, the contour tracking combined with Kalman filter prediction is proposed to obtain the positions of multiple spots in real-time. In order to guide 3 spots to align the target, an incremental proportional integral (PI) controller is employed to make the image features of spots converge to the desired ones. Comparative experiments show that, the proposed tracking method can successfully cope with the fast motion, partial or complete occlusion. The experiment results on spots guiding also exhibit the accurate and robust performance of the strategy. The proposed visual system solves the problem of spots mixing, reduces the alignment time, improves the shooting accuracy and has been successfully applied to the experimental platform.
With the increasing demand on higher strip quality, the profile and flatness of hot rolling strips have become subjects of concern, particularly for compact strip product (CSP) hot strip mills. Based on the roll contour, control model, and rolling process, a comprehensive shape control technology is proposed and applied to CSP hot strip mill of Lianyuan steel, which includes optimization and design of the work roll contour and varying contact back-up roll (VCR) plus backup roll contour, analysis of the flatness feedback control model, as well as improvement of the rolling process control system. The application of the technology has significantly improved the shape control performance. The roll wear is improved and the general roll consumption of the finishing mill is reduced by 29.86%. The percentages that satisfy the control target ranges of the average strip flatness and crown are increased by approximately 15.40% and 14.82%, respectively. The rejection rate of grade Q235 due to shape quality problem is reduced monthly by 39.69%, which creates significant economic benefits for the plant.
This paper deals with the problem of the energy system optimization for photovoltaic generators. A great necessity of optimizing the output energy appears as a result of the nonlinearity of the photovoltaic generator operation besides its variable output characteristic under different climatic conditions. As a consequence for the big need to extract maximum energy, many solutions have been proposed in order to have a good operation at the optimum power for photovoltaic systems. In this paper, we further extend this work by using a robust optimization technique based on the first order sliding mode approach to cope with the uncertainty in photovoltaic power generation caused by weather variability and load change. Indeed, we examine by using this control approach the effectiveness of this method and we note the different performance that affects to the system operation. The first order sliding mode maximum power point tracking controller is presented in detail in this paper. Then, a detailed study of algorithm stability has been carried out. The robustness and stability of the proposed sliding mode controller are investigated against load variations and weather changes. The simulation results confirm the effectiveness, the good and improved performance of the proposed sliding mode method in the presence of load variations and environment changes for direct current/direct current (DC/DC) boost converter.
The problem of the chattering phenomenon is still the main drawback of the classical sliding mode control. To resolve this problem, a discrete second order sliding mode control via input-output model is proposed in this paper. The proposed control law is synthesized for decouplable multivariable systems. A robustness analysis of the proposed discrete second order sliding mode control is carried out. Simulation results are presented to illustrate the effectiveness of the proposed strategy.
Perfect tracking of the tip position of a flexible-link manipulator (FLM) is unable to be achieved by causal control because it is a typical non-minimum phase system. Combined with non-causal stable inversion, an adaptive iterative learning control scheme based on Fourier basis function is presented for the tip trajectory tracking of FLM performing repetitive tasks. In this method, an iterative identification algorithm is used to construct the Fourier basis function space model of the manipulator, and a pseudoinverse type iterative learning law is designed to approximate the stable inversion of the non-minimum phase system, which guarantees the convergence and robustness of the control system. Simulation results show the performance and effectiveness of the proposed scheme.
A network time-delay compensation method based on time-delay prediction and implicit proportional-integral-based generalized predictive controller (PIGPC) is proposed. The least squares support vector machine (LSSVM) is used to predict the current time-delay, the parameters of the least squares support vector machine are optimized by particle swarm optimization (PSO) algorithm, and the predicted time-delay is used instead of the actual time-delay as the parameters of the network time-delay compensation controller. In order to improve the compensation effect of implicit generalized predictive controller (GPC), this paper puts forward an implicit generalized predictive control algorithm with proportional-integral-based (PI) structure and designs the controller based on implicit PIGPC. Through the simulation results, the effectiveness of this design in the paper is verified.
This paper deals with the robust stability of time-delay system with time-varying uncertainties via homogeneous polynomial Lyapunov-Krasovskii functions (HPLKF). We give a sufficient condition to demonstrate that the system is asymptotically stable. A new class of Lyapunov-Krasovskii function is introduced, whose main feature is that the conservativeness due to uncertainties is reduced. Numerical examples illustrate the effectiveness of our method.
This article derives a new scheme to an adaptive observer for a class of fractional order systems. Global asymptotic convergence for joint state-parameter estimation is established for linear time invariant single-input single-output systems. For such fractional order systems, it is proved that all the signals in the resulting closed-loop system are globally uniformly bounded, the state and parameter estimation errors converge to zero. Potential applications of the presented adaptive observer include online system identification, fault detection, adaptive control of fractional order systems, etc. Numerical simulation examples are presented to demonstrate the performance of the proposed adaptive observer.
This paper addresses a robust H∞ filter design problem for nonlinear systems with time-varying delay through Takagi-Sugeno (T-S) fuzzy model approach. Firstly, by introducing free-weighting matrix method combined with a matrix decoupling approach and adopting an improved integral inequality method without ignoring any integral term, less conservative results are achieved. Next, based on the model, new delay-dependent sufficient conditions are derived, which are less conservative than the existing ones via solving the linear matrix inequalities (LMIs). Lastly, simulations show a significant improvement over the previous results.
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