This paper addresses the problem of designing quantitative feedback theory (QFT) based controllers for the vibration reduction in a structure equipped with an MR damper. In this way, the controller is designed in the frequency domain and the natural frequencies of the structure can be directly accounted for in the process. Though the QFT methodology was originally conceived of for linear time invariant systems, it can be extended to nonlinear systems. A new methodology is proposed for characterizing the nonlinear hysteretic behavior of the MR damper through the uncertainty template in the Nichols chart. The resulting controller performance is evaluated in a real-time hybrid testing experiment.
In this paper, a wavelet-based approach is presented for estimation of vehicle modal parameters. The acceleration of a colliding vehicle is measured in its center of gravity — this crash pulse contains detailed information about vehicle behavior throughout a collision. Three types of signal analysis are elaborated here: time domain analysis (i.e. description of kinematics of a vehicle in time domain), the frequency analysis (identification of the parameters of the crash pulse in frequency domain), and the time-frequency analysis, which comprises those techniques that study a signal in both the time and frequency domains simultaneously, using Morlet wavelet properties. The frequency components of the recorded crash pulse are identified by determining the ridge of the wavelet coefficients matrix. Having knowledge of the natural frequency of the signal, the damping factor for a given mode shape of the signal is estimated. In this work the major frequencies of the crash pulse are determined and the damping factor for the major mode shape is identified. The comparative analysis between the current method's outcome, the response of a model established previously by using different approach and the behavior of a real car is performed and reliability of the actual methods and tools is evaluated.
This paper investigates the problem of l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> observer design for positive switched systems with time-varying delays via delta operator approach. The purpose is to design a positive observer such that the estimated error exponentially converges to zero and the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -gain from the disturbance input to the estimated error is less than a prescribed level. Based on the average dwell time approach, a sufficient condition for the existence of a positive l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> observer for the considered system is established by constructing an appropriate co-positive type Lyapunov-Krasovskii functional in delta domain. Finally, a numerical example is presented to explicitly demonstrate the effectiveness and feasibility of the proposed method.
This paper is concerned with the problem of designing a robust observer-based controller for discrete-time networked systems with limited information. An improved networked control system model is proposed and the effects of random packet dropout, time-varying delay, and quantization are considered simultaneously. Based on the obtained model, a stability criterion is developed by constructing an appropriate Lyapunov-Krasovskii functional and sufficient conditions for the existence of a dynamic quantized output feedback controller which are given in terms of linear matrix inequalities (LMIs) such that the augmented error system is stochastically stable with an performance level. An example is presented to illustrate the effectiveness of the proposed method.
Notice of Violation of IEEE Publication Principles <br><br> After careful consideration by a duly constituted committee, an author of this article, Hamid Reza Karimi, was found to have acted in violation of the IEEE Principles of Ethical Publishing by artificially inflating the number of citations to this article. <br/> This paper presents a new sliding mode control (SMC) design methodology for fuzzy singularly perturbed systems (SPSs) subject to matched/unmatched uncertainties. To fully accommodate the model characteristics of the systems, a novel integral-type fuzzy switching surface function is put forward, which contains singular perturbation matrix and state-dependent input matrix simultaneously. Its corresponding sliding mode dynamics is a transformed fuzzy SPSs such that the matched uncertainty/perturbation is completely compensated without amplifying the unmatched one. By adopting a ε-dependent Lyapunov function, sufficient conditions are presented to guarantee the asymptotic stability of sliding mode dynamics, and a simple search algorithm is provided to find the stability bound. Then, a fuzzy SMC law is synthesized to ensure the reaching condition despite matched/unmatched uncertainties. A modified adaptive fuzzy SMC law is further constructed for adapting the unknown upper bound of the matched uncertainty. The applicability and superiority of obtained fuzzy SMC methodology are verified by a controller design for an electric circuit system.
This is an Accepted Manuscript of a book chapter published by Routledge/CRC Press in Advances in Engineering Materials, Structures and Systems: Innovations, Mechanics and Applications on 21 August 2019, available online: https://www.taylorfrancis.com/books/9780429426506
1 School of Astronautics, Harbin Institute of Technology, P.O. Box 3015, Yikuang Street No. 2, Nangang District, Harbin 150001, China 2Department of Engineering, Faculty of Engineering and Science, University of Agder, 4898 Grimstad, Norway 3 Institute of Automation and Complex Systems, University of Duisburg-Essen, 47057 Duisburg, Germany 4Department of Applied Mathematics III, Universitat Politecnica de Catalunya (UPC), 08242 Manresa, Spain
This paper is concerned with adaptive neural control of nonlinear strict-feedback systems with nonlinear uncertainties, unmodeled dynamics, and dynamic disturbances. To overcome the difficulty from the unmodeled dynamics, a dynamic signal is introduced. Radical basis function (RBF) neural networks are employed to model the packaged unknown nonlinearities, and then an adaptive neural control approach is developed by using backstepping technique. The proposed controller guarantees semiglobal boundedness of all the signals in the closed-loop systems. A simulation example is given to show the effectiveness of the presented control scheme.
The robust Kalman filter design problem for two-dimensional uncertain linear discrete time-varying systems with stochastic noises is investigated in this study. First, we prove that the solution to a certain deterministic regularized least squares problem constrained by the nominal two-dimensional system model is equivalent to the generalized two-dimensional Kalman filter. Then, based on this relationship, the robust state estimation problem for two-dimensional uncertain systems with stochastic noises is interpreted as a deterministic robust regularized least squares problem subject to two-dimensional dynamic constraint. Finally, by solving the robust regularized least squares problem and using a simple approximation, a recursive robust two-dimensional Kalman filter is determined. A heat transfer process serves as an example to show the properties and efficacy of the proposed filter.
This paper deals with the development of a novel deep learning framework to achieve highly accurate rotating machinery fault diagnosis using residual wide-kernel deep convolutional auto-encoder. Unlike most existing methods, in which the input data is processed by fast Fourier transform (FFT) and wavelet transform, this paper aims to learn important features from limited raw vibration signals. Firstly, the wide-kernel convolutional layer is introduced in the convolutional auto-encoder that can ensure the model can learn effective features from the data without any signal processing. Secondly, the residual learning block is introduced in convolutional auto-encoder that can ensure the model with sufficient depth without gradient vanishing and overfitting problems. Thirdly, convolutional auto-encoder can learn constructive features without massive data. To evaluate the performance of the proposed model, Case Western Reserve University (CWRU) bearing dataset and Southeast University (SEU) gearbox dataset are used to test. The experiment results and comparisons verify the denoising and feature extraction ability of the proposed model in the case of very few training samples.
Feature combination is a powerful approach to improve object classification performance. While various combination algorithms have been proposed, average combination is almost always selected as the baseline algorithm to be compared with. In previous work we have found that it is better to use only a sample of the most powerful features in average combination than using all. In this paper, we continue this work and further show that the behaviors of features in average combination can be integrated into the k -Nearest-Neighbor (kNN) framework. Based on the kNN framework, we then propose to use a selection based average combination algorithm to obtain the best classification performance from average combination. Our experiments on four diverse datasets indicate that this selection based average combination performs evidently better than the ordinary average combination, and thus serves as a better baseline. Comparing with this new and better baseline makes the claimed superiority of newly proposed combination algorithms more convincing. Furthermore, the kNN framework is helpful in understanding the underlying mechanism of feature combination and motivating novel feature combination algorithms.
Fault detection filter (FDF) design problem for a class of discrete-time nonlinear Markovian jump systems subject to unreliable communication channel is addressed in this study. The considered system nonlinearity is assumed to satisfy global Lipschitz condition and the missing measurement phenomenon is modeled by Bernoulli binary distribution. By constructing an observer-based FDF as a residual generator, the fault detection issue is cast into a stochastic H ∞ filtering framework. Sufficient existence conditions of the H ∞-FDF are derived via matrix inequalities. Two cases for the Markovian parameters, one is completely known transition probabilities and the other is partially known transition probabilities, have been considered in the filter design procedure, respectively. Furthermore, parameter matrices of the FDF are obtained by solving a set of linear matrix inequalities. An illustrative example is addressed to show the efficacy of the proposed filter.
This paper is concerned with the L 1 fixed-order filtering problem for a class of switched linear parameter-varying (LPV) systems in which the system matrices and the time delays are dependent on the real-time measured parameters. The authors׳ attention is concentrated on designing the fixed-order filter that guarantees the filtering error system to be exponentially stable and to satisfy a prescribed L 1 disturbance attenuation level with respect to all amplitude-bounded disturbances. Based on the switching logic with the minimum average dwell time (ADT), the delay-dependent L 1 performance criterion for the switched LPV systems is first established. As there exists coupling between a Lyapunov function matrix and system parameter matrices, we utilize a slack matrix to decouple it. According to the obtained results, the admissible filter can be solved in terms of parameter linear matrix inequality (PLMI) technology. Using approximate basis function and gridding technique, the L 1 filter design can be transformed into finite number of LMIs. A numerical example is presented to verify the effectiveness of the proposed method.
In this paper, the robust control on the synchronization of a hyper-chaotic system is investigated. Based on Lyapunov stability theory and linear matrix inequality techniques, the multi-dimensional and the single-dimensional robust H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> synchronization controllers are constructed for the possible application in practical engineering. Some numerical simulations are provided to demonstrate the effectiveness of the presented controllers.
