Volume 7, Number 4, 2010
Display Mode： |
This paper investigates the problem of robust exponential stability for neutral systems with time-varying delays and nonlinear perturbations. Based on a novel Lyapunov functional approach and linear matrix inequality technique, a new delay-dependent stability condition is derived. Since the model transformation and bounding techniques for cross terms are avoided, the criteria proposed in this paper are less conservative than some previous approaches by using the free-weighting matrices. One numerical example is presented to illustrate the effectiveness of the proposed results.
This paper presents an approach in designing a robust controller for vehicle suspensions considering changes in vehicle inertial properties.A four-degree-of-freedom half-car model with active suspension is studied in this paper,and three main performance requirements are considered.Among these requirements,the ride comfort performance is optimized by minimizing the H norm of the transfer function from the road disturbance to the sprung mass acceleration,while the road holding performance and the suspension deflection limitation are guaranteed by constraining the generalized H2 (GH2) norms of the transfer functions from the road disturbance to the dynamic tyre load and the suspension deflection to be less than their hard limits,respectively.At the same time,the controller saturation problem is considered by constraining its peak response output to be less than a given limit using the GH2 norm as well.By solving the finite number of linear matrix inequalities (LMIs) with the minimization optimization procedure,the controller gains,which are dependent on the time-varying inertial parameters,can be obtained.Numerical simulations on both frequency and bump responses show that the designed parameter-dependent controller can achieve better active suspension performance compared with the passive suspension in spite of the variations of inertial parameters.
This paper is concerned with the non-fragile H filter design problem for uncertain discrete-time Takagi-Sugeno (T-S) fuzzy systems with time delay.To begin with,the T-S fuzzy system is transformed to an equivalent switching fuzzy system.Then,based on the piecewise Lyapunov function and matrix decoupling technique,a new delay-dependent non-fragile H filtering method is proposed for the switching fuzzy system.The proposed condition is less conservative than the previous results.Since only a set of LMIs is involved,the filter parameters can be solved directly.Finally,a design example is provided to illustrate the validity of the proposed method.
An observer-based adaptive iterative learning control (AILC) scheme is developed for a class of nonlinear systems with unknown time-varying parameters and unknown time-varying delays.The linear matrix inequality (LMI) method is employed to design the nonlinear observer.The designed controller contains a proportional-integral-derivative (PID) feedback term in time domain.The learning law of unknown constant parameter is differential-difference-type,and the learning law of unknown time-varying parameter is difference-type.It is assumed that the unknown delay-dependent uncertainty is nonlinearly parameterized.By constructing a Lyapunov-Krasovskii-like composite energy function (CEF),we prove the boundedness of all closed-loop signals and the convergence of tracking error.A simulation example is provided to illustrate the effectiveness of the control algorithm proposed in this paper.
This paper deals with the H control problems of Markovian jump systems with mode-dependent time delays.First,considering the mode-dependent time delays,a different delay-dependent H performance condition for Markovian jump systems is proposed by constructing an improved Lyapunov-Krasovskii function.Based on this new H disturbance attenuation criterion,a full-order dynamic output feedback controller that ensures the exponential mean-square stability and a prescribed H performance level for the resulting closed-loop system is designed.Illustrative numerical examples are provided to demonstrate the effectiveness of the proposed approach.
This note concerns the problem of the robust stability of uncertain neutral systems with time-varying delay and saturating actuators.The system considered is continuous in time with norm bounded parametric uncertainties.By incorporating the free weighing matrix approach developed recently,some new delay-dependent stability conditions in terms of linear matrix inequalities (LMIs) with some tuning parameters are obtained.An estimate of the domain of attraction of the closed-loop system under a priori designed controller is proposed.The approach is based on a polytopic description of the actuator saturation nonlinearities and the LyapunovKrasovskii method.Numerical examples are used to demonstrate the effectiveness of the proposed design method.
In this paper,a parallel fuzzy proportional plus fuzzy integral plus fuzzy derivative (FP+FI+FD) controller is proposed.It is derived from the conventional parallel proportional-integral-derivative (PID) controller.It preserves the linear structure of a conventional parallel PID controller,with analytical formulas.The final shape of the controller is a discrete-time fuzzy version of a conventional parallel PID controller.Computer simulations are performed to evaluate the performance of the FP+FI+FD controller for setpoint tracking and load-disturbance rejection for some complex processes,such as first-and second-order processes with delay,inverse response process with and without delay and higher order processes.Also,the performance of the proposed fuzzy controller is evaluated experimentally on highly nonlinear liquid-flow process with a hysteresis characteristic due to a pneumatic control valve.The simulation and real time control is done using National Instrument TM hardware and software (LabVIEW TM).The response of the FP+FI+FD controller is compared with the conventional parallel PID controller,tuned with the Ziegler-Nichols (Z-H) and Astrm-Hgglund (A-H) tuning technique.It is observed that the FP+FI+FD controller performed much better than the conventional PI/PID controller.Simulation and experimental results demonstrate the effectiveness of the proposed parallel FP+FI+FD controller.
This paper addresses the observer design problem for a class of nonlinear descriptor systems whose nonlinear terms are slope-restricted.The full-order observer is formulated as a nonlinear descriptor system.A linear matrix inequality (LMI) condition is derived to construct the full-order observer.The existence and uniqueness of the solution to the obtained observer system are guaranteed.Furthermore,under the same LMI condition and a common assumption,a reduced-order observer is designed.Finally,the design methods are reduced to a strict LMI problem and illustrated by a numerical example.
A class of formulas for converting linear matrix mappings into conventional linear mappings are presented.Using them,an easily computable numerical method for complete parameterized solutions of the Sylvester matrix equation AX-EXF=BY and its dual equation XA-FXE=YC are provided.It is also shown that the results obtained can be used easily for observer design.The method proposed in this paper is universally applicable to linear matrix equations.
The problem of indirect adaptive fuzzy and impulsive control for a class of nonlinear systems is investigated.Based on the approximation capability of fuzzy systems,a novel adaptive fuzzy and impulsive control strategy with supervisory controller is developed.With the help of a supervisory controller,global stability of the resulting closed-loop system is established in the sense that all signals involved are uniformly bounded.Furthermore,the adaptive compensation term of the upper bound function of the sum of residual and approximation error is adopted to reduce the effects of modeling error.By the generalized Barbalat's lemma,the tracking error between the output of the system and the reference signal is proved to be convergent to zero asymptotically.Simulation results illustrate the effectiveness of the proposed approach.
This paper investigates the robust tracking control problem for a class of nonlinear networked control systems (NCSs) using the Takagi-Sugeno (T-S) fuzzy model approach.Based on a time-varying delay system transformed from the NCSs,an augmented Lyapunov function containing more useful information is constructed.A less conservative sufficient condition is established such that the closed-loop systems stability and time-domain integral quadratic constraints (IQCs) are satisfied while both time-varying networkinduced delays and packet losses are taken into account.The fuzzy tracking controllers design scheme is derived in terms of linear matrix inequalities (LMIs) and parallel distributed compensation (PDC).Furthermore,robust stabilization criterion for nonlinear NCSs is given as an extension of the tracking control result.Finally,numerical simulations are provided to illustrate the effectiveness and merits of the proposed method.
A common and critical operation for wireless sensor networks is data gathering.The efficient clustering of a sensor network that can save energy and improve coverage efficiency is an important requirement for many upper layer network functions.This study concentrates on how to form clusters with high uniformity while prolonging the network lifetime.A novel clustering scheme named power-and coverage-aware clustering (PCC) is proposed,which can adaptively select cluster heads according to a hybrid of the nodes residual energy and loyalty degree.Additionally,the PCC scheme is independent of node distribution or density,and it is free of node hardware limitations,such as self-locating capability and time synchronization.Experiment results show that the scheme performs well in terms of cluster size (and its standard deviation),number of nodes alive over time,total energy consumption,etc.
The problem of guaranteed cost active fault-tolerant controller (AFTC) design for networked control systems (NCSs) with both packet dropout and transmission delay is studied in this paper.Considering the packet dropout and transmission delay,a piecewise constant controller is adopted.With a guaranteed cost function,optimal controllers whose number is equal to the number of actuators are designed,and the design process is formulated as a convex optimal problem that can be solved by existing software.The control strategy is proposed as follows:when actuator failures appear,the fault detection and isolation unit sends out the information to the controller choosing strategy,and then the optimal stabilizing controller with the smallest guaranteed cost value is chosen.Two illustrative examples are given to demonstrate the effectiveness of the proposed approach.By comparing with the existing methods,it can be seen that our method has a better performance.
Two model reference adaptive system (MRAS) estimators are developed for identifying the parameters of permanent magnet synchronous motors (PMSM) based on the Lyapunov stability theorem and the Popov stability criterion,respectively.The proposed estimators only need online measurement of currents,voltages,and rotor speed to effectively estimate stator resistance,inductance,and rotor flux-linkage simultaneously.The performance of the estimators is compared and verified through simulations and experiments,which show that the two estimators are simple,have good robustness against parameter variation,and are accurate in parameter tracking.However,the estimator based on the Popov stability criterion,which can overcome parameter variation in a practical system,is superior in terms of response speed and convergence speed since there are both proportional and integral units in the estimator,in contrast to only one integral unit in the estimator based on the Lyapunov stability theorem.In addition,the estimator based on the Popov stability criterion does not need the expertise that is required in designing a Lyapunov function.
Aimed at the deficiencies of resources based time Petri nets (RBTPN) in doing scheduling analysis for distributed real-time embedded systems,the assemblage condition of complex scheduling sequences is presented to easily compute scheduling length and simplify scheduling analysis.Based on this,a new hierarchical RBTPN model is proposed.The model introduces the definition of transition border set,and represents it as an abstract transition.The abstract transition possesses all resources of the set,and has the highest priority of each resource;the execution time of abstract transition is the longest time of all possible scheduling sequences.According to the characteristics and assemblage condition of RBTPN,the refinement conditions of transition border set are given,and the conditions ensure the correction of scheduling analysis.As a result,it is easy for us to understand the scheduling model and perform scheduling analysis.
Steganographic techniques accomplish covert communication by embedding secret messages into innocuous digital images in ways that are imperceptible to the human eye.This paper presents a novel passive steganalysis strategy in which the task is approached as a pattern classification problem.A critical part of the steganalyser design depends on the selection of informative features.This paper is aimed at proposing a novel attack with improved performance indices with the following implications:1) employing higher order statistics from a curvelet sub-band image representation that offers better discrimination ability for detecting stego anomalies in images,as compared to other conventional wavelet transforms;2) increasing the sensitivity and specificity of the system by the feature reduction phase;3) realizing the system using an efficient classification engine,a neuro-C4.5 classifier,which provides better classification rate.An extensive experimental evaluation on a database containing 5600 clean and stego images shows that the proposed scheme is a state-of-the-art steganalyser that outperforms other previous steganalytic methods.
In this paper,the edge detection for a medical image is performed based on Sobel operator,and the bounding box is obtained,by which the effective medical sub-image is extracted.Then,the centroid and the normalized central moments of the medical sub-image are calculated,and the rotation angle is obtained by minimizing the second-order central moment based on its rotation invariance.Finally,the whole medical image is rotated around the centroid by- to correct the tilted image.Furthermore,inspired by the uniformity degree of the image,the rotation angle is revised,which achieves a better correction effect and performance.The experimental results show that the proposed algorithms are fairly reliable and accurate for the determination of tilt angles,and are practical and effective tilt correction techniques.
This paper considers the problems of target tracking and obstacle avoidance for multi-agent systems.To solve the problem that multiple agents cannot effectively track the target while avoiding obstacle in dynamic environment,a novel control algorithm based on potential function and behavior rules is proposed.Meanwhile,the interactions among agents are also considered.According to the state whether an agent is within the area of its neighbors influence,two kinds of potential functions are presented.Meanwhile,the distributed control input of each agent is determined by relative velocities as well as relative positions among agents,target and obstacle.The maximum linear speed of the agents is also discussed.Finally,simulation studies are given to demonstrate the performance of the proposed algorithm.
In this paper,decentralized methods of optimally rigid graphs generation for formation control are researched.The notion of optimally rigid graph is first defined in this paper to describe a special kind of rigid graphs.The optimally rigid graphs can be used to decrease the topology complexity of graphs while maintaining their shapes.To minimize the communication complexity of formations,we study the theory of optimally rigid formation generation.First,four important propositions are presented to demonstrate the feasibility of using a decentralized method to generate optimally rigid graphs.Then,a formation algorithm for multi-agent systems based on these propositions is proposed.At last,some simulation examples are given to show the efficiency of the proposed algorithm.
A semi-blind adaptive beamforming scheme is proposed for wireless systems that employ high-throughput quadrature amplitude modulation signalling.A minimum number of training symbols,equal to the number of receiver antenna array's elements,are first utilised to provide a rough initial least squares estimate of the beamformer's weight vector.A concurrent constant modulus algorithm and soft decision-directed scheme is then applied to adapt the beamformer.This semi-blind adaptive beamforming scheme is capable of converging fast to the minimum mean-square-error beamforming solution,as demonstrated in our simulation study.
This paper presents a mathematical model that analyzes the throughput of the IEEE 802.11b distributed coordination function (DCF) with the collision aware rate adaptation (CARA) algorithm.IEEE 802.11 WLANs provide multiple transmission rates to improve system throughput by adapting the transmission rate to the current channel conditions.The system throughput is determined by some stations using low transmission rates due to bad channel conditions.CARA algorithm does not disturb the existing IEEE 802.11b formats and it can be easily incorporated into the commercial wireless local area networks (WLAN) devices.Finally,we verify our findings with simulation.
In the processes of product innovation and design,it is important for the designers to find and capture customer's focus through customer requirement weight calculation and ranking.Based on the fuzzy set theory and Euclidean space distance,this paper puts forward a method for customer requirement weight calculation called Euclidean space distances weighting ranking method.This method is used in the fuzzy analytic hierarchy process that satisfies the additive consistent fuzzy matrix.A model for the weight calculation steps is constructed;meanwhile,a product innovation design module on the basis of the customer requirement weight calculation model is developed.Finally,combined with the instance of titanium sponge production,the customer requirement weight calculation model is validated.By the innovation design module,the structure of the titanium sponge reactor has been improved and made innovative.
With the fast development of business logic and information technology,today s best solutions are tomorrow's legacy systems.In China,the situation in the education domain follows the same path.Currently,there exists a number of e-learning legacy assets with accumulated practical business experience,such as program resource,usage behaviour data resource,and so on.In order to use these legacy assets adequately and efficiently,we should not only utilize the explicit assets but also discover the hidden assets.The usage behaviour data resource is the set of practical operation sequences requested by all users.The hidden patterns in this data resource will provide users practical experiences,which can benefit the service composition in service-oriented architecture (SOA) migration.Namely,these discovered patterns will be the candidate composite services (coarse-grained) in SOA systems.Although data mining techniques have been used for software engineering tasks,little is known about how they can be used for service composition of migrating an e-learning legacy system (MELS) to SOA.In this paper,we propose a service composition approach based on sequence mining techniques for MELS.Composite services found by this approach will be the complementation of business logic analysis results of MELS.The core of this approach is to develop an appropriate sequence mining algorithm for mining related data collected from an e-learning legacy system.According to the features of execution trace data on usage behaviour from this e-learning legacy system and needs of further pattern analysis,we propose a sequential mining algorithm to mine this kind of data of the legacy system.For validation,this approach has been applied to the corresponding real data,which was collected from the e-learning legacy system;meanwhile,some investigation questionnaires were set up to collect satisfaction data.The investigation result is 90 % the same with the result obtained through our approach.
Agile intelligent manufacturing is one of the new manufacturing paradigms that adapt to the fierce globalizing market competition and meet the survival needs of the enterprises,in which the management and control of the production system have surpassed the scope of individual enterprise and embodied some new features including complexity,dynamicity,distributivity,and compatibility.The agile intelligent manufacturing paradigm calls for a production scheduling system that can support the cooperation among various production sectors,the distribution of various resources to achieve rational organization,scheduling and management of production activities.This paper uses multi-agents technology to build an agile intelligent manufacturing-oriented production scheduling system.Using the hybrid modeling method,the resources and functions of production system are encapsulated,and the agent-based production system model is established.A production scheduling-oriented multi-agents architecture is constructed and a multi-agents reference model is given in this paper.
In this paper,we propose a new method to estimate the relationship between software reliability and software development cost taking into account the complexity for developing the software system and the size of software intended to develop during the implementation phase of the software development life cycle.On the basis of estimated relationship,a set of empirical data has been used to validate the correctness of the proposed model by comparing the result with the other existing models.The outcome of this work shows that the method proposed here is a relatively straightforward one in formulating the relationship between reliability and cost during implementation phase.
IJAC CiteScore keeps raising in 2018
IJAC receives a CiteScore as high as 2.34 in 2018 which is 1.37 times higher than that in 2017. Being in the top 15%, it ranks #69 among 460 journals in respective categories.
【Open Access】Download highlight papers for free
During the past two years, IJAC has published a series of high-quality papers by famous scholars around the world, including professor Tomaso Poggio from MIT, professor Brian Anderson from Australian National University, professor Yike Guo from Imperial College London, etc.. All the papers are open access, covering topics of Deep Learning, Artificial Intelligence, Neural Networks, and so on. You’ll never miss it!
2019 International Academic Conference List
International Journal of Automation and Computing (IJAC) maintains this list of conferences at the beginning of each year that are highly relevant to current hot research topics, including artificial intelligence, machine learning, computer vision, pattern recognition, robotics and automatic control.