Passivity–preserving interpolation‐based parameterized model order reduction of PEEC models based on scattered grids

The increasing operating frequencies and decreasing IC feature size call for 3‐D electromagnetic (EM) methods, such as the Partial Element Equivalent Circuit (PEEC) method, as necessary tools for the analysis and design of high‐speed systems. Very large systems of equations are often generated by 3‐D EM methods and model order reduction (MOR) techniques are commonly used to reduce such a high model complexity. A typical design process includes optimization and design space exploration, and hence requires multiple simulations for different design parameter values. Traditional MOR techniques perform model reduction only with respect to frequency and such design activities call for parameterized MOR (PMOR) methods that can reduce large systems of equations with respect to frequency and other design parameters of the circuit, such as geometrical layout or substrate characteristics. We present a novel PMOR technique applicable to the PEEC method that provides parametric reduced order models, stable and passive by construction, over a user defined design space. We treat the construction of parametric reduced order models on scattered design space grids. Pertinent numerical examples validate the proposed approach. Copyright © 2010 John Wiley & Sons, Ltd.     

Gradient‐based optimization using parametric sensitivity macromodels

A new method for gradient‐based optimization of electromagnetic systems using parametric sensitivity macromodels is presented. Parametric macromodels accurately describe the parameterized frequency behavior of electromagnetic systems and their corresponding parameterized sensitivity responses with respect to design parameters, such as layout and substrate parameters. A set of frequency‐dependent rational models is built at a set of design space points by using the vector fitting method and converted into a state‐space form. Then, this set of state‐space matrices is parameterized with a proper choice of interpolation schemes, such that parametric sensitivity macromodels can be computed. These parametric macromodels, along with the corresponding parametric sensitivity macromodels, can be used in a gradient‐based design optimization process. The importance of parameterized sensitivity information for an efficient and accurate design optimization is shown in the two numerical microwave examples. Copyright © 2012 John Wiley & Sons, Ltd.     

Robust model order reduction technique for MIMO systems via ANN‐LMI‐based state residualization

Even though model order reduction (MOR) techniques for linear dynamical systems are developed rather properly, there are still quite a lot of issues to be considered. This paper addresses a novel MOR technique for multi‐input multi‐output system with dominant eigenvalue preservation, which leads to controller cost minimization. The new technique is formulated based on an artificial neural network (ANN) prediction of an upper triangular form of the system state matrix A. Using the new system state matrix along with the linear matrix inequality (LMI) optimization method, a permutation matrix is obtained which leads to the new formulation of the complete system considered for MOR. Utilizing the non‐projection state residualization technique, a reduced model order is obtained. The proposed ANN‐LMI‐based MOR method is compared with well‐known reduction techniques such as the balanced Schur decomposition, proper orthogonal decomposition (POD), and state elimination through balanced realization. Copyright © 2010 John Wiley & Sons, Ltd.     

Tracking time‐varying‐coefficient functions

A method for adaptive and recursive estimation in a class of non‐linear autoregressive models with external input is proposed. The model class considered is conditionally parametric ARX‐models (CPARX‐models), which is conventional ARX‐models in which the parameters are replaced by smooth, but otherwise unknown, functions of a low‐dimensional input process. These coefficient functions are estimated adaptively and recursively without specifying a global parametric form, i.e. the method allows for on‐line tracking of the coefficient functions. Essentially, in its most simple form, the method is a combination of recursive least squares with exponential forgetting and local polynomial regression. It is argued, that it is appropriate to let the forgetting factor vary with the value of the external signal which is the argument of the coefficient functions. Some of the key properties of the modified method are studied by simulation. Copyright © 2000 John Wiley & Sons, Ltd.     

Nonlinear system modeling and identification using Volterra‐PARAFAC models

Discrete‐time Volterra models are widely used in various application areas. Their usefulness is mainly because of their ability to approximate to an arbitrary precision any fading memory nonlinear system and to their property of linearity with respect to parameters, the kernels coefficients. The main drawback of these models is their parametric complexity implying the need to estimate a huge number of parameters. Considering Volterra kernels of order higher than two as symmetric tensors, we use a parallel factor (PARAFAC) decomposition of the kernels to derive Volterra‐PARAFAC models that induce a substantial parametric complexity reduction. We show that these models are equivalent to a set of Wiener models in parallel. We also show that Volterra kernel expansions onto orthonormal basis functions (OBF) can be viewed as Tucker models that we shall call Volterra‐OBF‐Tucker models. Finally, we propose three adaptive algorithms for identifying Volterra‐PARAFAC models when input–output signals are complex‐valued: the extended complex Kalman filter, the complex least mean square (CLMS) algorithm and the normalized CLMS algorithm. Some simulation results illustrate the effectiveness of the proposed identification methods. Copyright © 2011 John Wiley & Sons, Ltd.     

Insulation technology in dry air and vacuum for a 72‐kV low‐pressure dry‐air insulated switchgear

A new 72‐kV rated low‐pressure dry‐air insulated switchgear applying electromagnetic actuation and a function that supports CBM has been developed. First, the dielectric characteristics in dry air under lightning impulse application were investigated for bare and insulator‐covered electrodes. The dependence of the breakdown electric field strength on the effective area was found in order to apply it to the configuration design of the insulation mold for the vacuum interrupter. In addition, the dependence of the moisture volume on the surface resistance was found in order to decide the moisture volume in the gas pressure tank. Next, a new vacuum circuit breaker (VCB) was designed. To keep the dimensions the same as in the previous 72‐kV SF₆ gas insulated switchgear, the distance between contacts in the vacuum interrupter must be shorter than in the previous switchgear. The voltage withstand capability between electrodes practically designed for the vacuum interrupter was investigated under DC voltage application, in a small capacitive current breaking simulation. The gap configuration, including contacts and slits, was optimized and the distance was shortened by 11% from the previous switchgear. As a result, the new low‐pressure dry‐air insulated switchgear was designed to be comparable in external size to the previous SF₆ gas insulated switchgear. Using dry air as an insulation medium with low pressure makes it possible to reduce the environmental burden. © 2011 Wiley Periodicals, Inc. Electr Eng Jpn, 175(1): 18–24, 2011; Published online in Wiley Online Library ( wileyonlinelibrary.com ). DOI 10.1002/eej.21058     

Data‐driven bounded‐error fault detection

In this paper, a new data‐driven fault‐detection method is proposed. This method is based on a new nonparametric system identification approach, which constitutes the principal contribution to this work. The fault‐detection method is a parametric model‐free approach that can be applied to nonlinear systems that work at various operating points. Not only can the fault‐detection process be applied to the steady state of each operating point, but it can also be applied to the transient state resulting from a change in the operating point. In order to detect faults, the proposed method uses an interval predictor based on bounded‐error techniques. The utilization of techniques based on bounded error enables system uncertainties to be included in an explicit way. This in turn leads to the possibility of obtaining interval predictions of the behaviour of the system, which include information on the reliability of the prediction itself. In order to show the effectiveness of the fault‐detection method, two examples are presented: in the form of a simulated process (counter‐flow shell‐and‐tube heat‐exchanger system) and an example of a real application (two‐tanks system). A comparison with two fault‐detection methods has also been included. Copyright © 2013 John Wiley & Sons, Ltd.     

Data‐driven tuning of linear parameter‐varying precompensators

Methods for direct data‐driven tuning of the parameters of precompensators for linear parameter‐varying (LPV) systems are developed. Since the commutativity property is not always satisfied for LPV systems, previously proposed methods for LTI systems that use this property cannot be directly adapted. When the ideal precompensator giving perfect mean tracking exists in the proposed precompensator parameterization, the LPV transfer operators do commute and an algorithm using only two experiments on the real system is proposed. It is shown that this algorithm gives consistent estimates of the ideal parameters despite the presence of stochastic disturbances. For the more general case, when the ideal precompensator does not belong to the set of parameterized precompensators, another technique is developed. This technique requires a number of experiments equal to twice the number of precompensator parameters and it is shown that the calculated parameters minimize the mean‐squared tracking error. The theoretical results are demonstrated in simulation. Copyright © 2009 John Wiley & Sons, Ltd.     

Predictor‐based iterative neural dynamic surface control for three‐phase voltage source PWM rectifier

This paper addresses the control problem of a three‐phase voltage source pulse width modulation rectifier in the presence of parametric uncertainties and external time‐varying disturbances. An adaptive controller is designed by combining a modified dynamic surface control method and a predictor‐based iterative neural network control algorithm. Especially, neural networks with iterative update laws based on prediction errors are employed to identify the lumped uncertainties. Besides, a finite‐time‐convergent differentiator, instead of a first‐order filter, is used to obtain the time derivative of the virtual control law. Using a Lyapunov–Krasovskii functional, it is proved that all signals in the closed‐loop system are ultimately uniformly bounded. Both simulation and experimental studies are provided to show the effectiveness of the proposed approach. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Finite‐model adaptive control using an LS‐like algorithm

Adaptive control problem of a class of discrete‐time nonlinear uncertain systems, of which the internal uncertainty can be characterized by a finite set of functions, is formulated and studied by using an least squares (LS)‐like algorithm to design the feedback control law. For the finite‐model adaptive control problem, this algorithm is proposed as an extension of counterpart of traditional LS algorithm. Stability in sense of pth mean for the closed‐loop system is proved under a so‐called linear growth assumption, which is shown to be necessary in general by a counter‐example constructed in this paper. The main results have been also applied to parametric cases, which demonstrate how to bridge the non‐parametric case and parametric case. Copyright © 2006 John Wiley & Sons, Ltd.     

Analytical multi‐parametric stability boundaries of DC‐DC buck converters under V1 control concept

Two main methods for controlling switching converters exist in the literature. The direct one is the voltage mode control, which suffers from some disadvantages such as slow response to load variations and an input voltage‐dependent total loop gain. The current mode control can overcome these problems but at the expense of extra cost and more complex control design. V¹ concept is a new promising control technique for designing voltage mode control of buck‐type converters with an optimal response similar to current mode control. In this paper, the dynamics and the stability of buck converters under V¹ control are studied. In particular, subharmonic oscillation limits in the parameter space are addressed. First, a closed‐loop state‐space model is derived and then used to formulate an analytical matrix‐form expression for predicting the stability limit of the system. Using this expression, multi‐parametric stability boundaries are obtained. It is shown that the equivalent series inductance of the output capacitor can narrow the stability region. It is also demonstrated that the integral action in the feedback loop of a V¹‐controlled buck converter has a negligible effect on the subharmonic oscillation boundary. The theoretical analysis is validated through numerical simulation of the circuit‐level switched model of the system. Copyright © 2017 John Wiley & Sons, Ltd.     

Composite learning fuzzy synchronization for incommensurate fractional‐order chaotic systems with time‐varying delays

This paper presents a composite learning fuzzy control to synchronize two different uncertain incommensurate fractional‐order time‐varying delayed chaotic systems with unknown external disturbances and mismatched parametric uncertainties via the Takagi‐Sugeno fuzzy method. An adaptive controller together with fractional‐order composite learning laws is designed based on both a parallel distributed compensation technology and a fractional Lyapunov criterion. The boundedness of all variables in the closed‐loop system and the Mittag‐Leffler stability of tracking error can be guaranteed. T‐S fuzzy systems are provided to tackle unknown nonlinear functions. The distinctive features of the proposed approach consist in the following: (1) a supervisory control law is designed to compensate the lumped disturbances; (2) both the prediction error and the tracking error are used to estimate the unknown fuzzy system parameters; (3) parameter convergence can be ensured by an interval excitation condition. Finally, the feasibility of the proposed control strategy is demonstrated throughout an illustrative example.     

基于FLUX的直线电机建模及仿真分析

通过FLUX软件,建立了直线电机的参数化模型,详细阐述了参数化建模的过程。采用有限元方法对直线电机电磁场进行仿真分析,得到直线电机的仿真结果,并对此结果进行了简要说明,指出了参数化建模的优点,仿真结果可以用来指导该类电机的发展研究。     

Discrete‐time periodic adaptive control for parametric systems with non‐sector nonlinearities

In this paper, a periodic adaptive control approach is proposed for a class of discrete‐time parametric systems with non‐sector nonlinearities. The proposed periodic adaptive control law is characterized by either one‐period delayed parametric updating or two‐period delayed parametric updating when input gain contains periodic unknowns. Logarithmic‐type discrete Lyapunov function is employed to handle the difficulties caused by the uncertainties that do not satisfy the linear growth condition. Some extensions to nonlinear systems with multiple unknown parameters and time‐varying input gain, tracking tasks, as well as higher‐order systems in canonical form, are also discussed. Copyright © 2013 John Wiley & Sons, Ltd.     

Robust adaptive output‐feedback control for a class of nonlinear systems with time‐varying actuator faults

A robust adaptive output‐feedback control scheme is proposed for a class of nonlinear systems with unknown time‐varying actuator faults. Additional unmodelled terms in the actuator fault model are considered. A new linearly parameterized model is proposed. The boundedness of all the closed‐loop signals is established. The desired control performance of the closed‐loop system is guaranteed by appropriately choosing the design parameters. The properties of the proposed control algorithm are demonstrated by two simulation examples. Copyright © 2010 John Wiley & Sons, Ltd.     

An adaptive robust observer for velocity estimation in an electro‐hydraulic system

This paper proposes the design of an observer to estimate the velocity of an electro‐hydraulic system by using pressure measurements only. The difficulties involved in the design of an observer for such a system include the highly nonlinear system dynamics, severe parametric uncertainties such as large variation of inertial load and unmatched model uncertainties. In order to address these issues, a nonlinear model‐based adaptive robust observer is designed to estimate the velocity. The contributions of the proposed work is twofold. First, it introduces a novel coordinate transformation to reconstruct the velocity estimate. And second, from a structural viewpoint, the design has two important features: (i) an underlying robust filter structure, which can attenuate the effect of uncertain nonlinearities such as friction and disturbances on the velocity estimation, and (ii) an adaptation mechanism to reduce the extent of parametric uncertainties. Experimental results on the swing motion control of an electro‐hydraulic robot arm demonstrate the effectiveness of the proposed observer. Copyright © 2012 John Wiley & Sons, Ltd.     

Data‐driven model reference control with asymptotically guaranteed stability

This paper presents a data‐driven controller tuning method that includes a set of constraints for ensuring closed‐loop stability. The approach requires a single experiment and can also be applied to nonminimum‐phase and unstable systems. The tuning scheme generates an estimate of the closed‐loop output error that is used to minimize an approximation of the model reference control problem. The correlation approach is used to deal with the influence of measurement noise. For linearly parameterized controllers, this leads to a convex optimization problem. A sufficient condition for closed‐loop stability is introduced, which can be included in the optimization problem for control design. As the data length tends to infinity, closed‐loop stability is guaranteed. The quality of the estimated controller is analyzed for finite data length. The effectiveness of the proposed method is demonstrated in simulation as well as experimentally on a laboratory‐scale mechanical setup. Copyright © 2010 John Wiley & Sons, Ltd.     

Survey of integrated‐circuit‐oscillator phase‐noise analysis

This tutorial distills the salient phase‐noise analysis concepts and key equations developed over the last 75 years relevant to integrated circuit oscillators. Oscillator phase and amplitude fluctuations have been studied since at least 1938 when Berstein solved the Fokker–Planck equations for the phase/amplitude distributions of a resonant oscillator. The principal contribution of this work is the organized, unified presentation of eclectic phase‐noise analysis techniques, facilitating their application to integrated circuit oscillator design. Furthermore, we demonstrate that all these methods boil down to obtaining three things: (1) noise modulation function; (2) noise transfer function; and (3) current‐controlled oscillator gain. For each method, this paper provides a short background explanation of the technique, a step‐by‐step procedure of how to apply the method to hand calculation/computer simulation, and a worked example to demonstrate how to analyze a practical oscillator circuit with that method. This survey article chiefly deals with phase‐noise analysis methods, so to restrict its scope, we limit our discussion to the following: (1) analyzing integrated circuit metal–oxide–semiconductor/bipolar junction transistor‐based LC, delay, and ring oscillator topologies; (2) considering a few oscillator harmonics in our analysis; (3) analyzing thermal/flicker intrinsic device‐noise sources rather than environmental/parametric noise/wander; (4) providing mainly qualitative amplitude‐noise discussions; and (5) omitting measurement methods/phase‐noise reduction techniques. Copyright © 2013 John Wiley & Sons, Ltd.     

Green's function approach for modeling of electrostatic effects in nanoscale fully depleted double‐gate silicon‐on‐insulator metal‐oxide‐semiconductor field‐effect transistors

Exact solution of two‐dimensional (2D) Poisson's equation for fully depleted double‐gate silicon‐on‐insulator metal‐oxide‐semiconductor field‐effect transistor is derived using three‐zone Green's function solution technique. Framework consists of consideration of source–drain junction curvature. 2D potential profile obtained forms the basis for estimation of threshold voltage. Temperature dependence of front surface potential distribution, back surface potential distribution and front‐gate threshold voltage are modeled using temperature sensitive parameters. Applying newly developed model, surface potential and threshold voltage sensitivities to gate oxide thickness have been comprehensively investigated. Device simulation is performed using ATLAS 2D (SILVACO, 4701 Patrick Henry Drive, Bldg. Santa Clara, CA 95054 USA) device simulator, and the results obtained are compared with the proposed 2D model. The model results are found to be in good agreement with the simulated data. Copyright © 2013 John Wiley & Sons, Ltd.     

An oscillatory noise‐shaped quantizer for time‐based continuous‐time sigma‐delta modulators

In this paper, a new type of an oscillatory noise‐shaped quantizer (NSQ) for time‐based continuous‐time sigma‐delta modulators is presented. The proposed NSQ is composed of an oscillatory voltage‐to‐time converter and a polyphase sampler. Using Tustin's transformation method and through the approximation of the comparator gain, a linearized model of the NSQ is introduced. This way, a novel realization of the first‐ and second‐order NSQ is presented. Its implementation is based on fully passive continuous‐time filters without needing any amplifier or power consuming element. The ploy‐phase sampler inside the NSQ is based on the combination of a time‐to‐digital and a digital‐to‐time converter. The layout of the proposed NSQ is provided in Taiwan Semiconductor Manufacturing Company 0.18 μm complementary metal‐oxide‐semiconductor 1P6M technology. The verification of the proposed NSQ is done via investigating both the system level and postlayout simulation results. Leveraging the proposed NSQ in an Lth‐order time‐based continuous‐time sigma‐delta modulator enhances the noise‐shaping order up to L + 2, confirming its superior effectiveness. This makes it possible to design high performance and wideband continuous‐time SDMs with low power consumption and relaxed design complexity.