Dynamic model development and control for multiple‐output flyback converters in DCM and CCM

Multiple‐output flyback converters are widely used in switching power supplies due to their low component count and cost‐effective structure. The main problem of this structure is how to balance output voltages in different load conditions. This paper proposes a new approach for single‐input multiple‐output flyback converters operating in DCM and CCM by a small‐signal averaged model. The averaged model is derived by presenting the piecewise‐linear waveform for the inductor currents inside the converter. In DCM, the magnetizing current and currents through the output windings reach zero when the switch is turned off. In CCM, the magnetizing current of the converter is continuous over a switching interval and this possibility exists that only some of the output diodes completely conduct when the switch is off. The proposed model of the converter can be used in a wide range of operations within identical and non‐identical loading conditions. Using a laboratory prototype, several case studies and input‐to‐output transfer functions are considered to verify the proposed model. The controller design is performed for the both CCM and DCM, and then dynamic characteristics of the overall system are evaluated.     

Adaptive unknown input observer approach for aircraft actuator fault detection and isolation

In this paper, an adaptive unknown input observer (UIO) approach is developed to detect and isolate aircraft actuator faults. In a multiple-model scheme, a bank of parallel observers are constructed, each of which is based on a model that describes the system in the presence of a particular actuator fault. The observers are constructed based on a modified form of the standard UIO to generate fault-dependant residual signals, such that when a model matches the system, the residual signal will be zero. Otherwise, the residual will be definitely non-zero and governed uniquely by the faulty signal. For locked actuators and loss of actuator effectiveness, in which the locked position and the reduced effectiveness are additional unknowns, we develop an adaptive scheme to estimate these unknown parameters. To the best of our knowledge, this is the first adaptive UIO presented. We prove that the proposed adaptive algorithms guarantee that both the residual signals and the estimation errors of the unknown parameters converge exponentially when a model matches the plant. By further designing a model-matching index, the fault can be isolated accurately. A condition for the approach is that for an nth order system, there must be n independent measurements available. This requirement limits the applicability of our proposed approach. The condition is certainly satisfied by all state-feedback control systems. However, for some other systems, extra efforts may be needed to increase the number of measurements. The method is applied to a linear model of the F-16 aircraft with controller. The results show that the approach is effective. Copyright © 2006 John Wiley & Sons, Ltd.     

Self‐tuning control for polynomial systems using a receding horizon observer and control strategy

A receding horizon observer and control scheme is introduced for non‐linear systems described by polynomial maps. This control scheme has a natural interpretation as a two‐stage adaptive or self‐tuning control algorithm. The non‐linear feedback that results is defined only on the basis of past input and output measurements. The computational complexity aspects of this approach to adaptive or self‐tuning control are briefly discussed. A linear system and a Hénon map example are used to illustrate the ideas. Copyright © 2004 John Wiley & Sons, Ltd.     

Sensor classification for the fault detection and isolation,a structural approach

This paper addresses the sensor classification problem for fault detection and isolation (FDI) with observability requirement in a structural way. The system under consideration is a linear system subject to additive faults and affected by unknown input disturbances. This system is equipped with a sensor network subject to sensor failures. We represent the dynamics of the system by a linear parameterized state space model called linear structured model. The underlying prior knowledge on the system is reduced to the existence or non‐existence of relations between variables in the model. A dedicated residual set using a bank of observers is designed in order to detect and isolate the faults. The failure of some sensors may affect the observability of the system which is a natural requirement in order to build stable observers and also may affect the FDI solvability. The main contribution of this paper is to classify the sensors with respect to their importance in case of failure relatively to the considered FDI problem. More precisely, we characterize the sensors that are essential i.e. whose failure leads to FDI solvability loss and those which are useless for such property. We also quantify the relative importance of the sensors which are not useless. The proposed graph approach is visual, easy to handle and close to the physical structure of the system. It is well adapted to large‐scale systems and essentially leads to polynomial algorithms here. Copyright © 2010 John Wiley & Sons, Ltd.     

Leak localization in drinking water distribution networks using structured residuals

In this paper, a new model‐based approach to leakage localization in drinking water distribution networks is proposed. In particular, a method for generating a set of structured residuals for water distribution networks systems is presented. Non‐linear static equations are typically used in the modeling of such systems. Unfortunately, these non‐linearities prevent explicit solutions and thus the subsequent analytical expressions of the residuals are not feasible. For this reason, the residual computation procedure presented here uses a numerical solver based on an enhanced Newton–Raphson algorithm. Moreover, because the water distribution networks usually present a high degree of interconnection between nodes, good leak isolability properties can easily be obtained by applying the proposed residual generation method. Nevertheless, not fully leak detection and isolation can always be achieved, thus a methodology for the analysis of leak diagnosis is also presented in this paper. To show the applicability and the potential of the proposed approach, the structured residual generators are tested on a real water network model and compared with another existing method based on directional residuals. Finally, the results of the two methods are combined in order to obtain the best of both methods. Copyright © 2014 John Wiley & Sons, Ltd.     

Direct adaptive control for non‐linear uncertain systems with exogenous disturbances

A direct adaptive non‐linear control framework for multivariable non‐linear uncertain systems with exogenous bounded disturbances is developed. The adaptive non‐linear controller addresses adaptive stabilization, disturbance rejection and adaptive tracking. The proposed framework is Lyapunov‐based and guarantees partial asymptotic stability of the closed‐loop system; that is, asymptotic stability with respect to part of the closed‐loop system states associated with the plant. In the case of bounded energy L₂ disturbances the proposed approach guarantees a non‐expansivity constraint on the closed‐loop input–output map. Finally, several illustrative numerical examples are provided to demonstrate the efficacy of the proposed approach. Copyright © 2002 John Wiley & Sons, Ltd.     

Fault accommodation for multi‐input linear sampled‐data systems

A novel observer‐based fault accommodation technique for linear multi‐input multi‐output sampled‐data systems affected by a general class of actuator faults in the presence of quantization errors is addressed in the paper. Only the output signal has been assumed to be available for direct measurement. A simulation study on a three‐tank system supports theoretical developments. Copyright © 2014 John Wiley & Sons, Ltd.     

Watermarking on CNN‐UM for image and video authentication

In this paper a new approach to fragile watermarking technique is introduced. This problem is particularly interesting in the field of modern multimedia applications, when image and video authentication are required. The approach exploits the cellular automata suitability to work as pseudorandom pattern generators and extends the related algorithms under the framework of the cellular non‐linear networks (CNNs). The result is a novel way to perform watermarking generation in real time, using the presently available CNN‐universal chip prototypes. In this paper, both the CNN algorithms for fragile watermarking as well as on‐chip experimental results are reported, confirming the suitability of CNNs to successfully act as real‐time watermarking generators. The availability of CNN‐based visual microprocessors allows to have powerful algorithms to watermark in real time images or videos for efficient smart camera applications. Copyright © 2004 John Wiley & Sons, Ltd.     

Adaptive output feedback actuator failure compensation for a class of non‐linear systems

An adaptive compensation control scheme using output feedback is designed and analysed for a class of non‐linear systems with state‐dependent non‐linearities in the presence of unknown actuator failures. For a linearly parameterized model of actuator failures with unknown failure values, time instants and pattern, a robust backstepping‐based adaptive non‐linear controller is employed to handle the system failure, parameter and dynamics uncertainties. Robust adaptive parameter update laws are derived to ensure closed‐loop signal boundedness and small tracking errors, in general, and asymptotic regulation, in particular. An application to controlling the angle of attack of a non‐linear hypersonic aircraft dynamic model in the presence of elevator segment failures is studied and simulation results show that the developed adaptive control scheme has desired actuator failure compensation performance. Copyright © 2004 John Wiley & Sons, Ltd.     

Adaptive fault tolerant control for a class of multi‐input multi‐output nonlinear systems with both sensor and actuator faults

Compared with the fault diagnosis, detection, and isolation literature, very few results are available to discuss control algorithms directly for multi‐input multi‐output nonlinear systems with both sensor and actuator faults in the fault tolerant control literature. In this work, we present a fault tolerant control algorithm to address the system output stabilization problem for a class of multi‐input multi‐output nonlinear systems with both parametric and nonparametric uncertainties, subject to sensor and actuator faults that can be both multiplicative and additive. All elements of the sensor measurements and actuator components can be faulty. Besides, the control input gain function is not fully known. Backstepping method is used in the analysis and control design. We show that under the proposed control scheme, uniformly ultimate boundedness of the system output is guaranteed, while all closed‐loop system signals stay bounded. In the cases where the sensor faults are only multiplicative, exponential convergence of the system state variables into small neighbourhoods around zero is guaranteed. An illustrative example on a robot manipulator model is presented in the end to further demonstrate the effectiveness of the proposed control scheme.     

On identification methods for direct data‐driven controller tuning

In non‐iterative data‐driven controller tuning, a set of measured input/output data of the plant is used directly to identify the optimal controller that minimizes some control criterion. This approach allows the design of fixed‐order controllers, but leads to an identification problem where the input is affected by noise, and not the output as in standard identification problems. Several solutions that deal with the effect of measurement noise in this specific identification problem have been proposed in the literature. The consistency and statistical efficiency of these methods are discussed in this paper and the performance of the different methods is compared. The conclusions offer a guideline on how to solve the data‐driven controller tuning problem efficiently. Copyright © 2010 John Wiley & Sons, Ltd.     

Neural network adaptive robust control with application to precision motion control of linear motors

In this paper, neural networks (NNs) and adaptive robust control (ARC) design philosophy are integrated to design performance‐oriented control laws for a class of single‐input–single‐output (SISO) nth‐order non‐ linear systems. Both repeatable (or state dependent) unknown non‐linearities and non‐repeatable unknown non‐linearities such as external disturbances are considered. In addition, unknown non‐linearities can exist in the control input channel as well. All unknown but repeatable non‐linear functions are approximated by outputs of multi‐layer neural networks to achieve a better model compensation for an improved performance. All NN weights are tuned on‐line with no prior training needed. In order to avoid the possible divergence of the on‐line tuning of neural network, discontinuous projection method with fictitious bounds is used in the NN weight adjusting laws to make sure that all NN weights are tuned within a prescribed range. By doing so, even in the presence of approximation error and non‐repeatable non‐linearities such as disturbances, a controlled learning is achieved and the possible destabilizing effect of on‐line tuning of NN weights is avoided. Certain robust control terms are constructed to attenuate various model uncertainties effectively for a guaranteed output tracking transient performance and a guaranteed final tracking accuracy in general. In addition, if the unknown repeatable model uncertainties are in the functional range of the neural networks and the ideal weights fall within the prescribed range, asymptotic output tracking is also achieved to retain the perfect learning capability of neural networks in the ideal situation. The proposed neural network adaptive control (NNARC) strategy is then applied to the precision motion control of a linear motor drive system to help to realize the high‐performance potential of such a drive technology. NN is employed to compensate for the effects of the lumped unknown non‐linearities due to the position dependent friction and electro‐magnetic ripple forces. Comparative experiments verify the high‐performance nature of the proposed NNARC. With an encoder resolution of 1 µm, for a low‐speed back‐and‐forth movement, the position tracking error is kept within ±2 µm during the most execution time while the maximum tracking error during the entire run is kept within ±5.6 µm. Copyright © 2001 John Wiley & Sons, Ltd.     

Eigenvalue estimation of dominant electromechanical modes based on synchronous measurement and generator grouping technique

The authors have been studying a new approach for modal analysis of large power systems that utilizes GPS‐based synchronous measurement technology. The approach is based on the identification of a linearized multi‐input multi‐output model of power system. Since the identified model expresses approximately the electromechanical dynamics of an actual power system, modal frequencies, dampings, and mode shapes corresponding to electromechanical modes can be estimated as eigenvalues and eigenvectors of the identified model. In the paper, in order to advance our approach to a practical technique, it is mainly discussed how to select a small number of machines suitable for measurement locations to estimate eigenvalues associated with dominant slow modes. Such machines can be detected by identifying coherent groups related to the slow modes. The reference generators that behave representatively in each coherent group are the optimal ones to be measured. Therefore, the slow modes can be obtained by observing one generator from each group. The verification of the new modal analysis and coherency‐based machine selection is done through simulation studies using the IEEJ EAST 10‐machine system model. © 2008 Wiley Periodicals, Inc. Electr Eng Jpn, 164(4): 24–32, 2008; Published online in Wiley InterScience ( www.interscience. wiley.com ). DOI 10.1002/eej.20428     

On parameter estimation for excitation control of synchronous generators

This paper deals with the problem of identification of the network parameters and the desired equilibrium in applications of excitation control for synchronous generators. Our main contribution is the construction of a new non‐linear identifier that provides asymptotically consistent estimates (with guaranteed transient bounds) of the line impedance and the equilibrium for the classical three‐dimensional flux‐decay model of a single generator connected to an infinite bus. This model is non‐linear, and non‐linearly parameterized, and the equilibria depend also non‐linearly on the unknown parameters. The proposed estimator can be used, adopting a certainty equivalent approach, to make adaptive any power system stabilizer that relies on the knowledge of these parameters. The behaviour of the scheme is illustrated in two simulated case studies with the interconnection and damping assignment passivity‐based controller recently proposed by the authors. Copyright © 2004 John Wiley & Sons, Ltd.     

Network‐based fault detection for discrete‐time state‐delay systems: A new measurement model

In this paper, the fault detection problem is studied for a class of discrete‐time networked systems with multiple state delays and unknown input. A new measurement model is proposed to account for both the random measurement delays and the stochastic data missing (package dropout) phenomenon, which are typically resulted from the limited capacity of the communication networks. At any time point, one of the following cases (random events) occurs: measurement missing case, no time‐delay case, one‐step delay case, two‐step delay case, …, q‐step delay case. The probabilistic switching between different cases is assumed to obey a homogeneous Markovian chain. We aim to design a fault detection filter such that, for all unknown input and incomplete measurements, the error between the residual and weighted faults is made as small as possible. The addressed fault detection problem is first converted into an auxiliary H_∞ filtering problem for a certain Markovian jumping system (MJS). Then, with the help of the bounded real lemma of MJSs, a sufficient condition for the existence of the desired fault detection filter is established in terms of a set of linear matrix inequalities (LMIs). A simulation example is provided to illustrate the effectiveness and applicability of the proposed techniques. Copyright © 2007 John Wiley & Sons, Ltd.     

Filter‐based fault detection and diagnosis using output PDFs for stochastic systems with time delays

In this paper, a fault detection and diagnosis (FDD) scheme is studied for general stochastic dynamic systems subjected to state time delays. Different from the formulation of classical FDD problems, it is supposed that the measured information for the FDD is the probability density function (PDF) of the system output rather than its actual value. A B‐spline expansion technique is applied so that the output PDF can be formulated in terms of the dynamic weights of the B‐spline expansion, by which a time delay model can be established between the input and the weights with non‐linearities and modelling errors. As a result, the concerned FDD problem can be transformed into a classic FDD problem subject to an uncertain non‐linear system with time delays. Feasible criteria to detect the system fault are obtained and a fault diagnosis method is further presented to estimate the fault. Simple simulations are given to demonstrate the efficiency of the proposed approach. Copyright © 2006 John Wiley & Sons, Ltd.     

On the transposition of Gm–C filters to DC stabilized log‐domain filters

High‐order log‐domain filters could be designed by transposing the already known linear‐domain G_m–C filter topologies to the corresponding topologies in the log‐domain. This is achieved by using a non‐linear transconductor configuration, where the output current is exponentially related to its input and output voltages. A drawback of the non‐linear transconductor configuration already introduced in the literature is that a number of the transposed log‐domain filter topologies suffer from DC instability, while in some others a DC offset current appears at their output. In order to eliminate the aforementioned problems a modified non‐linear transconductor configuration for transposing G_m–C filter topologies to log‐domain filter topologies is introduced in this paper. The achieved improvements are demonstrated through a number of log‐domain filter configurations derived using the already introduced and the proposed transposition schemes. Copyright © 2006 John Wiley & Sons, Ltd.     

Bias‐eliminating least‐squares identification of errors‐in‐variables models with mutually correlated noises

This paper proposes a bias‐eliminating least‐squares (BELS) approach for identifying linear dynamic errors‐in‐variables (EIV) models whose input and output are corrupted by additive white noise. The method is based on an iterative procedure involving, at each step, the estimation of both the system parameters and the noise variances. The proposed identification algorithm differs from previous BELS algorithms in two aspects. First, the input and output noises are allowed to be mutually correlated, and second, the estimation of the noise covariances is obtained by exploiting the statistical properties of the equation error of the EIV model. Copyright © 2012 John Wiley & Sons, Ltd.     

Fault diagnosis of a simulated industrial gas turbine via identification approach

In this paper, a model‐based procedure exploiting the analytical redundancy principle for the detection and isolation of faults on a simulated process is presented. The main point of the work consists of using an identification scheme in connection with dynamic observer and Kalman filter designs for diagnostic purpose. The errors‐in‐variables identification technique and output estimation approach for residual generation are in particular advantageous in terms of solution complexity and performance achievement. The proposed tools are analysed and tested on a single‐shaft industrial gas turbine MATLAB/SIMULINK^® simulator in the presence of disturbances, i.e. measurement errors and modelling mismatch. Selected performance criteria are used together with Monte‐Carlo simulations for robustness and performance evaluation. The suggested technique can constitute the design methodology realising a reliable approach for real application of industrial process FDI. Copyright © 2006 John Wiley & Sons, Ltd.     

Multi‐input multi‐output model‐free predictive control and its application to wastewater treatment

Model‐free predictive control is a data‐driven control method that directly computes the control input from massive input/output datasets. It does not require the mathematical models that are used in conventional model predictive control. It has recently been shown that the control offered by model‐free predictive control can be improved by the introduction of polynomial regression vectors containing the control input and measurement output. In this paper, we extend these findings to multi‐input multi‐output nonlinear systems and investigate the effectiveness of the approach through numerical simulations of a wastewater treatment process. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.