Online System Identification Research Articles

Online System Identification for Lifetime Diagnostic of Supercapacitors With Guaranteed Stability

In this paper, an online system identification method is introduced for lifetime diagnostic of supercapacitors. The online strategy uses a Lyapunov-based adaptation law to estimate online the supercapacitor’s parameters. Therefore, the adaptive observer’s stability is guaranteed by Lyapunov’s direct method. State-of-health (SoH) estimation is crucial since aging introduces degradation in supercapacitors’ performance, which might eventually lead to their failure. SoH is usually measured by offline time or frequency domain characterization techniques such as spectroscopy. However, these methods require interruption of the system’s operation, and hence, they are not suitable for real-time applications such as electric vehicles. Unlike other online estimation strategies, only voltage and current measurements are required. Simulation and experimental results along with comparison against two different methods highlight the effectiveness of the proposed approach in estimating the SoH.

Online identification of non-linear dynamic systems by Wiener model using subspace method and neural networks

This paper presents a method for online identification of non-linear dynamic systems using the Wiener model. For the linear dynamic part the subspace identification method with the multivariable output-error state-space algorithm is employed, whereas for the non-linear static part the multi-layer perceptron neural network with Levenberg–Marquardt algorithm is used. The stability and convergence of the proposed method is shown using the Lyapunov direct method and the region solution of the linear matrix inequality (LMI) approach. The proposed method is tested by simulations performed on the continuous stirred tank reactor (CSTR) plant, which is presented by non-linear differential equations. Moreover, the method is applied on the input–output data that are obtained from a practical system of the CSTR plant as well as the pH neutralization plant. The results show significant improvements in online identification of the non-linear dynamic systems compared with the recently reported methods in literature.

Implementation and Evaluation of Online System Identification of Electromechanical Systems Using Adaptive Filters

Online system identification method provides useful insight into the electromechanical plant behavior during commissioning as well as health monitoring. In addition to controller tuning, the methods can be applied for identifying mechanical modes or resonances, sensor vibrations, and unmodeled load dynamics. Online system identification using frequency response analysis is considered in this study. The focus of this paper will be on estimating the real-time frequency response of various systems using adaptive filters. For comparative evaluation, a commercial off-the-shelf spectrum analyzer is used. The essential aspects to be addressed for adaptive filter are: understanding the influence of convergence gain on accuracy of frequency response and selecting the frequency sweep rate. In this work, online system identification using frequency response is demonstrated on two setups: 1) magnetic levitation system and 2) permanent-magnet motor drive operating in sensorless mode. The experimental result validates that the method elaborated is simple, easy to implement, and accurate when compared with spectrum analyzer.

Online identification for batch processes in closed loop incorporating priori controller knowledge

It is of great importance to develop an online modeling method for chemical processes operated in closed loop for better understanding, monitoring the process or other purposes without endangering the system. This paper intends to devise an online system identification method, particularly for the batch process, by fully exploiting its intrinsic repetitiveness. It properly uses the information from the time direction and the batch direction, thus leading to a gradual performance enhancement. In addition, the identification method formulates the priori controller knowledge such as closed-loop stability as optimization constraints to refine the parameter estimates. A trust region method is employed to overcome the significant computation burden of directly handling these constraints such as solving Lyapunov inequalities. An adaptive filter is introduced to further smooth the parameter estimates. Finally, the effectiveness of the approach is verified by three numerical examples including a two-tank system.

産業用マルチロータヘリコプタのオンラインシステム同定機構を有する自動チューニング適応I-PD制御

産業用マルチロータヘリコプタのオンラインシステム同定機構を有する自動チューニング適応I-PD制御

Biological characteristic online identification technique over 5G network

Although the maturity of 3G and 4G mobile communication technologies has profoundly changed our lives, people’s pursuit of higher performance for mobile communication has never stopped. In order to deal with the future explosive growth in mobile data generated by huge equipment connections and a large number of new business and application scenarios, the 5G mobile communications system is under development at amazing speed. Meanwhile, under the impetus of the high-speed transmission rate of 5G networks, biological characteristic identification technology will reflect new application forms. To improve the security level of the Internet, biological characteristic authentication and multimedia signal fast encoding over 5G are studied in this article, and the realization of the key technologies of the online identification system, which could be a very helpful model for future related research and applications, is discussed.

On fractional order adaptive observer

This article derives a new scheme to an adaptive observer for a class of fractional order systems. Global asymptotic convergence for joint state-parameter estimation is established for linear time invariant single-input single-output systems. For such fractional order systems, it is proved that all the signals in the resulting closed-loop system are globally uniformly bounded, the state and parameter estimation errors converge to zero. Potential applications of the presented adaptive observer include online system identification, fault detection, adaptive control of fractional order systems, etc. Numerical simulation examples are presented to demonstrate the performance of the proposed adaptive observer.

An Adaptive High-Precision Tracking Controller for the Coupled Switched Reluctance Two-Finger Gripper

An adaptive high-precision position control algorithm with online system identification is implemented onto the switched reluctance, two-finger gripper with coupled magnetic paths. Theoretical investigation proves that the magnetic paths from the two fingers are highly coupled. Without the introduction of any decoupling scheme, the position control performance under the proportional integral differential controller and the adaptive controller is inspected and the control results are compared. Experimental results verify that, without introducing any decoupling mechanism, the adaptive controller is capable of independent control of each finger with a high-precision and uniform position control performance.

Wind Tunnel Testing of Subspace Predictive Repetitive Control for Variable Pitch Wind Turbines

An increasing number of wind turbines implement individual blade pitch control (IPC) to reduce turbine dynamic loading, and thereby, to reduce the capital and operational costs associated with energy production. The aim of this paper is to demonstrate IPC on a wind turbine prototype, in a model-free data-driven manner and with reduced pitch activity. For this, subspace predictive repetitive control (RC) is used, which combines online system identification with the continuous implementation of RC to form a fully adaptive control law. The controller is tested on a scaled two-bladed wind turbine with active pitchable blades, placed in an open-jet wind tunnel. Substantial load reductions to an extent of 68% are observed, and strict control over actuator signal frequency content is achieved. The control law also demonstrates the ability to adjust to changes in system dynamics while maintaining a high degree of load alleviation.

A Fast Adaptive Tunable RBF Network For Nonstationary Systems.

This paper describes a novel on-line learning approach for radial basis function (RBF) neural network. Based on an RBF network with individually tunable nodes and a fixed small model size, the weight vector is adjusted using the multi-innovation recursive least square algorithm on-line. When the residual error of the RBF network becomes large despite of the weight adaptation, an insignificant node with little contribution to the overall system is replaced by a new node. Structural parameters of the new node are optimized by proposed fast algorithms in order to significantly improve the modeling performance. The proposed scheme describes a novel, flexible, and fast way for on-line system identification problems. Simulation results show that the proposed approach can significantly outperform existing ones for nonstationary systems in particular.

Energy awareness for supercapacitors using Kalman filter state-of-charge tracking

Among energy buffering alternatives, supercapacitors can provide unmatched efficiency and durability. Additionally, the direct relation between a supercapacitor's terminal voltage and stored energy can improve energy awareness. However, a simple capacitive approximation cannot adequately represent the stored energy in a supercapacitor. It is shown that the three branch equivalent circuit model provides more accurate energy awareness. This equivalent circuit uses three capacitances and associated resistances to represent the supercapacitor's internal SOC (state-of-charge). However, the SOC cannot be determined from one observation of the terminal voltage, and must be tracked over time using inexact measurements. We present: 1) a Kalman filtering solution for tracking the SOC; 2) an on-line system identification procedure to efficiently estimate the equivalent circuit's parameters; and 3) experimental validation of both parameter estimation and SOC tracking for 5 F, 10 F, 50 F, and 350 F supercapacitors. Validation is done within the operating range of a solar powered application and the associated power variability due to energy harvesting. The proposed techniques are benchmarked against the simple capacitive model and prior parameter estimation techniques, and provide a 67% reduction in root-mean-square error for predicting usable buffered energy.

On-line parameter and delay estimation of continuous-time dynamic systems

Abstract The problem of on-line identification of non-stationary delay systems is considered. The dynamics of supervised industrial processes are usually modeled by ordinary differential equations. Discrete-time mechanizations of continuous-time process models are implemented with the use of dedicated finite-horizon integrating filters. Least-squares and instrumental variable procedures mechanized in recursive forms are applied for simultaneous identification of input delay and spectral parameters of the system models. The performance of the proposed estimation algorithms is verified in an illustrative numerical simulation study.

Nonlinear adaptive filtering using kernel‐based algorithms with dictionary adaptation

SummaryNonlinear adaptive filtering has been extensively studied in the literature, using, for example, Volterra filters or neural networks. Recently, kernel methods have been offering an interesting alternative because they provide a simple extension of linear algorithms to the nonlinear case. The main drawback of online system identification with kernel methods is that the filter complexity increases with time, a limitation resulting from the representer theorem, which states that all past input vectors are required. To overcome this drawback, a particular subset of these input vectors (called dictionary) must be selected to ensure complexity control and good performance. Up to now, all authors considered that, after being introduced into the dictionary, elements stay unchanged even if, because of nonstationarity, they become useless to predict the system output. The objective of this paper is to present an adaptation scheme of dictionary elements, which are considered here as adjustable model parameters, by deriving a gradient‐based method under collinearity constraints. The main interest is to ensure a better tracking performance. To evaluate our approach, dictionary adaptation is introduced into three well‐known kernel‐based adaptive algorithms: kernel recursive least squares, kernel normalized least mean squares, and kernel affine projection. The performance is evaluated on nonlinear adaptive filtering of simulated and real data sets. As confirmed by experiments, our dictionary adaptation scheme allows either complexity reduction or a decrease of the instantaneous quadratic error, or both simultaneously. Copyright © 2015 John Wiley & Sons, Ltd.

Global Parametric Identification of a Cable-Stayed Bridge Model under Vertical Excitations Using SNLSE Approach

Global assessment of structural conditions is important for structural health monitoring system. Especially, identification of structural parameters based solely on vibration data measured from sensors has become a popular topic in the field of civil engineering. The problem becomes extremely challenging when the number of sensors installed is limited. In this paper, a newly proposed online system identification technique, referred to as the sequential nonlinear LSE (SNLSE) approach, will be studied for global parametric identification of an experimental cable-stay bridge model. A dynamic equivalent model of the bridge will be established and the finite element analysis will be carried out. Both numerical and experimental studies will be conducted and different damage scenarios and limited number of response data will be considered. The capability of the proposed SNLSE approach in identifying the structural parameters and assessing the structural conditions will be verified.

Research of Material On-Line Identification Based on Terahertz Spectrum

A material on-line identification system based on THz time-domain spectroscopy using VC was developed in this paper. The main functions including data reading, linear motor driving, the calculation of optical parameters, the display and management of curve, the management of interface, the analysis of historical data , the maintenance and updating of database, and on-line identification based on principal component analysis were realized. As the prototype of material detection application, the material whose corresponding type characteristic spectrum has already existed in the database could be well identified.

An Interval Type-2 Neural Fuzzy System for Online System Identification and Feature Elimination.

We propose an integrated mechanism for discarding derogatory features and extraction of fuzzy rules based on an interval type-2 neural fuzzy system (NFS)-in fact, it is a more general scheme that can discard bad features, irrelevant antecedent clauses, and even irrelevant rules. High-dimensional input variable and a large number of rules not only enhance the computational complexity of NFSs but also reduce their interpretability. Therefore, a mechanism for simultaneous extraction of fuzzy rules and reducing the impact of (or eliminating) the inferior features is necessary. The proposed approach, namely an interval type-2 Neural Fuzzy System for online System Identification and Feature Elimination (IT2NFS-SIFE), uses type-2 fuzzy sets to model uncertainties associated with information and data in designing the knowledge base. The consequent part of the IT2NFS-SIFE is of Takagi-Sugeno-Kang type with interval weights. The IT2NFS-SIFE possesses a self-evolving property that can automatically generate fuzzy rules. The poor features can be discarded through the concept of a membership modulator. The antecedent and modulator weights are learned using a gradient descent algorithm. The consequent part weights are tuned via the rule-ordered Kalman filter algorithm to enhance learning effectiveness. Simulation results show that IT2NFS-SIFE not only simplifies the system architecture by eliminating derogatory/irrelevant antecedent clauses, rules, and features but also maintains excellent performance.

Detection of Intoxicated Drivers Using Online System Identification of Steering Behavior

Impaired driving is known to be among the leading causes of death and injury on roads; however, the existing measures to address this menace appear to be insufficient. This paper presents a novel method to detect intoxicated driving and lays a foundation that can be implemented in future cars to derive personalized models of drivers and to detect not only intoxicated driving but also other reckless driving styles. We employ system identification techniques to develop models for sober and impaired drivers. A total of 200 sets of data from various subject drivers were collected in a high-fidelity driving simulator. The lateral preview error and the steering wheel angle were considered the input and output of a driver, respectively. We will demonstrate that the autoregressive noise integration moving average with exogenous input (ARIMAX) model best fits the data to describe the steering behavior of drivers. The positions of model poles are shown to be a good indicator of intoxicated driving behavior. An aggressive driving style due to impaired driving leads to the migration of dominant poles toward the instability region. The Kalman filter and online identification techniques are used to update the driver model during driving. The poles of this updated model are used for the detection of impaired driving.

Adaptive Web QoS controller based on online system identification using quantum-behaved particle swarm optimization

Web quality of service (QoS) control can effectively prevent a Web Server from becoming overloaded. This paper presents an adaptive controller for Web QoS, which dynamically adjusts parameters of the proportional-integral (PI) controller according to the changes of the Web server model. Different from the existing methods based on the off-line system identification, the adaptive Web QoS controller is implemented based on the online system identification through a quantum-behaved particle swarm optimization (QPSO) algorithm and a mutated version of QPSO (MuQPSO). The proposed approach for online system identification by QPSO and MuQPSO shows better performance than genetic algorithms and the particle swarm optimization algorithm on the simulation tests. Then, the performance of the adaptive controller for Web QoS is tested in three experiments and compared with that of fixed PI controller. Experimental results show that the adaptive control employing online system identification by QPSO and MuQPSO algorithms is able to control the Web server resource more effectively in case of overload and, thus, improves the Web QoS.

Performance Improvement of PID-DAC-Nonlinear-Compensator by Applying Online System Identification

Performance Improvement of PID-DAC-Nonlinear-Compensator by Applying Online System Identification

2A1-F04 マルチロータ型ヘリコプターのオンラインシステム同定(飛行ロボット・メカトロニクス(1))

This study describes online system identification of an altitude model and adaptive control of a multi-rotor helicopter. The identification approach is a nonlinear filtering problem for a augmented state space. As a system identification algorithm, EKF (Extended Kalman Filter) and UKF (Unscented Kalman Filter) are compared. Furthermore, Self-Tuning PID Controller is designed. Once PID gains are tuned, Self-Tuning PID Controller tunes these gains automatically in response to the system variation. This tuning algorithm is based on a characteristic equation of the feedback system. A simulation and offline data processing are done to verify the efficiency of the algorithm.