System Identification Model Research Articles

Forecasting density, oil formation volume factor and bubble point pressure of crude oil systems based on nonlinear system identification approach

Accurate predictions of fluid properties, such as density, oil formation volume factor and bubble point pressure, are essentials for all reservoir engineering calculations. In this paper, an approach based on nonlinear system identification modeling; Nonlinear ARX (NARX) and Hammerstein-Wiener (HW) predictive model, is proposed for forecasting the pressure/volume/temperature (PVT) properties of crude oil systems. To this end, two datasets; one containing 168 PVT samples from different Iranian oil reservoirs and other a databank containing 755 data from various geographical locations, were employed to construct (i.e. train) and evaluate (i.e. test) the models. Simulation results demonstrate that the proposed NARX and HW models outperform previously employed methods including three types of artificial neural networks models (committee machine, multilayer perceptron and radial basis function), two types of ANFIS models (grid partition and fuzzy c-mean) and several empirical correlations with the smallest prediction error, and that they are reliable models for predicting the oil properties in reservoirs engineering among other soft computing approaches.

System identification modeling and unstable behavior of the dynamics of flows within the tip region of an axial compressor blade passage

In recent years, the correlation coefficient of pressure data from the same blade passage in an axial compressor unit has been used to characterize the state of flow in the blade passage. In addition, the correlation coefficient has been successfully used as an indicator for active control action using air injection. In this work, the correlation coefficient approach is extended to incorporate system identification algorithms in order to extract a mathematical model of the dynamics of the flows within a blade passage. The dynamics analyzed in this research focus on the flow streams and pressure along the rotor blades as well as on the unsteady tip leakage flow from the rotor tip gaps. The system identification results are used to construct a root locus plot for different flow coefficients, starting far away from stall to near stall conditions. As the compressor moves closer to stall, the poles of the identified models move towards the imaginary axis of the complex plane, indicating an impending instability. System frequency data is captured using the proposed correlation based system identification approach. Additionally, an oscillatory tip leakage flow is observed at a flow coefficient away from stall and how this oscillation changes as the compressor approaches stall is an interesting result of this research. Comparative research is analyzed to determine why the oscillatory flow behavior occurs at a specific sensor location within the tip region of the rotor blade.

Fuzzy functions with function expansion model for nonlinear system identification

AbstractIn this study, the structure of fuzzy functions is improved by function expansion. Unlike fuzzy conventional if-then rules, classical fuzzy function structure includes fuzzy bases and linear inputs. Membership functions of fuzzy bases are set using fuzzy C-means (FCM) algorithm, and the linear parameters are computed using the least-square estimation (LSE). This study has two main contributions. First, conventional “fuzzy functions” structure is powered by the expansion of orthogonal “trigonometric functions” where the approximation capabilities of the fuzzy functions are increased. Second, the widths of the normalized membership functions determined for the fuzzy function model are optimized using the Nelder-Mead simplex algorithm that provides further enhancement on the identification performance. The advantages of the proposed model are shown via offline identification of a benchmark nonlinear system and online identification of two real-time nonlinear systems.

White box, black box and self-organization

Purpose – The purpose of this paper is to present two system identification models – “white box” and “black box” – as useful tools that help understand self-organization processes within and outside the organizations facilitated by leaders. Every leader is presented as a “systems designer” who plays a fundamental role in the process of self-organization, both within and outside the organization under study. Design/methodology/approach – First, “white box” and “black box” system identification models are presented as a basis for an integrated model of the “system” and its “environment.” Next, the ideas of “closed” and “open” systems as the prerequisites of self-organization processes are described. Finally, two basic leadership tactics as well as their combination are characterized and discussed. Findings – Two system identification models give a complementary view to the reality, as they combine both reductionist and holistic perspectives. The argument presented in the paper shows that there is a far reaching complementarity of the two system identification models. Practical implications – Since leaders need to comprehend complex adaptive processes taking place in the organizations and in their environment, they search for the best strategy to approach this task. The tactics presented in the paper could serve as a cognitive tools that help approach the reality leaders are immersed in. Originality/value – The paper utilizes two categories that are well recognized in systems theory and cybernetics, combines them with the idea of self-organization and puts it all in the context of leadership. It provides an integrated, yet relatively simple cognitive scheme that may be of theoretical and practical use.

Latent Autoregressive Gaussian Processes Models for Robust System Identification

We introduce GP-RLARX, a novel Gaussian Process (GP) model for robust system identification. Our approach draws inspiration from nonlinear autoregressive modeling with exogenous inputs (NARX) and it encapsulates a novel and powerful structure referred to as latent autoregression. This structure accounts for the feedback of uncertain values during training and provides a natural framework for free simulation prediction. By using a Student-t likelihood, GP-RLARX can be used in scenarios where the estimation data contain non-Gaussian noise in the form of outliers. Further, a variational approximation scheme is developed to jointly optimize all the hyperparameters of the model from available estimation data. We perform experiments with five widely used artificial benchmarking datasets with different levels of outlier contamination and compare GP-RLARX with the standard GP-NARX model and its robust variant, GP-tVB. GP-RLARX is found to outperform the competing models by a relatively wide margin, indicating that our latent autoregressive structure is more suitable for robust system identification.

The Use of Vibration Signals for Structural Health Monitoring, System Identification, Test Planning/Optimization, and Dynamic Model Validation/Updating

Departamento de Engenharia Mecânica Faculdade de Engenharia de Ilha Solteira Universidade Estadual Paulista (UNESP)

System identification modelling based on modification of all terrain vehicle (ATV) using wireless control system

System identification modelling based on modification of all terrain vehicle (ATV) using wireless control system

NARMAX model identification using a set-theoretic evolutionary approach

A method is presented for identification of system models that are linear in parametric structure, but arbitrarily nonlinear in signal operations. The fundamental parameter estimation task uses a set-theoretic analysis of the data to deduce feasible sets of solutions in light of certain model assumptions. In turn, measurable set solution properties are used to assess the viability of nonlinear regressor functions that “compete for survival” as components of the model best fit to represent the system. The strategy blends traditional system identification methods with three modeling strategies that are not commonly employed in signal processing: linear-time-invariant-in-parameters models, set-based parameter identification, and evolutionary selection of the model structure. The algorithm can identify nonlinear model structure and estimate parameters in the presence of different unknown noise scenarios, especially correlated noise. This paper reports the theoretical foundation of the methods and the simulation studies to illustrate the performance benefits.

NON-PARAMETRIC IDENTIFICATION TECHNIQUES FOR INTELLIGENT PNEUMATIC ACTUATOR

The aim of this paper is to present experimental, empirical and analytic identification techniques, known as non-parametric techniques. Poor dynamics and high nonlinearities are parts of the difficulties in the control of pneumatic actuator functions, which make the identification technique very challenging. Firstly, the step response experimental data is collected to obtain real-time force model of the intelligent pneumatic actuator (IPA). The IPA plant and Personal Computer (PC) communicate through Data Acquisition (DAQ) card over MATLAB software. The second method is approximating the process by curve reaction of a first-order plus delay process, and the third method uses the equivalent n order process with PTn model parameters. The obtained results have been compared with the previous study, achieved based on force system identification of IPA obtained by the (Auto-Regressive model with eXogenous) ARX model. The models developed using non-parameters identification techniques have good responses and their responses are close to the model identified using the ARX system identification model. The controller approved the success of the identification technique with good performance. This means the Non-Parametric techniques are strongly recommended, suitable, and feasible to use to analyze and design the force controller of IPA system. The techniques are thus very suitable to identify the real IPA plant and achieve widespread industrial acceptance.

Reduced order modeling and parameter identification of a building energy system model through an optimization routine

Different control techniques together with intelligent building technology (Building Automation Systems) are used to improve energy efficiency of buildings. In almost all control projects, it is crucial to have building energy models with high computational efficiency in order to design and tune the controllers and simulate their performance. In this paper, a set of partial differential equations are formulated accounting for energy flow within the building space. These equations are then solved as conventional finite difference equations using Crank–Nicholson scheme. Such a model of a higher order is regarded as a benchmark model. An optimization algorithm has been developed, depicted through a flowchart, which minimizes the sum squared error between the step responses of the numerical and the optimal model. Optimal model of the construction element is nothing but a RC-network model with the values of Rs and Cs estimated using the non-linear time invariant constrained optimization routine. The model is validated with comparing the step responses with other two RC-network models whose parameter values are selected based on a certain criteria. Validations are showed for different types of building construction elements viz., low, medium and heavy thermal capacity elements. Simulation results show that the optimal model closely follow the step responses of the numerical model as compared to the responses of other two models.

A CS Recovery Algorithm for Model and Time Delay Identification of MISO-FIR Systems

This paper considers identifying the multiple input single output finite impulse response (MISO-FIR) systems with unknown time delays and orders. Generally, parameters, orders and time delays of an MISO system are separately identified from different algorithms. In this paper, we aim to perform the model identification and time delay estimation simultaneously from a limited number of observations. For an MISO-FIR system with many inputs and unknown input time delays, the corresponding identification model contains a large number of parameters, requiring a great number of observations for identification and leading to a heavy computational burden. Inspired by the compressed sensing (CS) recovery theory, a threshold orthogonal matching pursuit algorithm (TH-OMP) is presented to simultaneously identify the parameters, the orders and the time delays of the MISO-FIR systems. The proposed algorithm requires only a small number of sampled data compared to the conventional identification methods, such as the least squares method. The effectiveness of the proposed algorithm is verified by simulation results.

NeuroControl of movement: system identification approach for clinical benefit.

Progress in diagnosis and treatment of movement disorders after neurological diseases like stroke, cerebral palsy (CP), dystonia and at old age requires understanding of the altered capacity to adequately respond to physical obstacles in the environment. With posture and movement disorders, the control of muscles is hampered, resulting in aberrant force generation and improper impedance regulation. Understanding of this improper regulation not only requires the understanding of the role of the neural controller, but also attention for: (1) the interaction between the neural controller and the “plant”, comprising the biomechanical properties of the musculaskeletal system including the viscoelastic properties of the contractile (muscle) and non-contractile (connective) tissues: neuromechanics; and (2) the closed loop nature of neural controller and biomechanical system in which cause and effect interact and are hence difficult to separate. Properties of the neural controller and the biomechanical system need to be addressed synchronously by the combination of haptic robotics, (closed loop) system identification (SI), and neuro-mechanical modeling. In this paper, we argue that assessment of neuromechanics in response to well defined environmental conditions and tasks may provide for key parameters to understand posture and movement disorders in neurological diseases and for biomarkers to increase accuracy of prediction models for functional outcome and effects of intervention.

System identification modelling of ship manoeuvring motion based on support vector regression

Based on the ɛ – support vector regression, three modelling methods for the ship manoeuvring motion, i.e., the white-box modelling, the grey-box modelling and the black-box modelling, are investigated. The 10°/10°, 20°/20° zigzag tests and the 35° turning circle manoeuvre are simulated. Part of the simulation data for the 20°/20° zigzag test are used to train the support vectors, and the trained support vector machine is used to predict the whole 20°/20° zigzag test. Comparison between the simulated and predicted 20°/20° zigzag test shows a good predictive ability of the three modelling methods. Then all mathematical models obtained by the modelling methods are used to predict the 10°/10° zigzag test and 35° turning circle manoeuvre, and the predicted results are compared with those of simulation tests to demonstrate the good generalization performance of the mathematical models. Finally, the modelling methods are analyzed and compared with each other in terms of the application conditions, the prediction accuracy and the computation speed. An appropriate modelling method can be chosen according to the intended use of the mathematical models and the available data for the system identification.

A Control-Oriented Model of a PEM Fuel Cell Stack Based on NARX and NOE Neural Networks

Hydrogen-related technologies have been proposed as an alternative to store the energy surplus from renewable sources. Among these technologies, the proton exchange membrane fuel cell (PEMFC) and electrolyzer are the preferred choice for practical applications since they have reached a certain level of maturity and are commercially available at present. In order to achieve a cost-effective operation, a PEMFC stack must operate at maximum efficiency most of the time. Since PEMFC stacks present a time-varying behavior, an adaptive model-based controller should be employed to accomplish this goal. A fixed-parameter electrochemical model may not offer a reliable prediction over a midterm time horizon for such a controller. For this reason, system identification techniques appear as more appropriate choices to obtain an effective model for this class of control systems. In this paper, a system identification modeling methodology employing nonlinear autoregressive with exogenous input (NARX) and nonlinear output error (NOE) neural networks is presented to obtain a black-box model of a PEMFC stack oriented for a predictive control system. The experimental data for the model development are obtained with a commercial 3-kW PEMFC stack. The model built according to the proposed methodology provides accurate predictions of the voltage for the whole operating range of the stack for a long time and, hence, the ability to represent the time-varying behavior of a PEMFC stack for a predictive control application.

Virtual partition surface temperature sensor based on linear parametric model

Multi-zone structure is common in commercial office buildings, retail stores and supermarkets. Because there is no physical partition between zones, there could exist significant thermal impact among the adjacent zones than in other commercial buildings. It is critical to accurately analyze the energy interaction and reduce the modeling uncertainty for supporting advanced control. We propose a virtual partition surface temperature sensor for quantifying the variables and solve this challenge by using linear parametric models. The derived models based on physical law can be used as a guideline on selecting appropriate orders in mainly ARMAX (autoregressive moving average with exogenous variables) and/or ARX for improving the sensor’s performance. Validation of the virtual temperature models is conducted by three validation criteria: goodness of fit (G), mean squared error (MSE) and coefficient of determination (R2) under off-control conditions. The validation results show that the physical model based linear parametric model, ARX 211, performs well and similar to other system identification models, such as ARMAX 2111 and ARX 221, for estimating surface temperatures. The sensitivity analysis using three on-control conditions (under-sizing, proper-sizing and over-sizing condition) is conducted for analyzing and evaluating the performance and barriers of this virtual sensor. The proposed easy-to-implement model can be applied to support supervisory control of equipment in multi-zone buildings and other applications to supplement the measurements, like estimating the temperature of a structure integrated cooling or heating application in renewable energy areas.

Long Term Precision of Dual-Energy X-ray Absorptiometry VAT Measurements and Their Associations

The JSAE & SICE Joint Research Committee on Control and Modeling virtual test engine was used as data source for a system identification and subsequent modeling attempt. The benchmark task was the identification of the air mass flow and the engine torque of an internal combustion engine over the whole operating range. The model represents the dynamics of a 4-cylinder Gasoline engine with a basic control unit with typical control inputs. After initial investigations into the dynamics of the engine model, initial data was acquired to gain insight into the system dynamics. Using DoE and dynamic modeling, a model was devised. It was shown that with reasonable modeling effort, the air mass flow could be modeled to an accuracy for the validation run of 1.2 g/s RSME, 2.9 g/s max, while the torque output could be estimated to within 4.6 Nm RMSE, 9.7 Nm max. Usage of a virtual test bench is beneficial to demonstrate benefits of new algorithms or methods inexpensively and efficiently. This exercise was undertaken by IAV within the benchmark challenge of the Japanese calibration user group, JCUG.

A Developed Local Polynomial Neuro-Fuzzy Model for Nonlinear System Identification

This paper proposes a local polynomial model tree (LPMT) learning technique for prediction and identification of nonlinear systems and processes. The proposed LPMT learning algorithm is applied to a local polynomial neuro fuzzy (LPNF) model, which includes local polynomial models (LPMs), as Taylor series expansion of any unknown function. . The LPMT algorithm is established based on the concept of hierarchical binary tree and heuristically partitions the input space into smaller subdomains through axis-orthogonal splits. The proposed learning algorithm starts from a single local polynomial model and then refines the LPNF model by increasing the degree of the worstperforming LPM or by further partitioning of the input space. During the learning procedure, the validity functions automatically form a partition of unity and therefore normalization side effects, e.g. reactivation and poor extrapolation performance, are prevented. The LPNF model, trained by the proposed LPMT algorithm, is used for prediction and identification of the nonlinear processes and systems in three case studies. The obtained results and comparisons to other methods revealed the promising performance of the proposed model.

System identification modeling of ship manoeuvring motion in 4 degrees of freedom based on support vector machines

Based on support vector machines, three modeling methods, i.e., white-box modeling, grey-box modeling and black-box modeling of ship manoeuvring motion in 4 degrees of freedom are investigated. With the whole-ship mathematical model for ship manoeuvring motion, in which the hydrodynamic coefficients are obtained from roll planar motion mechanism test, some zigzag tests and turning circle manoeuvres are simulated. In the white-box modeling and grey-box modeling, the training data taken every 5 s from the simulated 20°/20° zigzag test are used, while in the black-box modeling, the training data taken every 5 s from the simulated 15°/15°, 20°/20° zigzag tests and 15°, 25° turning manoeuvres are used; and the trained support vector machines are used to predict the whole 20°/20° zigzag test. Comparisons between the simulated and predicted 20?/20° zigzag tests show good predictive ability of the proposed methods. Besides, all mathematical models obtained by the proposed modeling methods are used to predict the 10°/10° zigzag test and 35° turning circle manoeuvre, and the predicted results are compared with those of simulation tests to demonstrate the good generalization performance of the mathematical models. Finally, the proposed modeling methods are analyzed and compared with each other in aspects of application conditions, prediction accuracy and computation speed. The appropriate modeling method can be chosen according to the intended use of the mathematical models and the available data needed for system identification.

Identifikasi Model dari Pesawat Udara Tanpa Awak Sayap Tetap Jenis Bixler

AbstrakModel terbang merupakan bagian penting dalam pembangunan sistem kontrol suatu pesawat udara tanpa awak sayap tetap, terutama dalam pembangunan sistem kendali mandiri atau autopilot pesawat. Dengan model terbang, gerak terbang pesawat dapat direpresentasikan dan disimulasikan. Pada penelitian ini, model terbang dihasilkan melalui identifikasi sistem atau pemodelan pesawat udara tanpa awak sayap tetap jenis Bixler. Identifikasi sistem dilakukan berdasarkan eksperimen (pengambilan data terbang) dan digunakan struktur model state-space. Terdapat tiga tahapan dalam penelitian ini, pemodelan gerak terbang pesawat, pengambilan data terbang, dan identifikasi model terbang. Melalui tiga tahapan tersebut, diperoleh model terbang pesawat sayap tetap jenis Bixler yang dapat merepresentasikan gerak terbang pesawat yang terbagi dalam 2 mode, mode longitudinal dan mode lateral.Diperoleh model terbang mode longitudinal pesawat udara tanpa awak sayap tetap jenis Bixler menggunakan 13 parameter menggunakan struktur model state-space orde 4. Model terbang mode lateral pesawat udara tanpa awak sayap tetap jenis Bixler menggunakan 11 parameter menggunakan struktur model state-space orde 4. Kata kunci—UAV, Identifikasi Sistem, Pemodelan AbstractFlight model is one of importing thing in fixed-wing unmanned aerial vehicle (UAV) control system development, mainly in the aircraft autopilot. Through this flight model, the aircraft motion can be represented and simulated.In this research, the flight model is obtained through system identification and system modelling of Bixler fixed-wing unmanned aerial vehicle. System identification is based on experiment data and use state-space model structure. There are three stages in this research, aircraft motion system modelling, flight data collecting, and flight model identification. Through those three stages, Bixler fixed-wing unmanned aerial vehicle flight model is obtained as represented in two modes, longitudinal mode and lateral modeThe Bixler fixed-wing unmanned aerial vehicle longitudinal mode flight model is obtained using 13 parameters. The lateral mode is obtained using 11 parameters. All modes are in 4th order state space model structure. Keywords— UAV, System Identification, Modelling

PCA-based neuro-fuzzy model for system identification of smart structures

This paper proposes an efficient system identification method for modeling nonlinear behavior of civil structures. This method is developed by integrating three different methodologies: principal component analysis (PCA), artificial neural networks, and fuzzy logic theory, hence named PANFIS (PCA-based adaptive neuro-fuzzy inference system). To evaluate this model, a 3-story building equipped with a magnetorheological (MR) damper subjected to a variety of earthquakes is investigated. To train the input-output function of the PANFIS model, an artificial earthquake is generated that contains a variety of characteristics of recorded earthquakes. The trained model is also validated using the1940 El-Centro, Kobe, Northridge, and Hachinohe earthquakes. The adaptive neuro-fuzzy inference system (ANFIS) is used as a baseline. It is demonstrated from the training and validation processes that the proposed PANFIS model is effective in modeling complex behavior of the smart building. It is also shown that the proposed PANFIS produces similar performance with the benchmark ANFIS model with significant reduction of computational loads.