In this work estimation of vehicle modal parameters was achieved by application of a wavelet-based method. The time-frequency analysis, which comprises those techniques that study a signal in both the time and frequency domains simultaneously, using Morlet wavelet properties are applied to the measured acceleration pulse of the colliding vehicle. Determination of the ridge of the wavelet coefficients matrix makes it possible to identify the frequency components of the recorded crash pulse. Subsequently, by using the estimated natural frequency of the system, the values of damping factor for a given mode shape are assessed. In this work there are concerned both: the major frequencies of the crash pulse and damping factor for the major mode shape.
This paper is devoted to investigating stability in mean of partial variables for coupled stochastic reaction-diffusion systems on networks (CSRDSNs). By transforming the integral of the trajectory with respect to spatial variables as the solution of the stochastic ordinary differential equations (SODE) and using Itô formula, we establish some novel stability principles for uniform stability in mean, asymptotic stability in mean, uniformly asymptotic stability in mean, and exponential stability in mean of partial variables for CSRDSNs. These stability principles have a close relation with the topology property of the network. We also provide a systematic method for constructing global Lyapunov function for these CSRDSNs by using graph theory. The new method can help to analyze the dynamics of complex networks. An example is presented to illustrate the effectiveness and efficiency of the obtained results.
This paper is devoted to studying the passivity-based sliding mode control for nonlinear systems and its application to dock cranes through an adaptive neural network approach, where the system suffers from time-varying delay, external disturbance and unknown nonlinearity. First, relying on the generalized Lagrange formula, the mathematical model for the crane system is established. Second, by virtue of an integral-type sliding surface function and the equivalent control theory, a sliding mode dynamic system can be obtained with a satisfactory dynamic property. Third, based on the RBF neural network approach, an adaptive control law is designed to ensure the finite-time existence of sliding motion in the face of unknown nonlinearity. Fourth, feasible easy-checking linear matrix inequality conditions are developed to analyze passification performance of the resulting sliding motion. Finally, a simulation study is provided to confirm the validity of the proposed method.
In order to improve the market value of the product, the platform enterprise often participates in the development process of supporting product of emerging industry’s platform innovation ecosystem. This paper puts forward a revenue sharing contract between the platform company and the supporting company by creating a collaborative development model of the supporting product in the ecosystem, and this paper studies the platform enterprise investment resource property's (complementary or substitution) impact on the supporting enterprise R&D efforts and the revenue sharing factor and analyzes collaborative development mechanism of supporting product of emerging industry platform innovation ecosystem. The research indicates that when platform enterprise and supporting enterprise's resources are complementary, the supporting enterprise R&D effort level and revenue sharing coefficient increase as the platform company’s investment increases. When platform enterprise and supporting enterprise's resources are substitutive, the supporting enterprise’s R&D effort level and revenue sharing coefficient decrease as the platform company’s investment increases.
We evaluate the spectral efficiency gains observed through multi-radio transmission diversity (MRTD), whereby packets of data are jointly scheduled for downlink transmission over multiple independent radio accesses. We specifically address downlink switched MRTD employed across macro- and pico-cellular radio accesses with non-collocated base stations in a hierarchical cell structure. It is shown that while significant gains can be achieved via MRTD among collocated macro-cell (or pico-cell) base stations, tight cooperation across non-collocated macro-and pico-cell base stations is only beneficial for a small subset of possible geometries. The impact of CQI reporting delays is also investigated
This paper investigates the problem of sampled-data (SD) exponentially synchronization for a class of Markovian neural networks with time-varying delayed signals. Based on the tunable parameter and convex combination computational method, a new approach named flexible terminal approach is proposed to reduce the conservatism of delay-dependent synchronization criteria. The SD subject to stochastic sampling period is introduced to exhibit the general phenomena of reality. Novel exponential synchronization criterion are derived by utilizing uniform Lyapunov-Krasovskii functional and suitable integral inequality. Finally, numerical examples are provided to show the usefulness and advantages of the proposed design procedure.
Artificial Neural Networks (ANNs) have strong potential in modeling nonlinear systems. This paper presents application of a feedforward neural network which utilizes back-propagation learning algorithm, in the area of modeling a vehicle to pole central collision. Kinematics of a typical mid-size vehicle impacting a rigid pole is reproduced by the means of neural networks approach. Firstly, a network is trained with the appropriate data set (acceleration, velocity, and displacement) and subsequently it is tested and simulated. We also provide a comparison concerning the efficiency and performance of each ANN created in this research. It is judged which of them generates the most satisfactory output in the shortest time.
This paper deals with modeling and adaptive output tracking of a transverse flux permanent magnet machine as a nonlinear system with unknown nonlinearities by utilizing high gain observer and radial basis function networks. The proposed model is developed based on computing the permeance between rotor and stator using quasiflux tubes. Based on this model, the techniques of feedback linearization and H ∞ control are used to design an adaptive control law for compensating the unknown nonlinear parts, such as the effect of cogging torque, as a disturbance is decreased onto the rotor angle and angular velocity tracking performances. Finally, the capability of the proposed method in tracking both the angle and the angular velocity is shown in the simulation results.
In this work, a new methodology for the structuring of multiple model estimation schemas is developed. The proposed filter is applied to the estimation and detection of active mode in dynamic systems. The discrete-time Markovian switching systems represented by several linear models, associated with a particular operating mode, are studied. Therefore, the main idea of this work is the subdivision of the models set to some subsets in order to improve the detection and estimation performances. Each subset is associated with sub-estimators based on models of the subset. In order to compute the global estimate and subset probabilities, a global estimator is proposed. Theoretical developments based on a hierarchical decision, leading to more efficiency in detection and state estimation, are proposed. Naturally, these results can be used for fault detection and isolation, using the activation probabilities of operating modes. These results are applied to detect switches in the centre of gravity for vehicle roll dynamics.Keywords: Markovian switching systemmultiple model estimationactive mode detectionvehicle roll dynamic AcknowledgementsThis work was supported by Section Innovation Norges Forskningsraad-Norwegian Research Council (in Norway) and by the Ministries of Foreign Affairs (MAE) and Higher Education and Research (MESR) within the Aurora program (Partnership PHC) (in France).Additional informationNotes on contributorsAbdelfettah HocineAbdelfettah Hocine received his master's degree (DEA) from the Institut National des Sciences Appliquées (INSA) of Lyon and his PhD degree from the Centre de Recherche en Automatique de Nancy (CRAN), France, in 2006. Since 2008, he has been working as an association professor at the Université de Khemis Miliana and as a researcher in the Laboratoire de l'Énergie et des Systemes Intelligents (LESI, Algeria). His research interests are in the areas of Markovian switching systems with applications.Mohammed ChadliMohammed Chadli is a graduate of the Ecole Normale Supérieure, Mohammedia, Morocco (1993). He received his master's degree (DEA) from the Engineering School INSA of Lyon in 1999, his PhD degree from CRAN, France, in 2002 and then completed his habilitation in 2011 at the University of Picardie Jules Verne (UPJV), Amiens, France. From 1999 to 2004, he was an associate researcher in CRAN and a lecturer at the Institut National Polytechnique de Lorraine (INPL) of Nancy. Since 2004, he has been working as an associate professor at the UPJV and as a researcher in the Modeling, Information & Systems Laboratory, Amiens, France. He was a visiting professor at the VSB TUO, Ostrava, Czech Republic (2012) and UiA, Norway (2013). His research interests include, on the theoretical side, analysis and control of singular (switched) systems, analysis and control of fuzzy/linear parameter varying polytopic models, the multiple model approach, robust control, fault detection and isolation, fault-tolerant control, and analysis and control via linear matrix inequality optimisation techniques and Lyapunov methods. On the application side, he is mainly interested in automotive control. He has authored/co-authored four books (Wiley, Hermes), book chapters and numerous articles published in international journals and conferences. Dr Chadli is a senior member of IEEE and is also serving as an editorial board member for some international journals. A structured filter for Markovian switching systemsAll authorsAbdelfettah Hocine, Mohammed Chadli & Hamid Reza Karimihttps://doi.org/10.1080/00207721.2014.909094Published online:03 June 2014Display full sizeHamid Reza KarimiHamid Reza Karimi is a professor in control systems at the Faculty of Engineering and Science of the University of Agder, Norway. His research interests are in the areas of control systems and signal processing with an emphasis on applications in engineering. Dr Karimi is a senior member of IEEE and is also serving as the chairman of the IEEE chapter on control systems at the IEEE Norway section. He is also serving as an editorial board member for some international journals such as Mechatronics, Neurocomputing, Information Sciences, Asian Journal of Control, Journal of Franklin Institute, IEEE Access, for instance. He is also a member of the IEEE Technical Committee on Systems with Uncertainty, IFAC Technical Committee on Robust Control and IFAC Technical Committee on Automotive Control. A structured filter for Markovian switching systemsAll authorsAbdelfettah Hocine, Mohammed Chadli & Hamid Reza Karimihttps://doi.org/10.1080/00207721.2014.909094Published online:03 June 2014Display full size
This paper provides a direct and practical presentation of a novel methodology for static output-feedback controller design. The proposed design strategy has been successfully applied in the fields of control systems for seismic protection of large buildings and multi-building structures, control of offshore wind turbines, and active control of vehicle suspensions. The positive results obtained in these initial applications clearly indicate that this approach could be an effective tool in a large variety of control problems, for which an LMI formulation of the statefeedback version of the problem is available. The main objective of the paper is to facilitate a brief and friendly presentation of the main ideas involved in the new design methodology. To this end, a discrete-time static output-feedback H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> controller is designed for a simplified quarter-car suspension system. Numerical simulations indicate that the proposed controller exhibits a remarkably good behavior when compared with the corresponding statefeedback H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> controller.
No abstract is provided for this article.
This paper addresses the delay-dependent stability for systems with time-varying delay. First, by taking multi-integral terms into consideration, new Lyapunov-Krasovskii functional is defined. Second, in order to reduce the computational complexity of the main results, reciprocally convex approach and some special transformations are introduced, and new delay-dependent stability criteria are proposed, which are less conservative and have less decision variables than some previous results. Finally, two well-known examples are given to illustrate the correctness and advantage of our theoretical results.
This paper intents to investigate the problem of mean-square stability analysis of Markovian jump systems with generally unknown and uncertain transition rates. Different from pervious works that the transition rates from one mode to others may be partially unknown or uncertain, in this note, the case that the transition rates from one mode to others are totally unknown will be investigated. By means of transition rate estimation, two ways are provided to tackle with the totally unknown case. In general, five cases in the transition rates matrix are studied for the mean-square stability analysis, which almost have covered all types of generally unknown and uncertain transition rates. Simultaneously, corresponding conditions for checking the mean-square stability of the considered Markovian jump systems are developed for the five studied cases. Finally, numerical examples are provided to verify the effectiveness of the proposed results.
This paper is concerned with the problem of multitarget coverage based on probabilistic detection model. Coverage configuration is an effective method to alleviate the energy-limitation problem of sensors. Firstly, considering the attenuation of node’s sensing ability, the target probabilistic coverage problem is defined and formalized, which is based on Neyman-Peason probabilistic detection model. Secondly, in order to turn off redundant sensors, a simplified judging rule is derived, which makes the probabilistic coverage judgment execute on each node locally. Thirdly, a distributed node schedule scheme is proposed for implementing the distributed algorithm. Simulation results show that this algorithm is robust to the change of network size, and when compared with the physical coverage algorithm, it can effectively minimize the number of active sensors, which guarantees all the targets<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi>γ</mml:mi></mml:mrow></mml:math>-covered.
No abstract is provided for this article.
Although the properties of direction-finding (DF) algorithms have heen investigated extensively, the fundamental effects of the array configuration on the performance of DF systems remain unknown. Furthermore, it is often overlooked that there are some theoretical lower limits on the DF performance which are imposed by the array geometry itself. In the paper eight diverse array geometries of elevated feed monopoles, which are used in a number of experimental sites in the UK, are investigated and compared using the ultimate detection, resolution and accuracy thresholds as figures of merit.
