1,256 publications from this institution
Complex Engineering Systems are generally represented in terms of a set of interconnected systems that their collective/global behaviors/properties are somewhat difficult to be predicted or managed. The context of complex engineering systems is mainly concerned with developing multi-component engineering systems, designs, or algorithms to exploit those unpredictable collective/global behaviors/properties. Complexity in Engineering Systems is in general manifested in component, product, system, interconnections of interacting subsystems or multidisciplinary system designs. In a broad sense, complexity is related to the expected amount of information may need to describe a dynamical system.
This paper is focused on the problem of ℋ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filtering for a class of discrete-time T-S fuzzy time-varying delay systems. Our interest is how to design full- and reduced-order filters that guarantee the filtering error system to be asymptotically stable with a prescribed ℋ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance. Sufficient conditions for the obtained filtering error system are proposed by applying an input-output approach and a two-term approximation method, which is employed to approximate the time-varying delay. The corresponding full and reduced-order filter design is cast into a convex optimization problem, which can be efficiently solved by standard numerical algorithms.
This paper proposes a finite-time fuzzy adaptive quantized control scheme for a class of triangular structural nonlinear systems. Specifically, a smooth function with intermediate control law is introduced in the modified backstepping recursive process to eliminate the effect of quantization. The output constraints issue is solved by employing a barrier Lyapunov function. Then, a command filter is applied to avoid repeatedly differentiating virtual control signals and reduce the computational complexity. Moreover, the proposed method can guarantee that the tracking error converges to a small neighborhood of the origin in the finite time. Finally, the performance and the effectiveness of the proposed method are demonstrated via simulation results.
A robust observer design is proposed for Takagi‐Sugeno fuzzy neutral models with unknown inputs. The model consists of a mixed neutral and discrete delay, and the disturbances are imposed on both state and output signals. Delay‐dependent sufficient conditions for the design of an unknown input T‐S observer with time delays are given in terms of linear matrix inequalities. Some relaxations are introduced by using intermediate variables. A numerical example is given to illustrate the effectiveness of the given results.
For locating inaccurate problem of the discrete localization criterion proposed by Demigny, a new criterion expression of “good localization” is proposed. Firstly, a discrete expression of good detection and good localization criterion of two dimension edge detection operator is employed, and then an experiment to measure optimal parameters of two dimension Canny's edge detection operator is introduced after. Moreover, a detailed performance comparison and analysis of two dimension optimal filter obtained via utilizing tensor product for one dimension optimal filter are provided which can prove that least square support vector regression (LS-SVR) is a smoothness filter and give the construct method of the derivate operator. This paper uses LS-SVR as the object function constructor and then realizes the approximation of two dimension optimal edge detection operator. This paper proposes the utility method of using singleness operator to realize multiscale edge detection by referencing the multiscale analysis technology of the wavelets theory. Experiment shows that the method has utility and efficiency.
In this brief, we study the problem of output tracking for continuous-time stochastic dynamical systems with parametric uncertainties and aperiodic disturbances by using a modified repetitive controller (MRC). More precisely, the MRC is obtained based on the equivalent input disturbance (EID) technique such that the closed-loop modified repetitive-control system is asymptotically stable in the presence of uncertainties and aperiodic disturbances. The main advantage of the proposed controller is that it can incorporate an EID estimator, which estimates and eliminates disturbances in the repetitive-control systems. Finally, simulation is conducted to reveal that the proposed controller can effectively reject the aperiodic disturbance, reduce the stochastic noise, and track the reference signal without steady-state error.
In this paper, we deal with the issue of robust delay-independent asymptotic stability and robust disturbance attenuation problem for linear parameter-dependent systems. Using Hamiltonian-Jacoby-Isaac approach, a parameter-dependent LMI optimization is obtained. It is shown that by utilizing polynomial parameter-dependent quadratic Lyapunov functions, a parameter-dependent LMI optimization problem is derived. Therefore, state feedback control is determined by solving a parameter-independent LMI. Finally, the applicability of the proposed design is illustrated on a simple example
This paper describes a new sufficient condition for the Dynamic Output Feedback (DOF) stabilisation of linear time invariant parameter-dependent systems in the presence of norm-bounded non-linear uncertainties. The required sufficient conditions with high precision are expressed as Linear Matrix Inequalities (LMIs) using the idea of Polynomial Parameter-Dependent Quadratic (PPDQ) functions. It is shown that the parameter-dependent DOF control ensures robust asymptotically stability and robust disturbance attenuation, simultaneously, based on the Hamiltonian-Jacobi-Isaac method. The applicability of the proposed method is illustrated with a simple example.
This paper intends to analyse the disturbance rejection and output tracking control for a class of nonlinear control systems with disturbances. In this connection, we present a method that combines a proportional-integral observer and nonlinear-equivalent-input-disturbance estimator for superior disturbance rejection performance. Specifically, the nonlinear-equivalent-input-disturbance estimator comprises of equivalent-input-disturbance estimator and nonlinear feedback term, which is employed to estimate and reject the disturbances from the nonlinear system. Notably, the proportional-integral loop in the proportional-integral observer reduces the estimation inaccuracy of the nonlinear-equivalent-input-disturbance. Then the estimated disturbance is intertwined into the repetitive control input to compensate it efficiently. In order to obtain the required results, the proposed control system is converted into a two-dimensional modified repetitive control system to describe the learning and control actions. In particular, the proposed controller enables to adjust the gains directly to improve the learning and control performance and as a result, the tracking accuracy increases. Using a general Lyapunov–Krasovskii functional, singular value decomposition technique and linear matrix inequalities approach, a design algorithm for establishing proportional-integral observer and feedback gains is developed for the system under consideration. Finally, simulation results are given to illustrate the developed method's validity and superiority.
Landing gear suspension systems fulfill the tasks of absorbing the vertical energy of the touch-down as well as providing passenger and crew comfort with a smooth ground ride before take-off and after landing. They are also designed to have optimal performance in the case of a hard landing. In general, the tasks of aircraft landing gears are complex and sometimes lead to a number of contradictory requirements. Although there are existing modifications of aircraft shock absorbers to reduce the problem, the basic design conflict between the requirements for landing and for rolling cannot be fully overcome by a passive suspension layout. Active and semiactive suspension techniques are a solution to this problem and are capable of reducing fuselage vibrations effectively. In order to get satisfactory damping performance with active and semiactive devices, appropriate control laws must be employed. In this paper, we study the use of an adaptive backstepping control with H∞ performance to cope with disturbances, uncertainties and nonlinearities, typical of suspension systems and damping devices. A comparison between active and semiactive strategies is provided through the analysis of simulation results.
Existing results on the generalized dissipativity of digital filters are limited to 1-D cases. This brief presents some novel results on the generalized dissipativity of 2-D filters in the discrete Fornasini–Marchesini second (FMS) form. More specifically, we propose a criterion to ascertain that single 2-D filters in the discrete FMS form are 2-D generalized dissipative. Using this result, a condition is established such that the interconnected 2-D filters in the discrete FMS form are 2-D generalized dissipative. Furthermore, the asymptotic stability of unforced interconnected 2-D filters is examined. It is shown that the results developed in our recent work for 1-D filters and in this brief for 2-D filters serve as an overall framework for the generalized dissipativity of digital filters.
We present an artificial neural network model to predict hourly A-weighted equivalent sound pressure levels (LA eq,1h) for roads in Tehran at distances less than 4 m from the nearside carriageway edge. Our model uses the UK Calculation of Road Traffic Noise (CORTN) approach. Data were obtained from 50 sampling locations near five roads in Tehran at nearside carriageway edge distances of less than 4 m. The data were randomly assigned to training, testing, and holdout subsets. Model training was carried out using the training and testing subsets and comprised 60% and 20% of the data, respectively. Model validation was performed using the remaining 20% of data as a holdout subset. We examine the overall model efficiency using non-parametric tests, such as the Wilcoxon matched-pairs signed-rank test for the training step and the Kolmogorov–Smirnov test for two independent samples for the validation step. Our results indicate that a neural network approach can be applied for traffic noise prediction in Tehran in a statistically sound manner. The Wilcoxon matched-pairs signed-ranks test detects no significant difference between the absolute testing set errors of the developed neural network and a calibrated version of the CORTN model.
A robust H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filtering algorithm is proposed for two-dimensional (2-D) linear uncertain time-varying systems in this study. The considered 2-D systems are subject to unknown inputs, measurement noises, and time-varying modeling uncertainties. To appropriately deal with the system uncertainties, a new problem formulation of robust H∞ filter design for 2-D uncertain systems is first presented by introducing an alternative indefinite quadratic performance function in lieu of the standard H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance index. Then, the robust 2-D H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filter design is converted to an optimization problem of finding the positive minimum of the new indefinite quadratic performance function under certain conditions. By relating this quadratic form to a specific 2-D state-space model in Krein space, the Krein space estimation theory is used to solve the reformulated optimization problem. A recursive robust 2-D time-varying H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filter and its explicit condition for the existence are obtained through projection techniques in 2-D Krein space. One thermal process plant is used to illustrate the effectiveness of the proposed filter.
Because of the fact that vehicle crash tests are complex and complicated experiments it is advisable to establish their mathematical models. This paper contains an overview of the kinematic and dynamic relationships of a vehicle in a collision. There is also presented basic mathematical model representing a collision together with its analysis. The main part of this paper is devoted to methods of establishing parameters of the vehicle crash model and to real crash data investigation i.e. - creation of a Kelvin model for a real experiment, its analysis and validation. After model's parameters extraction a quick assessment of an occupant crash severity is done.
The humidity sensitive characteristics of the sensor fabricated from 10 mol% La 2 O 3 doped CeO 2 nanopowders with particle size 17.26 nm synthesized via hydrothermal method were investigated at different frequencies. It was found that the sensor shows high humidity sensitivity, rapid response-recovery characteristics, and narrow hysteresis loop at 100 Hz in the relative humidity range from 11% to 95%. The impedance of the sensor decreases by about five orders of magnitude as relative humidity increases. The maximum humidity hysteresis is about 6% RH, and the response and recovery time is 12 and 13 s, respectively. These results indicate that the nanosized La 2 O 3 doped CeO 2 powder has potential application as high-performance humidity sensor.
