Closed-loop Identification Problem Research Articles

Closed-loop identification for aircraft flutter model parameters

PurposeThe purpose of this paper is to extend the authors’ previous contributions on aircraft flutter model parameters identification. Because closed-loop condition is more widely used in today’s practice, a closed-loop stochastic model of the aircraft flutter test is constructed to model the aircraft flutter process, whose input–output signals are all corrupted by the observed noises. Through using a rational transfer function, the equivalent property between the aircraft flutter model parameters and polynomial coefficients is established, and then the problem of aircraft flutter model parameters identification is turned to one closed-loop identification problem. An iterative identification algorithm is proposed to identify the unknown polynomial coefficients, being benefit for the latter flutter model parameter identification. Furthermore, as the closed-loop output corresponds to the flutter amplitude, so from the point of the minimization with respect to the variance of the closed-loop output, the optimal input signal and optimal feedback controller are all derived to achieve the zero flutter, respectively, for example, the optimal input spectrum and the detailed form for optimal feedback controller.Design/methodology/approachFirst, model parameter identification for aircraft flutter is reviewed as one problem of parameter identification and this aircraft flutter model corresponds to one closed-loop stochastic model, whose input signal and output are corrupted by external noises. Second, for aircraft flutter closed-loop statistical model with statistical noise, an iterative identification algorithm is proposed to identify the unknown model parameters. Third, from the point of minimizing with respect to the variance of the closed-loop output, the optimal input signal and optimal feedback controller are all derived to achieve the zero flutter, respectively, for example, the optimal input spectrum and the detailed form for optimal feedback controller.FindingsThis aircraft flutter model corresponds to one closed-loop stochastic model, whose input signal and output are corrupted by external noises. Then, identification algorithm and optimal input signal design are studied for aircraft flutter model parameter identification with statistical noise, respectively. It means the optimal input signal and optimal feedback controller are useful for the aircraft flutter model parameter identification within the constructed new closed-loop stochastic model.Originality/valueTo the best of the authors’ knowledge, this problem of the model parameter identification for aircraft flutter is proposed by their previous work, and they proposed many identification strategies to identify these model parameters. This paper proposes a new closed-loop stochastic model to construct the aircraft flutter test, and some related topics are considered about this closed-loop identification for aircraft flutter model parameter identification in the framework of closed-loop condition.

Closed-Loop MISO Identification of Propofol Effect on Blood Pressure and Depth of Hypnosis

Feasibility of closed-loop propofol anesthesia has been demonstrated in clinical trials; however, no closed-loop system is currently routinely used in clinical practice. Data from closed-loop clinical trials provide valuable information for system evaluation prior to market approval, including outlier behavior. To exploit this valuable data, closed-loop identification in the presence of nonzero disturbances is required. Disturbances due to stimulation are not zero mean and will cause bias in models identified from clinical data. In this paper, we use multiple-input single-output (MISO) closed-loop identification to take the response to disturbances during induction of anesthesia into account. It is shown that direct MISO output-error identification introduces limited bias for this specific closed-loop identification problem and the use of more complex closed-loop identification methods does not improve model accuracy. Using this approach, we identify and validate a set of models that describes both the blood pressure and the depth-of-hypnosis response to propofol infusion for patients at risk of cardiovascular suppression. Quantification of both these responses for the same patients provides a valuable basis for the design and evaluation of constrained control systems.

Signal Selection for Estimation and Identification in Networks of Dynamic Systems: A Graphical Model Approach

Network systems have become a ubiquitous modeling tool in many areas of science where nodes in a graph represent distributed processes and edges between nodes represent a form of dynamic coupling. When a network topology is already known (or partially known), two associated goals are, to derive estimators for nodes of the network, which cannot be directly observed or are impractical to measure; and to quantitatively identify the dynamic relations between nodes. We address both problems in the challenging scenario where only some outputs of the network are being measured and the inputs are not accessible. The approach makes use of the notion of $d$ -separation for the graph associated with the network. In the considered class of networks, it is shown that the proposed technique can determine or guide the choice of optimal sparse estimators. This article also derives identification methods that are applicable to cases where loops are present providing a different perspective on the problem of closed-loop identification. The notion of $d$ -separation is a central concept in the area of probabilistic graphical models, thus an additional contribution is to create connections between control theory and machine learning techniques.

Iterative Learning Identification and Compensation of Space-Periodic Disturbance in PMLSM Systems With Time Delay

This paper aims to solve a closed-loop identification problem for the space-periodic force ripple in permanent-magnet linear synchronous motor (PMLSM) systems. Conventional identification schemes use the overall error signal to update estimates. However, the error caused by mechanical vibration and measurement noise could affect and even deteriorate the identification performance. In this paper, a novel iterative learning identification method that utilizes the partial but most pertinent information in the error signal is proposed to identify the force ripple. First, the effective error signal caused by the reference trajectory and the force ripple are extracted by projecting the overall error signal to a subspace. The subspace is spanned by some basis functions selected on the basis of the physical model of the PMLSM and the sinusoidal model of the force ripple. The time delay of the PMLSM system is compensated in these basis functions. Then, a norm-optimal approach is proposed to design the learning gain. The monotonic convergence of the iterative learning identification is further analyzed. Numerical simulation and experiments are provided to validate the proposed method and confirm its feasibility and effectiveness in force ripple identification, as well as its compensation.

Closed-loop Identification of an Industrial Extrusion Process

This paper deals with the challenging problem of closed-loop identification for multivariable chemical processes and particularly the estimation of an open-loop plant model for a lab-scale industrial twin-screw extruder used in a powder coatings manufacturing line. The aim is to produce a low order efficient model in order to assist the scaling-up and the model-based control design of the manufacturing process. To achieve this goal, a two-stage indirect approach has been deployed which relies on the a-priori knowledge of the controller parameters in order to extract good estimates of the open-loop dynamics of the underlying process. As input excitation signals we have used multiple single variable step tests at various operating conditions (current industrial practice) carried out manually in order to generate the data-set which captures the dynamics of the extrusion process. In order to increase the efforts for obtaining a suitable plant model, we have employed various identification techniques, such as Prediction Error Methods (PEM) and Subspace Identification Methods (SIM) in order to generate candidate closed-loop models that fit to the original input-output process data. Then, a comparison of the estimated models was performed by means of the mean square error and data fitting criteria in order to select the model that best describes the dynamic behaviour of the extrusion process. Model validation based on closed-loop step responses also used as verification of the results.

Errors-in-Variables identification in bilaterally coupled systems with application to oil well testing

Bilaterally coupled models are a genuine tool for modeling interconnected physical systems. It is shown that identification problems in bilaterally coupled systems can be recast into a closed-loop identification problem. When all measured signals are subject to sensor noise a closed-loop errors-in-variables problem results, for which an attractive non-parametric and instrumental-variable solutions are presented. The developed methods are applied to an example from oil reservoir engineering, being the estimation of reservoir dynamics from measurements at the well bore.

Modelling and disentangling physiological mechanisms: linear and nonlinear identification techniques for analysis of cardiovascular regulation

Cardiovascular (CV) regulation is the result of a number of very complex control interactions. As computational power increases and new methods for collecting experimental data emerge, the potential for exploring these interactions through modelling increases as does the potential for clinical application of such models. Understanding these interactions requires the application of a diverse set of modelling techniques. Several recent mathematical modelling techniques will be described in this review paper. Starting from Granger's causality, the problem of closed-loop identification is recalled. The main aspects of linear identification and of grey-box modelling tailored to CV regulation analysis are summarized as well as basic concepts and trends for nonlinear extensions. Sensitivity analysis is presented and discussed as a potent tool for model validation and refinement. The integration of methods and models is fostered for a further physiological comprehension and for the development of more potent and robust diagnostic tools.

The CONTSID toolbox for Matlab: extensions and latest developments

This paper describes the latest developments for the CONtinuous-Time System IDentification (CONTSID) toolbox to be run with Matlab which includes time-domain identification methods for estimating continuous-time models directly from sampled data. The main additions to the new version aim at extending the available methods to handle wider practical situations in order to enhance the application field of the CONTSID toolbox. The toolbox now includes routines to solve errors-in-variables and closed-loop identification problems, as well as non-linear continuous-time model identification techniques.

Identification of Heffron–Phillips model parameters for synchronous generators operating in closed loop

Heffron–Phillips model of a synchronous machine is commonly used in small signal stability analysis and for off-line design of power system stabilisers. The data used to determine the parameters of this model are either hard to measure or require the machine to be taken off-line to take the measurements which, in general, is inconvenient. Identifying these parameters from online data measurements is important since it does not require any a priori knowledge of the machine data. The problem of closed-loop identification of the Heffron–Phillips model parameters is of practical importance since the data used for identification can be gathered when the machine is normally connected to the power system. The use of open-loop identification techniques using data gathered during closed-loop operation of synchronous generators leads to bias errors in the estimated parameters. Motivated by the fact that the synchronous machine model is multivariable and is well defined in a state space structure, a closed-loop subspace parameter identification technique is proposed. Consistency of the proposed approach is illustrated using Monte Carlo analysis. Comparison of the proposed method with open-loop identification technique shows the superiority of this approach.

Closed-loop subspace-based identification algorithm using third-order cumulants

The problem of closed-loop system identification for coloured noise system without any knowledge of feedback controller is considered. We develop a solution to this problem in the framework of subspace identification based on high-order cumulants. The key of the developed algorithm is using the properties that the third-order cumulants are insensitive to any coloured Gaussian noises. By post-multiplying a suitable instrumental variable to the noise terms, the cross third-order cumulants are constructed that become zero when the noises are Gaussian distributed, and meanwhile the column rank of extended observability matrix is maintained. Thus, the standard subspace identification algorithms can be extended to closed-loop system corrupted by arbitrary coloured noises. A numerical simulation is presented to demonstrate the proposed algorithm.

Recursive Subspace Model Identification Based On Vector Autoregressive Modelling

Recursive subspace model identification (RSMI) has been developed for a decade. Most of RSMIs are only applied for open loop data. In this paper, we propose a new recursive subspace model identification which can be applied under open loop and closed loop data. The key technique of this derivation of the proposed algorithm is to bring the Vector Auto Regressive with eXogenous input (VARX) models into RSMI. Numerical studies on a closed loop identification problem show the effectiveness of the proposed algorithm.

Closed-loop identification of multivariable processes with part of the inputs controlled

In many multivariable industrial processes a subset of the available input signals is being controlled. In this paper it is analysed in which sense the resulting partial closed-loop identification problem is actually a full closed-loop problem, or whether one can benefit from the presence of non-controlled inputs to simplify the identification problem. The analysis focuses on the bias properties of the plant estimate when applying the direct method of prediction error identification, and the possibilities to identify (parts of) the plant model without the need of simultaneously estimating full-order noise models.

Integrating virtual reference feedback tuning into a unified closed-loop identification framework

This paper compares particular instances of control-relevant identification, closed-loop identification, and controller identification (either in an actual loop or in a virtual reference feedback tuning approach, VRFT) problems. Significant similarities appear between them which allow, in particular, alternative formulations of the VRFT methodology in a differentiable setting.

A New Approach to the Estimation of Cutting Dynamics Using Active Magnetic Bearings

A new experimental approach to identify the cutting dynamics in turning operations is presented. This method treats the problem as a closed-loop gray-box identification problem and is based upon spectral analysis and least squares estimation techniques. An active magnetic bearing is employed both to excite the system and to increase the tool’s damping. The inherent time delay of the cutting process is “removed” in identification by limiting the observation period to less than one revolution period of the workpiece. The coefficients of several cutting tests were identified. The methodology was validated by predicting the critical depth of cut from the identification results and comparing with the experiment result measured from a cutting test.

A CLOSED‐LOOP SUBSPACE IDENTIFICATION METHOD FOR CONTINUOUS‐TIME SYSTEMS BASED ON δ‐OPERATOR MODEL

ABSTRACTThis paper is concerned with a subspace identification of a continuous‐time plant operating in closed‐loop in the framework of the joint input‐output approach. The main procedure consists of two steps. Firstly, the dual‐Youla parametrization of the plant is used for obtaining an equivalent open‐loop problem to the original closed‐loop identification problem. Then, a δ‐operator based IV‐MOESP type subspace identification algorithm is developed to estimate the state space model for the joint input‐output process, whereby a higher‐order state space model of the plant is obtained by an algebraic operation. Subsequently, a model reduction procedure is employed to derive a lower‐order plant model removing irrelevant modes from the higher order model. Simulation results by using numerical and chemical plant models demonstrate the feasibility of the proposed method.

A parametrization for closed-loop identification of nonlinear systems based on differentially coprime kernel representations

In this paper, we use the notion of a differentially coprime kernel representation to parametrize the set of all nonlinear plants stabilized by a given nonlinear controller using a so-called Youla parameter and to unify understanding of some stability concepts for nonlinear systems. By utilizing the differential kernel representation concept, we are able to convert a closed-loop identification problem into one of open-loop identification. The main advantage of our approach using kernel representations over fractional descriptions is that we address a larger class of nonlinear systems. The idea of a differential kernel representation allows us also to clarify the relationship between three different notions of internal stability available in the literature. The results in the paper thus provide new insights to the stability of nonlinear feedback systems.

On System Identification Using the Closed-Loop Observations

The aim of the given paper is development of a joint input-output approach and its comparison with a direct one in the case of an additive correlated noise acting on the output of the system (Fig. 1), when the prediction error method is applied to solve the closed-loop identification problem by processing observations. In the case of the known regulator, the two-stage method, which belongs to the ordinary joint input-output approach, reduces to the one-stage method. In such a case, the open-loop system could be easily determined after some extended rational transfer function (25) is identified, including the transfer functions of the regulator and of the open-loop system, respectively, as additional terms. In the case of the unknown regulator, the estimate of the extended transfer function (27) is used to generate an auxiliary input. The form of an additive noise filter (36), that guarantees the minimal value of the mean square criterion (35), is determined. The results of numerical simulation and identification of the closed-loop system (Fig. 5) by computer, using the two-stage method and the direct approach are given (Figures 6-12, Table 1).

A Class of Output Inter-Sampling Approaches to Closed-Loop Identification

A class of identification algorithms for the closed-loop identification problem is discussed based on the output inter-sampling scheme. It is illustrated that the inter-sampled linear plant has a special single-input multiple-output model structure with common control input, and the inter-sampled output has cyclostationary property. Thus it can be clarified that the identifiability of the algorithm can be guaranteed by the multiple model structure in the time domain, and the cyclostationary property can be used to eliminate the correlation between the plant input and output noise in the frequency domain even though no persistently exciting reference signal is available. Some simulation and experiment results demonstrate the effectiveness of the proposed algorithms.

Modeling and identification of a strip guidance process with internal feedback

A physically interpretable model of an experimental strip guidance installation, operated at Hoogovens Research and Development in the Netherlands, is derived by means of system identification. This process consists of an offset pivot guide steering roll with five degrees of freedom, four cylindrical guide rolls, and one driving roll. In this experimental installation, an endless steel strip is created by welding the ends of the strip to allow for continuous experimentation. Five identification strategies are applied and compared for a single operating condition, determined by the strip speed and tension, using experimental data. One of these strategies is called the controller compensation in closed-loop strategy. This new technique is proposed to reduce the closed-loop identification problem into an open-loop one by using a regulator to compensate the internal feedback due to the endless strip. The physically interpretable model is obtained by identifying a linear time-invariant system for different operation conditions, and by expressing the model parameters in terms of geometric and kinematic parameters that characterize the strip guidance installation.

Internal Structure of Two-Degree-of-Freedom Controller and a Design Method for Free Parameter of Compensator

In this paper, the authors show a link between a heuristic controller used in industry and a theoretical generalized controller. First of all, we clarify the internal structure of the generalized two-degree-of-freedom controller which yields a link between the theoretical researches and the practical applications. This is particulary useful in the applications where the guidelines on the design of the controller can be obtained from the physical insight of the systems. Secondly, we indicate how to blend identification and control together without any modification of the controller. This is in fact the problem of closed-loop identification. This can be considered also as the identification part of the auto-tuning technique. Thirdly, we propose a new design technique of a free parameter taking into account a robust stability based on the information obtained from the identification. Finally, we apply the proposed algorithm to vibration suppression control, and show some simulation results to verify the effectiveness of the proposed algorithm.