System Identification Model Research Articles

System Identification Modeling of a Smoking Cessation Intervention

This paper examines the use of system identification to describe time-varying phenomena in a smoking cessation intervention. The analysis is facilitated by the availability of intensive longitudinal data that enables the application of system identification techniques. Two model structures are considered; one involves the concept of statistical mediation, while the other describes a feedback mechanism. In fitting these models to intensive longitudinal data from a University of Wisconsin clinical trial that studied bupropion and counseling as smoking cessation aids, we focus on the relationship between craving and smoking. Here, we find craving features inverse response and smoking behavior features a dramatic reduction on the quit date, followed by a resumption in smoking. Analyzing the resulting models, we find that they differ in how they describe smoking resumption, and the case is made that the feedback mechanism more appropriately describes the relationship between craving and smoking.

A system identification analysis of neural adaptation dynamics and nonlinear responses in the local reflex control of locust hind limbs

Nonlinear type system identification models coupled with white noise stimulation provide an experimentally convenient and quick way to investigate the often complex and nonlinear interactions between the mechanical and neural elements of reflex limb control systems. Previous steady state analysis has allowed the neurons in such systems to be categorised by their sensitivity to position, velocity or acceleration (dynamics) and has improved our understanding of network function. These neurons, however, are known to adapt their output amplitude or spike firing rate during repetitive stimulation and this transient response may be more important than the steady state response for reflex control. In the current study previously used system identification methods are developed and applied to investigate both steady state and transient dynamic and nonlinear changes in the neural circuit responsible for controlling reflex movements of the locust hind limbs. Through the use of a parsimonious model structure and Monte Carlo simulations we conclude that key system dynamics remain relatively unchanged during repetitive stimulation while output amplitude adaptation is occurring. Whilst some evidence of a significant change was found in parts of the systems nonlinear response, the effect was small and probably of little physiological relevance. Analysis using biologically more realistic stimulation reinforces this conclusion.

Identification of a parametric loudspeaker system using an adaptive Volterra filter

Due to the parametric acoustic array effect in air, the input audible signal of a parametric loudspeaker system can be reproduced with high directivity at the target region. However, the reproduced audible signal suffers from harmonic distortion, which is the by-product of nonlinear interaction between the primary waves. In order to investigate this inherent nonlinear phenomenon, a nonlinear system identification model is developed based on an adaptive Volterra filter. Unlike the conventional loudspeaker, the nonlinear characteristic of a parametric loudspeaker system is dependent on several primary parameters in nonlinear acoustics, which include the initial pressure of the primary waves, the observing distance and angle, as well as ambient temperature and relative humidity. By using a truncated Volterra series up to the 2nd-order kernel, numerical simulations are conducted to develop a system model with one group of parameters and examine the quadratic nonlinear intensity for different parameters’ settings. Experimental measurements, which take into account of emitter’s response, are carried out to verify the modeling result and evaluate the model performance. Based on the Volterra system model, the sound pressure level and the harmonic distortion can be accurately predicted.

Nonparametric particle filtering approaches for identification and inference in nonlinear state-space dynamic systems

Most system identification approaches and statistical inference methods rely on the availability of the analytic knowledge of the probability distribution function of the system output variables. In the case of dynamic systems modelled by hidden Markov chains or stochastic nonlinear state-space models, these distributions as well as that of the state variables themselves, can be unknown or untractable. In that situation, the usual particle Monte Carlo filters for system identification or likelihood-based inference and model selection methods have to rely, whenever possible, on some hazardous approximations and are often at risk. This review shows how a recent nonparametric particle filtering approach can be efficiently used in that context, not only for consistent filtering of these systems but also to restore these statistical inference methods, allowing, for example, consistent particle estimation of Bayes factors or the generalisation of model parameter change detection sequential tests. Real-life applications of these particle approaches to a microbiological growth model are proposed as illustrations.

가변 스텝 사이즈를 이용한 개선된 의사 인접 투사 알고리즘

NLMS 알고리즘의 수렴 특성을 개선하기 위해 제안된 인접 투사 알고리즘의 성능을 향상시킨 알고리즘을 제안하였다. 기존의 인접 투사 또는 의사 인접 투사 알고리즘은 지연된 입력 신호 벡터 및 오차 신호 벡터를 기반으로 하기 때문에 복잡할 뿐만 아니라, 기존의 스칼라 오차를 기반으로 하는 LMS 형 알고리즘에서 개발된 기법을 적용하는데 어려움이 있다. 본 논문에서는 의사 인접 투사 알고리즘의 경우, 스칼라 오차 신호를 이용한다는 사실과 수렴 후 입력 신호가 오차 신호에 직교한다는 직교 원리에 근거하여 적은 연산량으로도 우수한 수렴 성능을 보이는 가변 스텝 사이즈 알고리즘을 제안하였다. 시스템 확인 모델 하에서 컴퓨터 모의 실험을 통하여 제안된 알고리즘이 기존의 알고리즘들에 비하여 적은 연산량에도 불구하고 정상 및 비정상 환경에서 수렴 특성이 우수함을 보였다. In this paper, we propose an enhanced algorithm for affine projection algorithms which have been proposed to speed up the convergence of the conventional NLMS algorithm. Since affine projection (AP) or pseudo AP algorithms are based on the delayed input vector and error vector, they are complicated and not suitable for applying methods developed for the LMS-type algorithms which are based on the scalar error signal. We devised a variable step size algorithm for pseudo AP using the fact that pseudo AP algorithms are updated using the scalar error and that the error signal is getting orthogonal to the input signal. We carried out a performance comparison of the proposed algorithm with other pseudo AP algorithms using a system identification model. It is shown that the proposed algorithm presents good convergence characteristics under both stationary and non-stationary environments despites its low complexity.

Nonlinear Identification Approach of Nonlinear Constitutive Model of Soil Material with Optimization Procedure

Model identification of dynamic systems in the vibration engineering field has been followed with interest in recent years. A number of identification techniques on this topic are now available, such as parametric or non-parametric identification methods, time domain or frequency domain estimation approaches, etc. The identification approach of nonlinear constitutive model from input-output measurements is proposed. The inverse problem of material characterization is formulated as parameter identification problem that is solved by using optimization procedure. A set of parameters corresponding to the material property can be determined by minimizing objective function which accounts for experimental data and calculated responses of the mechanical model. The performances of the proposed identification approach were evaluated with simulating data. The effectiveness of identification approach is validated by numerical simulation. The investigation results show that the proposed identification algorithm poses good robustness and high identification precision.

Assessment of ventricular contractility and ventricular-arterial coupling with a model-based sensor

Estimation of ventricular contractility and ventricular arterial coupling is clinically important in diagnosing and treating cardiac dysfunction in the critically ill. However, experimental assessment of indexes of ventricular contractility, such as the end-systolic pressure–volume relationship, requires a highly invasive maneuver and measurements that are not typical in an intensive care unit (ICU). This research describes the use of a previously validated cardiovascular system model and parameter identification process to evaluate the right ventricular arterial coupling in septic shock. Model-based ventricular arterial coupling is defined by the ratio of the end systolic right ventricular elastance (Eesrvf) over the pulmonary artery elastance (Epa) or the mean pulmonary inflow resistance (Rpulin). Results are compared to the clinical gold-standard assessment (conductance catheter method). Six anesthetized healthy pigs weighing 20–30kg received a 0.5mgkg−1 endotoxin infusion over a period of 30min from T0 to T30, to induce septic shock and veno-venous hemofiltration was used from T60 onward. The results show good agreement with the gold-standard experimental assessment. In particular, the model-based right ventricular elastance (Eesrvf) correlates well with the clinical gold standard (R2=0.69) and the model-based non-invasive coupling (Eesrvf/Rpulin) follow the same trends and dynamics (R2=0.37). The overall results show the potential to develop a model-based sensor to monitor ventricular-arterial coupling in clinical real-time.

Identification and robustness analysis of nonlinear hybrid dynamical system concerning glycerol transport mechanism

The fermentation of glycerol by Klebsiella pneumoniae is a complex bioprocess. In this paper, a generalized nonlinear hybrid dynamical system is proposed to describe this process under continuous culture based on three different transport modes of glycerol across cell membrane. In the system, the inhibitory effect of 3-hydroxypropionaldehyde to the activities of two key enzymes (glycerol dehydratase and 1,3-propanediol oxydoreductase) and to the specific cell growth rate are all taken into consideration. To infer the most reasonable transport mode of glycerol across cell membrane on the hypothesis that 1,3-propanediol passes cell membrane by both passive diffusion and active transport, a quantitative definition of biological robustness for intracellular substances is presented. Taking the presented biological robustness and relative error between experimental data and computational values as performance index, a system identification model is established. Numerical results show that it is most possible for glycerol to pass cell membrane by passive diffusion.

Continuous-time bilinear system identification using single experiment with multiple pulses

A novel method is presented for the identification of a continuous-time bilinear system from the input–output data generated by a single experiment with multiple pulses. In contrast to the conventional approach utilizing multiple experiments, the current work documents the advantage of using a single experiment and sets up a procedure to obtain bilinear system models. The special pulse inputs employed by earlier research can be avoided and accurate identification of the continuous-time system model is possible by performing a single experiment incorporating a class of control input sequences combining pulses with free-decay response. The algorithm presented herein is more attractive in practice for the identification of bilinear systems. Numerical examples presented demonstrate the methodology developed in the paper.

On Improving Probability Models in System Identification

AbstractA solution of the following problem is presented. Given a known model represented by a discrete probability distribution q¯ = (q1, q2, … , qn), and information Δ, a new improved model represented by a probability distribution p¯ is found which is associated with q¯ such that Δ = D( p¯|| q¯), where D( p¯|| q¯) represents the relative information between the two probability distributions. The improved probability distributions p¯ have been chosen to be those which become local minima of the discrete entropy. The fundamental properties of such models and natural effects how to use information are studied.

A New Hammerstein Model for Non-Linear System Identification

In the present work a newer type of black box nonlinear model in Hammerstein structure is proposed. The model has Wavelet Network coupled with Orthonormal Basis Functions which is capable of modeling a class of non-linear systems with acceptable accuracy. Wavelet basis functions have the property of localization in both the time and frequency domains which enables wavelet networks to approximate severe non-linearities using few number of parameters. Orthonormal Basis functions possess the ability to approximate any linear time invariant system using appropriate basis functions. The efficacy of the model in modeling is demonstrated using numerical examples.

Energy-Efficient Building HVAC Control Using Hybrid System LBMPC

Abstract Improving the energy-efficiency of heating, ventilation, and air-conditioning (HVAC) systems has the potential to realize large economic and societal benefits. This paper concerns the system identification of a hybrid system model of a building-wide HVAC system and its subsequent control using a hybrid system formulation of learning-based model predictive control (LBMPC). Here, the learning refers to model updates to the hybrid system model that incorporate the heating effects due to occupancy, solar effects, outside air temperature (OAT), and equipment, in addition to integrator dynamics inherently present in low-level control. Though we make significant modeling simplifications, our corresponding controller that uses this model is able to experimentally achieve a large reduction in energy usage without any degradations in occupant comfort. It is in this way that we justify the modeling simplifications that we have made. We conclude by presenting results from experiments on our building HVAC testbed, which show an average of 1.5MWh of energy savings per day (p = 0.002) with a 95% confidence interval of 1.0MWh to 2.1MWh of energy savings.

Real-Time Internet-Based Teleoperation

Internet-based teleoperation employs robots and internet a two breakthrough technologies to manipulate robots from distance for different applications. Variable and unknown time delay dynamics of internet is the main obstacle for realtime teleoperation via internet. In this paper the internet delay dynamics and its characteristics have been studied based on the measurement in different nodes. Then a black-box model for end-to-end internet delay dynamics has been developed using system identification and Auto-Regressive eXogeneous (ARX) model. Our experimental studies show a regular periodic behaviour in long-term intervals of internet delay variation and also confirm the accuracy and reliability of our theoretical and modelling derivations. This paper also introduces a novel multivariable control method for real-time telerobotic operations via Internet. Random communications delay of the Internet can cause instability in realtime closed-loop telerobotic systems. When a single identification model is used, it will have to adapt itself to the operating condition before an appropriate control mechanism can be applied. Slow adaptation may result in a large transient error. As an alternative, we propose to use a Multiple Model framework. The control strategy is to determine the best model for the current operating condition and activate the corresponding controller. We propose the use of Multi-Model Adaptive Control Theory and Multivariable Wave prediction method to capture the concurrency and complexity of Internet-based teleoperation. The results confirm the efficiency of the proposed technique in dealing with constant and variable delay dynamics of internet.

Damage Localization of Frame Structures Using Seismic Acceleration Response Data

In this study, damage localization of frame structures from seismic acceleration responses is explored using the DLV technique and ARX model for system identification. The concept of the DLV method is to identify the members with zero stress under some specific loading patterns derived by interrogating the changes in flexibility matrix of the structure before and after the damage state. Success of the DLV method for damage localization lies on the ability to identify the flexibility matrix. The ARX model, a discrete-time non-parametric auto-regressive system identification technique is adopted to identify the modal parameters (natural frequencies, transfer functions and mode shapes) from which the flexibility matrices of the intact and damaged structures are constructed. To explore the effectiveness of the DLV method, a five-storey steel model frame with diagonal bracings was considered for seismic shaking table tests. The damage conditions of the structure were simulated by partially removing some of the diagonals. With the flexibility matrices of both the intact and damaged structures synthesized on a truncated modal basis, the damage locations have been successfully identified by the DLV method for either single or multiple damage conditions, regardless of the damage locations. This study confirms the potential of the DLV method in the detection of local damages from global seismic response data for frame structures.

Damage Localization of Frame Structures Using Seismic Acceleration Response Data

In this study, damage localization of frame structures from seismic acceleration responses is explored using the DLV technique and ARX model for system identification. The concept of the DLV method is to identify the members with zero stress under some specific loading patterns derived by interrogating the changes in flexibility matrix of the structure before and after the damage state. Success of the DLV method for damage localization lies on the ability to identify the flexibility matrix. The ARX model, a discrete-time non-parametric auto-regressive system identification technique is adopted to identify the modal parameters (natural frequencies, transfer functions and mode shapes) from which the flexibility matrices of the intact and damaged structures are constructed. To explore the effectiveness of the DLV method, a five-storey steel model frame with diagonal bracings was considered for seismic shaking table tests. The damage conditions of the structure were simulated by partially removing some of the diagonals. With the flexibility matrices of both the intact and damaged structures synthesized on a truncated modal basis, the damage locations have been successfully identified by the DLV method for either single or multiple damage conditions, regardless of the damage locations. This study confirms the potential of the DLV method in the detection of local damages from global seismic response data for frame structures.

An Adaptive Vibration Control Procedure Based on Symbolic Solution of Diophantine Equation

AbstractIn this paper, the adaptive control based on symbolic solution of Diophantine equation is used to suppress circular plate vibrations. It is assumed that the system to be regulated is unknown. The plate is excited by a uniform force over the bottom surface generated by a loudspeaker. The axially-symmetrical vibrations of the plate are measured by the application of the strain sensors located along the plate radius, and two centrally placed piezoceramic discs are used to cancel the plate vibrations. The adaptive control scheme presented in this work has the ability to calculate the error sensor signals, to compute the control effort and to apply it to the actuator within one sampling period. For precise identification of system model the regularized RLS algorithm has been applied. Self-tuning controller of RST type, derived for the assumed system model of the 4th order is used to suppress the plate vibration. Some numerical examples illustrating the improvement gained by incorporating adaptive control are demonstrated.

Condition assessment of a heritage arch bridge using a novel model updation technique

The bridge network on Indian Railways comprises a large number of masonry arch bridges, a significant number of which are more than 100 years old. The traffic intensities and the loads that are imposed on these structures are significantly higher than those for which they were designed. A significant proportion of these arch bridges is still in good condition and might be structurally adequate for the increased traffic conditions and need not be replaced. A robust identification and assessment procedure is, therefore, critical to enable proper decisions to be taken by railway authorities for managing their arch bridges. A comprehensive monitoring of a 75-year-old heritage stone masonry arch was undertaken. The measured responses were used along with a generated finite element model for structural system identification. The finite element model was updated to estimate the properties and associated degradation of the materials in the masonry arch. The condition of the structure was then estimated to be satisfactory as the converged parameters were well within the expected ranges for the materials in question. The updated model can also be used to reliably check the adequacy of the bridge for future design loads. As the monitoring cost is just a fraction of the replacement cost of the system, this process shows promise of being able to be used to validate the existing infrastructure of stone masonry arch railway bridges and to optimize their replacement.

Reduced order modeling of X-31 wind tunnel model aerodynamic loads

With recent advances in computational techniques, turbulent Navier–Stokes solvers are now capable of capturing the unsteady nonlinear aerodynamic behavior that leads to various static and dynamic instabilities of full aircraft. As such, the focus of stability and control (S&C) research has been to effectively incorporate Computational Fluid Dynamics (CFD) into the model development process using dynamic CFD solutions of complete aircraft configurations. This has lead to an innovative approach for modeling aircraft stability and control characteristics: (a) CFD simulations are performed using computational training maneuvers designed to excite the relevant flow physics encountered during actual missions, (b) a mathematical Reduced Order Model (ROM) is built of the aircraft response using system identification methods, (c) the model is validated by comparing CFD simulations against model predictions, and (d) predictions of all fight test points are made using the model to determine the expected behavior of the aircraft. This process would identify unexpected S&C issues early in the design process. This article describes the application of this innovative approach to a problem of the System Identification (SID) of a high performance fighter-type configuration, the X-31. The training maneuver used to produce the training data has been tested in the DNW low speed wind tunnel which enables directly comparing the predictive capabilities of the CFD and wind tunnel SID modeling.

On the assessment of multivariable controllers using closed loop data. Part I: Identification of system models

Controller performance assessment of SISO and MIMO systems requires effective and systematic identification of the associated system models based on closed-loop data. In this work, a new methodology for the identification of the process, controller and disturbance models is presented for the purpose of enabling the evaluation of the performance of MIMO control systems. The methodology is based on subspace identification algorithms for the identification of the controller, process and disturbance models from closed-loop data. However, identification of the process model is enhanced by the estimation of the associated interactor matrix via the Variable Regression Estimation technique, the existence of which is mathematically proved. The proposed identification methodology is applied to two 2 × 2 systems utilizing both step-response and PRBS closed-loop data.

Identification of Nolinear Systems Using Inceremnetal and Diffusion Differential Evolution Algorithms

The paper investigates on the use of Differential Evolution (DE) for training the system identification model particularly when the measurement data are available at different sensor nodes. Under such situation the conventional DE algorithms cannot be applied directly. Hence in this paper two distributed learning algorithms known as incremental DE (IDE) and diffusion DE (DDE) have been developed to meet the requirements. The identification of nonlinear plants under different noise conditions has been obtained through simulation study and the results have been compared with distributed PSO algorithms. The performance of the proposed algorithms in terms of convergence rate and minimum mean squared error indicate that the distributed DE algorithms exhibit superior performance compared to its PSO counter parts.