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The cultivation of crops, conservation of plants, restoration of landscape, and management of soil are the phases incorporated in agriculture and horticulture. During the cultivation and conservati...

Our study aims at investigating the relationship between investment spikes and subsequent productivity development at the firm level. We propose a novel identification scheme for the effects of an ...

This paper presents an iterative learning identification scheme for a class of parabolic distributed parameter systems with unknown curved surfaces. The identification design method is proposed on ...

Monitoring the condition of suspension systems is significant to ensure the safe operation of modern railway vehicles. For this purpose, an online modal identification scheme, denoted as Correlatio...

In this paper, the conventional relay feedback test has been modified for modelling and identification of a class of real-time dynamical systems in terms of linear transfer function models with time-delay. An ideal relay and unknown systems are connected through a negative feedback loop to bring the sustained oscillatory output around the non-zero setpoint. Thereafter, the obtained limit cycle information is substituted in the derived mathematical equations for accurate identification of unknown plants in terms of overdamped, underdamped, critically damped second-order plus dead time and stable first-order plus dead time transfer function models. Typical examples from the literature are included for the validation of the proposed identification scheme through computer simulations. Subsequently, the comparisons between estimated model and true system are drawn through integral absolute error criterion and frequency response plots. Finally, the obtained output responses through simulations are verified experimentally on real-time liquid level control system using Yokogawa Distributed Control System CENTUM CS3000 set up.

Using a relay feedback approach, mathematical expressions for identification of real-time level control system in transfer function form are proposed in this paper. An ideal relay with hysteresis is feedback to a simple level control system for the generation of sustained oscillations. Subsequently, limit cycle information is utilised in deduced expressions for the estimation of unknown system parameters. Using describing function analysis, the gain of an ideal relay with hysteresis is approximated to yield simple mathematical expressions. Typical examples from literature are considered to show the effectiveness of the proposed identification scheme. Yokogawa distributed control system: CENTUM CS 3000 is considered as a platform for the realisation of both relay feedback and Ziegler–Nichols (ZN) method. The accuracy of derived transfer function models is compared with a model derived from ZN method and thereafter demonstrated through frequency response plots which illustrate the experimental validation of...

Certificateless identification CLI schemes offer an alternative solution to the certificate management problem of traditional identification schemes, as well as remove the key escrow from key generation, an inherent property of identity-based identification. In this paper, we provide a pairing-free CLI scheme, provable secure against passive and active/concurrent attacks for both Type-1 and Type-2 adversaries. This shows that our scheme is computationally efficient because no bilinear pairings are involved.

This paper develops methods for model identification of aggregated thermostatically controlled loads (TCLs) in smart grids, via partial differential equation (PDE) techniques. Control of aggregated TCLs provides a promising opportunity to mitigate the mismatch between power generation and demand, thus enhancing grid reliability and enabling renewable energy penetration. To this end, this paper focuses on developing parameter identification algorithms for a PDE-based model of aggregated TCLs. First, a two-state boundary-coupled hyperbolic PDE model for homogenous TCL populations is derived. This model is extended to heterogeneous populations by including a diffusive term, which provides an elegant control-oriented model. Next, a passive parameter identification scheme and a swapping-based identification scheme are derived for the PDE model structure. Simulation results demonstrate the efficacy of each method under various autonomous and non-autonomous scenarios. The proposed models can subsequently be employed to provide system critical information for power system monitoring and control.

This paper presents an identification scheme for sparse FIR systems with quantised data. We consider a general quantisation scheme, which includes the commonly deployed static quantiser as a special case. To tackle the sparsity issue, we utilise a Bayesian approach, where an l1 a priori distribution for the parameters is used as a mechanism to promote sparsity. The general framework used to solve the problem is maximum likelihood (ML). The ML problem is solved by using a generalised expectation maximisation algorithm.

In this paper, a subspace model identification method under closed-loop experimental condition is presented which can be implemented to recursively identify and update the system model. The projected matrices play an important role in this identification scheme which can be obtained by the projection of the input and output data onto the space of exogenous inputs and recursively updated through sliding window technique. The propagator type method in array signal processing is then applied to calculate the subspace spanned by the column vectors of the extended observability matrix without singular value decomposition. The speed of convergence of the proposed method is mainly dependent on the number of block Hankel matrix rows and the initialization accuracy of the projected data matrices. The proposed method is feasible for the closed-loop system contaminated with coloured noises. Two numerical examples show the effectiveness of the proposed algorithm.

We have developed an artificial-neural-network-based identification scheme for a modified version of the Preisach model for the hysteresis and relaxation observed in the variation of electrical resistance with strain in conductive polymer nanocomposites. This modeling approach models hysteresis and relaxation as two independent phenomenons. At first, the effects of the relaxation and static hysteresis have been decoupled from the total value of the resistance at different time steps. Unknown parameters in the relaxation model have been identified using a global optimization scheme. Identification of the static hysteresis has been done using an artificial neural network.

In this paper, an active fault-tolerant control scheme is proposed in the case of actuator faults. In particular, the general idea of integrating fault identification and control schemes, which takes into account the fault estimation error is first presented in a linear context. As a result, the so-called separation principle for the controller and the fault identification scheme is developed. Subsequently, the proposed approach is extended to a class of non-linear systems. Similarly to the linear case, it is proven that using a suitable control strategy and a faulty identification scheme it is possible to obtain an integrated fault-tolerant control framework, which takes into account the fault identification error. As a result, a non-linear counterpart of the above-mentioned separation principle is developed. Finally, the last part of the paper shows the application results obtained using a twin-rotor system that confirm the high performance of the proposed approach.

In this paper, three numerical algorithms for the identification of wheelrail contact forces based on measured wheel disc strains on an instrumented railway wheelset are discussed and compared. The three algorithms include one approach resting on static calibration, one that is applying a Kalman filter and the third is exploiting an inverse identification scheme. To demonstrate and evaluate the alternative methods, two load cases including periodic excitation by sinusoidal wheelrail irregularities and transient excitation by an insulated rail joint are considered. Based on a previously presented vehicletrack interaction model in the time domain, load scenarios are defined by taking the calculated vertical wheelrail contact forces as the reference force to be re-identified by the proposed algorithms. The reference contact forces are applied on a finite element model of the wheel to generate synthetic observation data, that is, radial strains at the positions of the strain gauges, serving as input to the identification procedures. It is concluded that the inverse identification scheme leads to superior accuracy at higher computational cost. If on-line implementation and evaluation is required, the Kalman filter generates better accuracy than the static calibration approach.

In this article, the decoupled property of the recently developed so-called hybrid model is used to derive time-variant analytical waveforms of the phase voltage and the field current following the load-rejection test. In the proposed identification approach, these time-variant responses are fitted to actual data using the asymptotic weighted least squares estimator along with a Newton-type optimization algorithm to compute the 4-pole 1.5-kVA, 208-V, 60-Hz laboratory synchronous generator parameters. In order to assess the robustness of the proposed identification scheme more thoroughly, the synchronous generator estimated parameters were successfully applied at the cross-validation stage using the symmetrical three-phase short-circuits test data.

This paper considers inference for impulse responses in models with highly persistent variables. We show that the impulse responses of interest are not consistently estimable under the long-run identification scheme when the strongly dependent process is parameterized as local to unity. We employ the instrumental variable framework to argue that the inconsistency and the large sampling uncertainty associated with the impulse responses arise from a weak instrument problem. Furthermore, the structure of the model is used to impose additional statistical restrictions that are combined with the economic long-run, identifying constraints to obtain an improved estimator.

It is known that the least-squares (LS) class of algorithms produce unbiased estimates providing certain assumptions are met. There are many practical problems, however, where the required assumptions are violated. Typical examples include non-linear dynamical system identification problems, where the input and output observations are affected by measurement uncertainty and possibly correlated noise. This will result in biased LS estimates and the identified model will exhibit poor generalisation properties. Model estimation for this type of error-in-variables problem is investigated in this study, and a new identification scheme based on a bootstrap algorithm is proposed to improve the model estimates for non-linear dynamical system identification.

A multistage identification scheme for structural damage detection using time domain acceleration responses is proposed. Previous studies of damage assessment using neural networks mostly involved training a backpropagation neural network (BPN) to learn damage patterns with significant computational effort. A hybrid neural network method has been proposed that uses a counterpropagation neural network (CPN) in the first stage for sorting the training data into clusters, giving an approximate guess of the damage extent quickly. After an approximate estimate is obtained, a new set of training patterns of reduced size is generated using the CPN prediction. In the second stage, a BPN trained with the Levenberg–Marquardt algorithm is used to learn the new training data and predict a more accurate result. A superior convergence and a substantial decrease in central processing unit time have been observed for three numerical examples. These examples show the computational superiority of the hybrid method compared...

It may be necessary to estimate the size of a fault if the fault develops slowly and the associated loss of performance could go unnoticed for a long period of time. This paper describes an observer-based method of estimating the magnitudes of slowly evolving faults in HVAC equipment. The sizes of two types of incipient faults in a cooling coil subsystem are estimated using a fault identification scheme based on a fuzzy relational proportional-integral observer. Because it would take a long time for a fault to develop in a real system, a detailed computer simulation is used to assess the performance of the scheme. The main causes of inaccuracy in the fault estimates are examined and long-term simulation results are presented, which demonstrate that an adaptive version of the scheme can generate satisfactory estimates of the sizes of the faults.

In this paper, a mathematical model and its parameter identification scheme are proposed for harmonic drive gears with compliance and hysteresis. The hysteresis phenomenon in harmonic drives is described by a nonlinear differential equation representing the torque-displacement relationship across the flexspline of the harmonic drive. The representation is equivalent to having the combination of nonlinear stiffness and nonlinear viscous damping. Numerical simulations along with experimental data have been used to validate the proposed modelling concept.

Evacuation of bulk power from remote energy sources is now being carried out using FACTS and HVDC links, employing thyristorized converters. The converter valves are many times subjected to abnormal conditions that force them to misbehave. Commutation failure and fire through are the most common misbehaviors. Fuzzy logic appears to be most suitable for the implementation of the natural language definitions of these misbehaviors. This paper presents a fuzzy-logic-based valve misbehavior identification scheme that uses only valve currents along with their derivatives and gate pulses. The identifier response is almost instantaneous. The information supplied by the identifier can be logged for preparing a cumulative failure index, which can be used for performance evaluation of the valves and for planning preventive maintenance. A simulation study using PSCAD/EMTDC confirms the success of the proposed identifier.

This paper describes the results of an attempt to develop a modelling and simulation environment for the closed-loop dynamics of the NASA Intelligent Flight Control System F-15 aircraft. Emphasis was placed on the modelling of fault tolerance capabilities for the aircraft fight control system. Failures of the primary control surfaces are simulated for the most common failure scenarios, which are actuator blockage with and without a missing portion of the control surface. The controller featured in the flight control system has an optimal "feedforward and feedback" architecture to provide desirable handling qualities at nominal flight conditions while retaining good performance at post-failure conditions. The parameters within the control laws are updated using an online real-time parameter identification scheme based on the Fourier Transform Regression method. The desirable performance and utility of the entire scheme are illustrated through numerical simulations in the event of stabilator failure.

We are considering non-linear system identification based on the Hammerstein model i.e. a non-linear static gain in series with linear dynamics. The static gain characteristic is any non-linear function F. An identification scheme is designed to get estimates of both the plant dynamics model and a set of N different points (x, F(x)), where N is arbitrarily chosen by the user. Such a scheme involves least squares and prediction-error algorithms as well as algebraic transformations such as singular values decomposition (SVD). Interestingly, the proposed scheme ensures persistent excitation allowing thus exact model identification in the case of no external disturbances.

In this paper, the problem of time-varying parametric system identification by wavelets is discussed. Employing wavelet operator matrix representation, we propose a new multiresolution least squares (MLS) algorithm for time-varying AR (ARX) system identification and a multiresolution least mean squares (MLMS) algorithm for the refinement of parameter estimation. These techniques can achieve the optimal tradeoff between the over-fitted solution and the poorly represented identification. The main features of time-varying model parameters are extracted in a multiresolution way, which can be used to represent the smooth trends as well as track the rapidly changing components of time-varying parameters simultaneously and adaptively. Further, a noisy time-varying AR (ARX) model can also be identified by combining the total least squares algorithm with the MLS algorithm. Based on the proposed AR (ARX) model parameter estimation algorithm, a novel identification scheme for time-varying ARMA (ARMAX) system is pres...

This paper investigates the use of genetic algorithms in the identification of linear systems with static nonlinearitites. Linear systems with static nonlinearities at the input known as the Hammerstein model, and linear systems with static nonlinearities at the output known as the Wiener model are considered in this paper. The parameters of the Hammerstein and the Wiener models are estimated using genetic algorithms from the input-output data by minimizing the error between the true model output and the identified model output. Using genetic algorithms, the Hammerstein and the Wiener models with known nonlinearity structure and unknown parameters can be identified. Moreover, systems with non-minimum phase characteristics can be identified. Extensive simulations have been used to study the convergence properties of the proposed scheme. Simulation examples are included to demonstrate the effectiveness and robustness of the proposed identification scheme.

A hierarchical crack identification scheme is proposed based on electric potential measurements. It consists of three stages for progressive estimation of the size and the location of a crack embedded in an electric current carrying body. The scheme includes two different identification procedures, applied on the different stages, which in the proposed method are internally connected as one whole operation, since each stage uses the results from the previous one. The identification procedure is organized in such a way that it can be stopped after each stage, for each stage provides information for the presence of the crack and its dimensions. The completion of the first stage gives a comparatively rough estimate of the size of the crack, classifying it as a negligible, small, medium or large one. The second stage obtains intervals to which the parameters characterizing the crack belong. In the last stage a precise enough estimate of the crack parameters is obtained by applying optimization technique. The ...

Identification schemes based on a hierarchy of properties are not well suited to geology in general and rock identification in particular. Hierarchical schemes depend on a close relationship between the classification system and readily observable features. Rocks of very different origin may have features that are very similar in appearance and vice versa. Using an assemblage of features, including both essential and non-essential ones that together allow a specimen to be identified and thus classified, is an approach that is more suited to geology. The identification scheme presented herein comprises a matrix of 24 common rocks and 13 observable features. The assemblage concept is fundamental to more advanced studies such as sedimentology and metamorphic geology, so its use and emphasis in early geological training is doubly advantageous.

A fundamental task in design and control of biotechnological processes is system modelling. This task is made difficult by the scarceness of online direct sensors for some key variables and by the fact that identifiability of models, including the Michaelis-Menten type of nonlinearities, is not straightforward. The use of adaptive estimation approaches constitutes an interesting alternative to circumvent these kinds of problems. This paper discusses an identification technique derived to solve the problem of estimating simultaneoulsy inaccessible state variables and time-varying parameters of a nonlinear wastewater treatment process. An extended linearization technique using Kronecker's calculation provides the error model of the joint observer-estimator procedure, whose convergence is proved via Lyapunov's method. Sufficient conditions for stability of this joint identification scheme are given and discussed according to the persistency excitation conditions of the signals. A simulation study with measur...

Rapid thermal processing (RTP) techniques are often applied in microelectronic technology. The main characteristics of RTP techniques lie in single wafer treatments, fast thermal kinetics at low thermal cost and reduced contamination risks; moreover, many types of reaction can be realized with one single wafer treatment. Problems of measurement and control of space-time temperature profiles have not yet been solved in a satisfactory way; in particular, classical control algorithms (such as PID control) can give bad dynamics to the system if nominal temperature profiles must be modified. Modelling of the thermal behaviour of the process leads to very complex mathematical models. These are the reasons why adaptive control techniques are chosen. A CARIMA model is an interesting input-output model of the thermal processor, whose parameters can be estimated on-line, using an identification scheme which includes supervisory actions. The adaptive control algorithm is a full-state feedback pole-placement law with an adaptive observer. A partial-state reference model was included in the control law algorithm. This type of control law can be implemented even for non-minimum-phase systems, time-varying dynamics, a wrong assumption about the time delay of the system, and when disturbances on the load exist. Experimental results are presented which show a better performance when using an adaptive control strategy

A novel method, based on a single experimental test under proportional feedback, has been developed to tune PID controllers on-line. The tuning method involving an identification scheme and a dominant pole design technique is ideal for automatic tuning. It also provides an adaptive algorithm to adjust the controller settings to achieve the desirable control performance satisfying the prescribed decay ratio and stability margin. A simulation study demonstrates that the method is valid for processes with large dead-times as well as open-loop underdamped processes.

The elementary MOESP algorithm presented in the first part of this series of papers is analysed in this paper. This is done in three different ways. First, we study the asymptotic properties of the estimated state-space model when only considering zero-mean white noise perturbations on the output sequence. It is shown that, in this case, the MOESPl implementation yields asymptotically unbiased estimates. An important constraint to this result is that the underlying system must have a finite impulse response and subsequently the size of the Hankel matrices, constructed from the input and output data at the beginning of the computations, depends on the number of non-zero Markov parameters. This analysis, however, leads to a second implementation of the elementary MOESP scheme, namely MOESP2. The latter implementation has the same asymptotic properties without the finite impulse response constraint. Secondly, we compare the MOESP2 algorithm with a classical state space model identification scheme. The latter...

The dynamic model of a manipulator system is a time‐varying highly nonlinear coupling equation set. When the moving speed increases or the payload, compared to its own weight, is no longer small, the performance of the conventional control schemes is not satisfactory for precision industrial application. Here a new adaptive control approach is developed for the manipulators to solve these problems. This algorithm directly uses a nonlinear dynamic model in the controller design to account for the nonlinear effects of the system. The least‐square time‐varying parameter identification scheme has been used to identify the change in configuration and payload. The simulation results show that this new approach has a very good trajectory tracking performance.

Moonen et al. (1989 a), presented an SVD-based identification scheme for computing state-space models for multivariable linear time-invariant systems. In the present paper, this identification procedure is reformulated making use of the quotient singular value decomposition (QSVD). Here the input-output error co-variance matrix can be taken into account explicitly, thus extending the applicability of the identification scheme to the case were the input and output data are corrupted by coloured noise. It turns out that in practice, due to the use of various pre-filtering techniques (anti-aliasing, etc.), this latter case is most often encountered. The extended identification scheme explicitly compensates for the filter characteristics and the consistency of the identification results follows from the consistency results for the QSVD. The usefulness of this generalization is demonstrated. The development is largely inspired by recent progress in total least-squares solution techniques (Van Huffel 1989) for ...

In this paper an attempt is made to combine the problems of the control and modelling of large-scale systems. Starting from the idea of the model-based (open-loop) solution of the two-layer steady-state optimization problem for the noiseless large-scale system, or one corrupted by slow-varying disturbances, an appropriate two-level identification scheme is derived and discussed. The problems connected with its numerical realization are also stated. The considerations are confined to systems with the cascade structure—typical of most production processes—and to the case when the observations of system input/output signals, from which the system model is determined, are noise-free.

This paper is concerned with the problem of identifying parameters in stochastic systems described by single-input single-output linear discrete-time equations. A stable estimation error system is developed by using the extended Kalman filter technique and the concept of strictly positive real transfer function. The identifier corresponding to the estimation error system is constructed, and the convergence of parameter estimates to the exact values is proved under some bounded conditions. It is shown that the recursive maximum likelihood identifier and the recursive extended least squares identifier are obtained by neglecting the correction terms in the proposed identifier. Numerical examples for a fourth-order system are presented to illustrate the effectiveness of the proposed method.

The main objective of the present paper is to extend some previously reported results (Kudva-Narandra 1974) to a class of stochastic system. The parameter identification scheme suggested in this paper requires two different measurements associated with an open-loop and a closed-loop systems. The stability of the closed-loop system along with the knowledge on the order of system are assumed. The stochastic version of Lyapunov's second method, along with a modified Meyer-Kalman-Yakubovich (M.K.Y.) lemma is used in deriving the stability properties of the parameter identification scheme.

An on-line scheme for identifying stable or unstable linear time-invariant processes subject to a class of deterministic and/or stochastic disturbances is proposed. The disturbances are restricted to be the outputs of some unknown linear time-invariant system. The identification scheme consists of: (a) a probing signal input which is a sum of sinusoids (or an exponentially increasing sum of sinusoids when the process is unstable or the disturbances are unbounded), (6) linear time-varying filters which have the properties of asymptotic stability, asymptotic signal tracking and asymptotic noise annihilation, and (c) a parameter update algorithm which uses the filtered input-output data. The frequency response of the process (at the probing signal frequencies) is also estimated. The proposed scheme is verified by simulation.

In Kochel 1982 a multi-location inventory model with redistribution is considered for the discounted cost criterion with full information on the demand characteristics. In the present paper the case that the distribution function of the demand depends on an unkown parameter is investigated. For this results on the discounted cost criterion are carried over to the average cost criterion. The so-called identification scheme from adaptive control theory for discrete-time Markov processes is applied and average optimality for the resulting adaptive inventory policy is shown. Simulation results show the practicability of that approach.

An on-line scheme for identifying a linear process is proposed which consists of a linear time-varying filter and a parameter update algorithm. The disturbances affecting the process, its input and its output, belong to a general class of signals which are a mixture of stochastic and deterministic signal processes generated by some linear time-invariant system excited by white noise and the Dirac delta function, respectively. The process and the disturbance signal models are not restricted to be asymptotically stable. Either a probing input signal or a normal operating input signal can be employed. The probing signal consists of a finite number of sinusoidal signals (exponentially increasing sinusoidal signals for unstable processes) of distinct frequencies. When a normal operating signal is used, an adaptive scheme is employed to tune the parameters of the filters to the distinct frequency components of the signal. The convergence of the parameter estimates to their true value is established.

A method for the on-line identification of a linear multivariable plant subject to both deterministic and stochastic disturbances is proposed. The identification scheme rests on the use of a sum of sinusoids of distinct frequencies as probing-signal inputs and on the employment of linear time-varying filters to filter the plant inputs and the plant outputs. The time-varying filters are essentially banks of narrow-band filters tuned to the probing-signal frequencies. The filtered plant inputs and the filtered plant outputs yield an estimate of the plant transfer function matrix at the probing-signal frequencies. The filtered data are further processed using a recursive least-squares algorithm and a time-domain model estimate is obtained in terms of the coefficients of the difference equation relating each input-output pair. The identification algorithm is decoupled in the sense that the estimate of the transfer function or difference equation between the ith input and jth output is unaffected by o...

The new approach of system identification through Liapunov functions is applied to stochastic systems. We demonstrate the possibility of constructing a consistent identification scheme to estimate the system parameters in the presence of noise, providing that the input is persistently exciting. A practical identification scheme is provided to obtain estimates with bounded estimation errors, where the bound can be made arbitrarily small.