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Quality prediction models are constructed based on multivariate statistical methods, including ordinary least squares regression (OLSR), principal component regression (PCR), partial least squares regression (PLSR), and modified partial least squares regression (MPLSR). The prediction model constructed by MPLSR achieves superior results, compared with the other three methods from both aspects of fitting efficiency and prediction ability. Based on it, further research is dedicated to selecting key variables to directly predict the product quality with satisfactory performance. The prediction models presented are more efficient than tradition ones and can be useful to support human experts in the evaluation and classification of the product quality. The effectiveness of the quality prediction models is finally illustrated and verified based on the practical data set of the red wine.

1 School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA 5005, Australia 2 Department of Engineering, Faculty of Technology and Science, University of Agder, 4898 Grimstad, Norway 3 College of Automation, Chongqing University, Chongqing 400044, China 4 School of Control Science and Engineering, Shandong University, Jinan 250061, China 5 College of Automation, Harbin Engineering University, Harbin 150001, China

Published version of av article from the journal: Nonlinear dynamics and systems theory. Also available fro the publisher:http://www.e-ndst.kiev.ua/v10n1.htm

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.

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.

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.

An optimal input design method for parameter estimation in a discrete‐time nonlinear system is presented in the paper to improve the observability and identification precision of model parameters. Determinant of the information matrix is used as the criterion function which is generally a nonconvex function about the input signals to be designed. To avoid the locally optimizing problem, a randomized design method is proposed by which a globally optimizing test plan other than input signals may be obtained. Then the randomized design can be approximated by a nonrandomized design about optimal inputs. An iterative algorithm integrated with dynamic programming is given and verified by a numerical example on experimental design for self‐calibration tests of ISP system.

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.

The UK and European framework for access to the UHF TV band by white space devices (WSDs) is predicated on the availability of instructions from white space databases (WSDBs) which specify location-specific maximum permitted WSD radiated power levels. These levels are calculated in such a way so as to afford protection to the digital terrestrial TV (DTT) service in terms of a maximum permitted degradation in DTT location probability. The contribution of this paper is two-fold. Firstly, we present an approach for the calculation of DTT location probability which improves considerably on the accuracy of the technique commonly used for this purpose. Secondly, we present a novel methodology for the calculation of the maximum permitted WSD EIRP, subject to a target degradation in location probability. We show that, despite its low computational efficiency, the methodology generates results which compare favorably with those generated via brute-force Monte Carlo simulations. Both presented techniques are suitable for implementation in TV white space databases.

An actuator and sensor allocation approach is proposed for the design of coupled-adjacent-building vibration suppression under seismic excitation. This paper first establishes a full-order model of adjacent buildings with the location information of actuators and sensors. Then, the order of the model is reduced via modal cost analysis, by retaining the modes contributing the most. In view of the fact that the output powers of the actuators are limited, this paper brings forward a mixed <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$H_{\infty}/GH_{2}$</tex></formula> control. By considering that not all the states of the system can be measured by the sensors, a dynamic output feedback controller is designed. The genetic algorithm is employed to obtain the locations of the actuators and sensors, as well as the corresponding controller. With the proposed approach, the allocation problem is solved, and the vibration of coupled adjacent buildings is attenuated at a sufficiently low level with constrained acting forces. Simulations demonstrate the effectiveness and robustness of the proposed approach in attenuating building vibration under earthquake excitation.

This paper presents a novel adaptive variable structure (AVS) method to design a fuzzy neural network (FNN). This AVS-FNN is based on radial basis function (RBF) neurons, which have center and width vectors. The network performs sequential learning through sliding data window reflecting system dynamic changes, and dynamic growing-and-pruning structure of FNN. The salient characteristics of the AVS-FNN are as follows: (1) Structure-learning and parameters estimation are performed automatically and simultaneously without partitioning input space and selecting initial parameters a priori. The structure-learning approach relies on the contribution of the size of the output. (2) A set of fuzzy rules can be inserted or reduced during the learning process. (3) The connection weighting factors between the deduction layer and output layer generated quickly without resorting to iteration learning are updated by the least-squares algorithm. The proposed method effectively generates a fuzzy neural model with a highly accurate and compact structure. Simulation results demonstrate that the proposed AVS-FNN has a self-organizing ability, which can determine the structure and parameters of the FNN automatically. The application of this new approach has been applied successfully in the 3 DOF helicopter systems, showing the effectiveness and potential of the proposed design techniques.

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