Control of sub‐harmonic oscillation in peak current mode buck converter with dynamic resonant perturbation

In this paper, a dynamic resonant perturbation (DRP) method is proposed to control sub‐harmonic oscillation in a peak current mode controlled buck converter. Different from the traditional non‐feedback resonant parametric perturbation method, the proposed DRP method extracts an approximate sinusoidal control signal from the output voltage. The self‐stabilizing condition for the designed control signal is presented, and its analog circuit implementation is given as well. Furthermore, the system stability boundary is obtained by investigating the system eigenvalues, and the control parameter is effectively determined. The presented simulation and experiment results show that the proposed DRP method eliminates sub‐harmonic oscillation without sacrificing the peak value of the inductor current and features with a self‐stabilizing characteristic for external condition changes. Copyright © 2014 John Wiley & Sons, Ltd.     

Absolute stability of a class of neural networks with unbounded delay

In this paper, the existence and uniqueness of the equilibrium point and absolute stability of a class of neural networks with partially Lipschitz continue activation functions are investigated. The neural networks contain both variable and unbounded delays. Using the matrix property, the necessary and sufficient condition for the existence and uniqueness of the equilibrium point of the neural networks is obtained. By constructing proper vector Liapunov functions and non‐linear integro‐differential inequalities involving both variable delays and unbounded delay, the sufficient conditions for absolute stability (global asymptotic stability) are obtained. Copyright © 2004 John Wiley & Sons, Ltd.     

Model reference adaptive control for nonlinear switched systems under asynchronous switching

In this paper, the problem of model reference adaptive control for nonlinear switched systems with parametric uncertainties is investigated. Asynchronous switching between subsystems and adaptive controllers is also considered. Firstly, a state feedback adaptive controller is designed. Then, sufficient conditions ensuring the global practical stability of the error switched system with average dwell time are proposed. The boundedness of all signals in the closed‐loop system is guaranteed by the proposed adaptive controller. Finally, a practical example is given to demonstrate the validity of the main results. Copyright © 2016 John Wiley & Sons, Ltd.     

Effects of leakage delay on global asymptotic stability of complex‐valued neural networks with interval time‐varying delays via new complex‐valued Jensen's inequality

This paper investigates the global asymptotic stability analysis for a class of complex‐valued neural networks with leakage delay and interval time‐varying delays. Different from previous literature, some sufficient information on a complex‐valued neuron activation function and interval time‐varying delays has been considered into the record. A suitable Lyapunov‐Krasovskii functional with some delay‐dependent terms is constructed. By applying modern integral inequalities, several sufficient conditions are obtained to guarantee the global asymptotic stability of the addressed system model. All the proposed criteria are formulated in the structure of a complex‐valued linear matrix inequalities technique, which can be checked effortlessly by applying the YALMIP toolbox in MATLAB linear matrix inequality. Finally, two numerical examples with simulation results have been provided to demonstrate the efficiency of the proposed method.     

Global adaptive linear control of the permanent‐magnet synchronous motor

We contribute with a linear time‐varying controller for the permanent magnet synchronous motor. We solve the open problem of speed‐tracking control by measuring only stator currents and the rotor angular positions, under parametric uncertainty. Integral action is used to compensate for the effects of the unknown load‐torque, and adaptation is employed to estimate the unknown parameters. In the case that parameters are known (except for the load), we show that the origin of the closed‐loop system is uniformly globally exponentially stable. For the case of unknown parameters, we prove uniform global asymptotic stability; hence, we establish parametric convergence. In contrast to other adaptive control schemes for electrical machines, we use a reduced‐order adaptive controller. Indeed, adaptation is used only for the electrical dynamics equations. Moreover, not surprisingly, the closed‐loop system has a structure well‐studied in adaptive‐control literature. Performance is illustrated in a numerical setting. Copyright © 2013 John Wiley & Sons, Ltd.     

Cellular neural network with trapezoidal activation function

This paper presents a cellular neural network (CNN) scheme employing a new non‐linear activation function, called trapezoidal activation function (TAF). The new CNN structure can classify linearly non‐separable data points and realize Boolean operations (including eXclusive OR) by using only a single‐layer CNN. In order to simplify the stability analysis, a feedback matrix W is defined as a function of the feedback template A and 2D equations are converted to 1D equations. The stability conditions of CNN with TAF are investigated and a sufficient condition for the existence of a unique equilibrium and global asymptotic stability is derived. By processing several examples of synthetic images, the analytically derived stability condition is also confirmed. Copyright © 2005 John Wiley & Sons, Ltd.     

Hammerstein system identification by the Haar multiresolution approximation

The paper deals with recovering non‐linearities in the Hammerstein systems using the multiresolution approximation—a basic concept of wavelet theory. The systems are driven by random signals and are disturbed by additive, white or coloured, random noise. The a priori information about system components is non‐parametric and a delay in the dynamical part of systems is admitted. A non‐parametric identification algorithm for estimating non‐linear characteristics of static parts is proposed and investigated. The algorithm is based on the Haar multiresolution approximation. The pointwise convergence and the pointwise asymptotic rate of convergence of the algorithm are established. It is shown that neither the form nor the convergence conditions of the algorithm need any modification if the noise is not white but correlated. Also the asymptotic rate of convergence is the same for white and coloured noise. The theoretical results are confirmed by computer simulations. Copyright © 1999 John Wiley & Sons, Ltd.     

Stabilization of controlled positive discrete‐time T‐S fuzzy systems by state feedback control

This paper deals with sufficient conditions of asymptotic stability and stabilization for nonlinear discrete‐time systems represented by a Takagi–Sugeno‐type fuzzy model whose state variables take only nonnegative values at all times t for any nonnegative initial state. This class of systems is called positive systems. The conditions of stabilizability are obtained with state feedback control. This work is based on multiple Lyapunov functions. The results are presented in linear matrix inequalities form. A real plant is studied to illustrate this technique. Copyright © 2010 John Wiley & Sons, Ltd.     

Online data‐driven composite adaptive backstepping control with exact differentiators

This paper presents an online data‐driven composite adaptive backstepping control for a class of parametric strict‐feedback nonlinear systems with mismatched uncertainties, where both tracking errors and prediction errors are utilized to update parametric estimates. Hybrid exact differentiators are applied to obtain the derivatives of virtual control inputs such that the complexity problem of integrator backstepping can be avoided. Closed‐loop tracking error equations are integrated in a moving‐time window to generate prediction errors such that online recorded data can be utilized to improve parameter adaptation. Semiglobal asymptotic stability of the closed‐loop system is rigorously established by the time‐scales separation and Lyapunov synthesis. The proposed composite adaptation can not only avoid the application of identification models and linear filters resulting in a simpler control structure, but also suppress parametric uncertainties and external perturbations via the time‐interval integral. Simulation results have demonstrated that the proposed approach possesses superior control performances under both noise‐free and noisy‐measurement environments. Copyright © 2015 John Wiley & Sons, Ltd.     

Adaptive robust control of nonlinear systems with dynamic uncertainties

In this paper, the discontinuous projection‐based adaptive robust control (ARC) approach is extended to a class of nonlinear systems subjected to parametric uncertainties as well as all three types of nonlinear uncertainties—uncertainties could be state‐dependent, time‐dependent, and/or dynamic. Departing from the existing robust adaptive control approach, the proposed approach differentiates between dynamic uncertainties with and without known structural information. Specifically, adaptive robust observers are constructed to eliminate the effect of dynamic uncertainties with known structural information for an improved steady‐state output tracking performance—asymptotic output tracking is achieved when the system is subjected to parametric uncertainties and dynamic uncertainties with known structural information only. In addition, dynamic normalization signals are introduced to construct ARC laws to deal with other uncertainties including dynamic uncertainties without known structural information not only for global stability but also for a guaranteed robust performance in general. Copyright © 2008 John Wiley & Sons, Ltd.     

Indirect adaptive control of a class of marine vehicles

A nonlinear adaptive framework for bounded‐error tracking control of a class of non‐minimum phase marine vehicles is presented. The control algorithm relies on a special set of tracking errors to achieve satisfactory tracking performance while guaranteeing stable internal dynamics. First, the design of a model‐based nonlinear control law, guaranteeing asymptotic stability of the error dynamics, is presented. This control algorithm solves the tracking problem for the considered class of marine vehicles, assuming full knowledge of the system model. Then, the analysis of the zero‐dynamics is carried out, which illustrates the efficacy of the chosen set of tracking errors in stabilizing the internal dynamics. Finally, an indirect adaptive technique, relying on a partial state predictor, is used to address parametric uncertainties in the model. The resulting adaptive control algorithm guarantees Lyapunov stability of the errors and parameter estimates, as well as asymptotic convergence of the errors to zero. Numerical simulations illustrate the performance of the adaptive algorithm. Copyright © 2009 John Wiley & Sons, Ltd.     

Nonlinear Lipschitz measure and adaptive control for stability and synchronization in delayed inertial Cohen‐Grossberg–type neural networks

In this paper, without transforming the original inertial neural networks into the first‐order differential equation by some variable substitutions, time‐varying delays are introduced into inertial Cohen‐Grossberg–type networks and the existence, the uniqueness, and the asymptotic stability and synchronisation for the neural networks are investigated. Firstly, the existence of a unique equilibrium point is proved by using nonlinear Lipschitz measure method. Second, by finding a new Lyapunov‐Krasovskii functional, some sufficient conditions are derived to ensure the asymptotic stability, the asymptotic synchronization, and the asymptotic adaptive synchronization. The results of this paper are new and they complete previously known results. We illustrate the effectiveness of the approach through a few examples.     

Vibration control of flexible arm by multiple observer structure

This paper describes vibration control of a flexible arm by multiple‐observer structure using the accelerations of the arm. In general, a flexible arm has several oscillation modes. In the model of a flexible arm described as a two‐mass resonant system, the reaction torque feedback makes the flexible arm system stable. In the N‐mass resonant system, it is known that the reaction torque feedback makes all oscillation modes, only the reaction torque feedback is not enough to make the system stable. Resonance ratio control with the arm disturbance observer has been proposed. The arm disturbance observer is able to suppress the disturbance applied to the tip, but it is not sufficient for the disturbance applied to other points on the arm. The aim is to make the control system robust to arm inertia variation and disturbance. This paper proposes vibration control of flexible arm by acceleration feedback, and disturbance rejection by multiple‐observer structure using the acceleration of the arm. The validity of the proposed method is shown by simulations and experiments. © 2005 Wiley Periodicals, Inc. Electr Eng Jpn, 154(2): 68–75, 2006; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20175     

A switching periodic adaptive control approach for time‐varying parameters with unknown periodicity

Periodic variations are encountered in many real systems, which can exist in the system parameters, as a disturbance or as the tracking objective. However, there exist a great number of situations where the periodicity is not known in advance. Hence, how to compensate for the effects of time‐varying parameters with unknown periodicity remains a challenge for the controller design. In this paper, we proposed a switching periodic adaptive control approach for continuous‐time nonlinear parametric systems with periodic uncertainties in which the period and bound are not known in advance. We utilized a fully saturated periodic adaptation law to identify the unknown periodic parameters in a pointwise manner. In addition, we provided a logic‐based switching scheme to estimate the unknown period and bound online simultaneously. By virtue of Lyapunov stability analysis, we show that the asymptotic convergence can be guaranteed irrespective of the initial conditions. Finally, we carried out numerical simulations to demonstrate the efficacy of the switching periodic adaptive control algorithm. The proposed approach can be applied to parametric nonlinear systems with time‐varying parameters of unknown periodicity irrespective of the types of periodic uncertainties. Copyright © 2015 John Wiley & Sons, Ltd.     

Reachability of the sliding mode control for multimachine power systems

This paper presents conditions of reachability of a switching plane for the sliding mode control of phase shifters in multimachine power systems. Sliding mode controllers are usually synthesized so as to satisfy only the existence condition of a sliding mode. However, there is the border of an asymptotically stable region and a system state cannot reach a switching plane unless a state at the beginning of control (initial state) exists in that region. Hence, reachability is defined as an asymptotic stability of the initial state. A sufficient condition to reach onto a switching plane is given by an energy‐type Lyapunov function. It is described by control parameters that are introduced to find required control gains rather than feedback gains themselves. This allows us to straightforwardly evaluate an asymptotic stability. A phase‐shift control system is numerically tested in a 2‐machine 1‐infinite‐bus power system. The simulation results show that the improved control system offers faster transient stability and achieves the reachability of a switching plane. © 2000 Scripta Technica, Electr Eng Jpn, 131(3): 43–50, 2000     

Sampled‐data adaptive control of a class of nonlinear systems

The paper considers the discrete‐time implementation of a class of backstepping adaptive controllers for uncertain nonlinear systems in the parametric strict‐feedback form. The stability of the resultant sampled‐data system cannot be guaranteed when the direct discretization of the continuous‐time backstepping controller is applied to the same system via a sampling and hold device. Therefore, the paper has presented a sampled‐data control scheme which involves first modifying the existing continuous‐time controller and then discretizing it using the forward Euler method. It has been shown that the proposed control guarantees in a semi‐global sense the boundedness of all the variables of the overall sampled‐data system under some conditions. Particularly, the state of the nonlinear system to be controlled can converge to any arbitrarily small neighbourhood of the origin. Copyright © 2011 John Wiley & Sons, Ltd.     

Stability analysis of wideband linear bipolar varactor‐controlled oscillator with two‐coupled resonant circuits

Theoretical analysis of the stability conditions of the steady‐state operation modes and tuning bandwidth characteristics of bipolar self‐biased varactor‐controlled oscillator (VCO) with two‐coupled resonant circuits are presented. The recommendations at the choice of the circuit and varactor parameters for a linearization of the wideband tuning frequency characteristics under free‐running stable oscillation conditions are given. Highly linear octave‐band tuning operation was found to be possible using hyper‐abrupt varactors in two‐coupled resonant circuits VCO. Numerical and experimental results verify the validity of the design approach described. Copyright © 1999 John Wiley & Sons, Ltd.     

Robust observer‐based control design for uncertain singular systems with time‐delay

This paper is concerned with the stability analysis and robust dynamic output feedback controller synthesis for uncertain continuous singular systems with time‐delay. First, on the basis of the Lyapunov functional method and by resorting to the delay‐partition technique, improved delay‐dependent sufficient conditions are presented to ensure the nominal unforced system to be admissible (i.e., to be regular, impulse‐free, and stable). Second, with the help of the obtained admissibility criterion, an observer‐based controller is designed by solving a set of LMIs. Finally, the validity and applicability of the proposed approach is shown by examples. Copyright © 2013 John Wiley & Sons, Ltd.     

Adaptive TS‐FNN control for a class of uncertain multi‐time‐delay systems: The exponentially stable sliding mode‐based approach

This paper presents an adaptive Takagi–Sugeno fuzzy neural network (TS‐FNN) control for a class of multiple time‐delay uncertain nonlinear systems. First, we develop a sliding surface guaranteed to achieve exponential stability while considering mismatched uncertainty and unknown delays. This exponential stability result based on a novel Lyapunov–Krasovskii method is an improvement when compared with traditional schemes where only asymptotic stability is achieved. The stability analysis is transformed into a linear matrix inequalities problem independent of time delays. Then, a sliding mode control‐based TS‐FNN control scheme is proposed to achieve asymptotic stability for the controlled system. Since the TS‐FNN combines TS fuzzy rules and a neural network structure, fewer numbers of fuzzy rules and tuning parameters are used compared with the traditional pure TS fuzzy approach. Moreover, all the fuzzy membership functions are tuned on‐line even in the presence of input uncertainty. Finally, simulation results show the control performance of the proposed scheme. Copyright © 2008 John Wiley & Sons, Ltd.     

Adaptive robust H∞ state feedback control for linear uncertain systems with time‐varying delay

This paper considers the problem of adaptive robust H_∞ state feedback control for linear uncertain systems with time‐varying delay. The uncertainties are assumed to be time varying, unknown, but bounded. A new adaptive robust H_∞ controller is presented, whose gains are updating automatically according to the online estimates of uncertain parameters. By combining an indirect adaptive control method and a linear matrix inequality method, sufficient conditions with less conservativeness than those of the corresponding controller with fixed gains are given to guarantee robust asymptotic stability and H_∞ performance of the closed‐loop systems. A numerical example and its simulation results are given to demonstrate the effectiveness and the benefits of the proposed method. Copyright © 2008 John Wiley & Sons, Ltd.