Volume 6, Number 2, 2009
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Database applications are becoming increasingly popular, mainly due to the advanced data management facilities that the underlying database management system offers compared against traditional legacy software applications. The interaction, however, of such applications with the database system introduces a number of issues, among which, this paper addresses the impact analysis of the changes performed at the database schema level. Our motivation is to provide the software engineers of database applications with automated methods that facilitate major maintenance tasks, such as source code corrections and regression testing, which should be triggered by the occurrence of such changes. The presented impact analysis is thus two-folded: the impact is analysed in terms of both the affected source code statements and the affected test suites concerning the testing of these applications. To achieve the former objective, a program slicing technique is employed, which is based on an extended version of the program dependency graph. The latter objective requires the analysis of test suites generated for database applications, which is accomplished by employing testing techniques tailored for this type of applications. Utilising both the slicing and the testing techniques enhances program comprehension of database applications, while also supporting the development of a number of practical metrics regarding their maintainability against schema changes. To evaluate the feasibility and effectiveness of the presented techniques and metrics, a software tool, called DATA, has been implemented. The experimental results from its usage on the TPC-C case study are reported and analysed.
In this paper, adaptive variable structure neural control is presented for a class of uncertain multi-input multi-output (MIMO) nonlinear systems with state time-varying delays and unknown nonlinear dead-zones. The unknown time-varying delay uncer- tainties are compensated for using appropriate Lyapunov-Krasovskii functionals in the design. The approach removes the assumption of linear function outside the deadband without necessarily constructing a dead-zone inverse as an added contribution. By utilizing the integral-type Lyapunov function and introducing an adaptive compensation term for the upper bound of the residual and optimal ap- proximation error as well as the dead-zone disturbance, the closed-loop control system is proved to be semi-globally uniformly ultimately bounded. In addition, a modified adaptive control algorithm is given in order to avoid the high-frequency chattering phenomenon. Simulation results demonstrate the effectiveness of the approach.
This paper presents an improved nonlinear system identification scheme using di?erential evolution (DE), neural network (NN) and Levenberg Marquardt algorithm (LM). With a view to achieve better convergence of NN weights optimization during the training, the DE and LM are used in a combined framework to train the NN. We present the convergence analysis of the DE and demonstrate the efficacy of the proposed improved system identification algorithm by exploiting the combined DE and LM training of the NN and suitably implementing it together with other system identification methods, namely NN and DE+NN on a number of examples including a practical case study. The identification results obtained through a series of simulation studies of these methods on different nonlinear systems demonstrate that the proposed DE and LM trained NN approach to nonlinear system identification can yield better identification results in terms of time of convergence and less identification error.
In this paper, a new fuzzy-neural adaptive control approach is developed for a class of single-input and single-output (SISO) nonlinear systems with unmeasured states. Using fuzzy neural networks to approximate the unknown nonlinear functions, a fuzzy- neural adaptive observer is introduced for state estimation as well as system identification. Under the framework of the backstepping design, fuzzy-neural adaptive output feedback control is constructed recursively. It is proven that the proposed fuzzy adaptive control approach guarantees the global boundedness property for all the signals, driving the tracking error to a small neighbordhood of the origin. Simulation example is included to illustrate the effectiveness of the proposed approach.
Conventional sliding mode controllers are based on the assumption of switching control, but a well-known drawback of such controllers is the chattering phenomenon. To overcome the undesirable chattering effects, the discontinuity in the control law can be smoothed out in a thin boundary layer neighboring the switching surface. In this paper, rigorous proofs of the boundedness and convergence properties of smooth sliding mode controllers are presented. This result corrects flawed conclusions previously reached in the literature. An illustrative example is also presented in order to confirm the convergence of the tracking error vector to the defined bounded region.
The H filtering problem for continuous-time polytopic uncertain time-delay systems is investigated. Attention is focused on the design of full-order filters guaranteeing a prescribed H attenuation level for the filtering error system. First, a simple alternative proof is given for an improved linear matrix inequality (LMI) representation of H performance. Then, based on the performance criterion which keeps Lyapunov matrices out of the product of system dynamic matrices, a suficient condition for the existence of robust estimators is formulated in terms of LMIs, and the corresponding filter design is cast into a convex optimization problem which can be effciently handled by using standard numerical algorithms. It is shown that the proposed design strategy allows the use of parameter-dependent Lyapunov functions and hence it is less conservative than some earlier results. A numerical example is employed to demonstrate the feasibility and advantage of the proposed design.
In this paper, the dynamic observer-based controller design for a class of neutral systems with H control is considered. An observer-based output feedback is derived for systems with polytopic parameter uncertainties. This controller assures delay-dependent stabilization and H norm bound attenuation from the disturbance input to the controlled output. Numerical examples are provided for illustration and comparison of the proposed conditions.
The problem of H filtering for polytopic Delta operator linear systems is investigated. An improved H performance criterion is presented based on the bounded real lemma. Upon the improved performance criterion, a sufficient condition for the existence of parameter-dependent H filtering is derived in terms of linear matrix inequalities. The designed filter can be obtained from the solution of a convex optimization problem. The filter design makes full use of the parameter-dependent approach, which leads to a less conservative result than conventional design methods. A numerical example is given to illustrate the effectiveness of the proposed approach.
A novel output-feedback adaptive learning control approach is developed for a class of linear time-delay systems. Three kinds of uncertainties: time delays, number of time delays, and system parameters are all assumed to be completely unknown, which is dfferent from the previous work. The design procedure includes two steps. First, according to the given periodic desired reference output and the allowed bound of tracking error, a suitable finite Fourier series expansion (FSE) is chosen as a practical reference output to be tracked. Second, by expressing the delayed practical reference output as a known time-varying vector multiplied by an unknown constant vector, we combine three kinds of uncertainties into an unknown constant vector and then estimate the vector by designing an adaptive law. By constructing a Lyapunov-Krasovskii functional, it is proved that the system output can asymptotically track the practical reference signal. An example is provided to illustrate the effectiveness of the control scheme developed in this paper.
In this paper, we propose a new state predictive model following control system (MFCS). The considered system has linear time delays. With the MFCS method, we obtain a simple input control law. The bounded property of the internal states for the control is given and the utility of this control design is guaranteed. Finally, an example is given to illustrate the effectiveness of the proposed method.
A new approach to speed control of induction motors is developed by introducing networked control systems (NCSs) into the induction motor driving system. The control strategy is to stabilize and track the rotor speed of the induction motor when the network time delay occurs in the transport medium of network data. First, a feedback linearization method is used to achieve input-output linearization and decoupling control of the induction motor driving system based on rotor flux model, and then the characteristic of network data is analyzed in terms of the inherent network time delay. A networked control model of an induction motor is established. The sufficient condition of asymptotic stability for the networked induction motor driving system is given, and the state feedback controller is obtained by solving the linear matrix inequalities (LMIs). Simulation results verify the efficiency of the proposed scheme.
Pressure ripples in electric power steering (EPS) systems can be caused by the phase lag between the driver s steering torque and steer angle, the nonlinear frictions, and the disturbances from road and sensor noise especially during high-frequency maneuvers. This paper investigates the use of the robust fuzzy control method for actively reducing pressure ripples for EPS systems. Remarkable progress on steering maneuverability is achieved. The EPS dynamics is described with an eight-order nonlinear state-space model and approximated by a Takagi-Sugeno (T-S) fuzzy model with time-varying delays and external disturbances. A stabilization approach is then presented for nonlinear time-delay systems through fuzzy state feedback controller in parallel distributed compensation (PDC) structure. The closed-loop stability conditions of EPS system with the fuzzy controller are parameterized in terms of the linear matrix inequality (LMI) problem. Simulations and experiments using the proposed robust fuzzy controller and traditional PID controller have been carried out for EPS systems. Both the simulation and experiment results show that the proposed fuzzy controller can reduce the torque ripples and allow us to have a good steering feeling and stable driving.
A closed-form solution to the linear matrix equation AX-EXF = BY with X and Y unknown and matrix F being in a companion form is proposed, and two equivalent forms of this solution are also presented. The results provide great convenience to the computation and analysis of the solutions to this class of equations, and can perform important functions in many analysis and design problems in descriptor system theory. The results proposed here are parallel to and more general than our early work about the linear matrix equation AX-XF = BY.
In the course of vehicle license plate (VLP) automatic recognition, tilt correction is a very crucial process. According to Karhunen-Loeve (K-L) transformation, the coordinates of characters in the image are arranged into a two-dimensional covariance matrix, on the basis of which the centered process is carried out. Then, the eigenvector and the rotation angle are computed in turn. The whole image is rotated by -. Thus, image horizontal tilt correction is performed. In the vertical tilt correction process, three correction methods, which are K-L transformation method, the line fitting method based on K -means clustering (LFMBKC), and the line fitting based on least squares (LFMBLS), are put forward to compute the vertical tilt angle . After shear transformation (ST) is imposed on the rotated image, the final correction image is obtained. The experimental results verify that this proposed method can be easily implemented, and can quickly and accurately get the tilt angle. It provides a new effective way for the VLP image tilt correction as well.
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.
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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
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