Accuracy Of System Identification Research Articles

Identification of fractional order time delay system with measurement noise using variable period integration operational matrix

This paper proposes a parameter identification method for fractional-order time-delay systems with measurement noise, based on the Legendre wavelet variable-period integration operational matrix. A variable-period integration operational matrix and a variable-period time-delay integration operational matrix are constructed by variable-period sampling principle. For the impulse noise, global Gaussian noise and local Gaussian noise present in the identification data, the variable-period sampling, operational matrix global coefficient decomposition and matrix local coefficient decomposition methods are used to reconstruct the variable-period integration operational matrix to separate the noise from the data; the fractional-order time-delay system is expressed as an integral equation by using the reconstructed variable-period operational matrix, this reduces the influence of measurement noise on system identification accuracy. To further improve the parameter identification accuracy, the integral equation is solved by the multi-innovation least squares algorithm. The proposed method’s effectiveness is verified through several simulations and an experimental study of a constant-temperature system.

A few-shot identification method for stochastic dynamical systems based on residual multipeaks adaptive sampling.

Neural networks are popular data-driven modeling tools that come with high data collection costs. This paper proposes a residual-based multipeaks adaptive sampling (RMAS) algorithm, which can reduce the demand for a large number of samples in the identification of stochastic dynamical systems. Compared to classical residual-based sampling algorithms, the RMAS algorithm achieves higher system identification accuracy without relying on any hyperparameters. Subsequently, combining the RMAS algorithm and neural network, a few-shot identification (FSI) method for stochastic dynamical systems is proposed, which is applied to the identification of a vegetation biomass change model and the Rayleigh-Van der Pol impact vibration model. We show that the RMAS algorithm modifies residual-based sampling algorithms and, in particular, reduces the system identification error by 76% with the same sample sizes. Moreover, the surrogate model accurately predicts the first escape probability density function and the P bifurcation behavior in the systems, with the error of less than 1.59×10-2. Finally, the robustness of the FSI method is validated.

An efficient non-negative least mean squares algorithm based on q-gradient for system identification

The system identification under non-negative constraints problem is a common and important one in the real-life problems. A non-negative least mean squares algorithm was proposed to address such problems. However, it suffers from slow and unbalanced convergence. Motivated by the introduction of non-Newtonian gradient in least mean squares algorithm can accelerate the convergence. In this paper, an efficient non-negative least mean squares algorithm based on q-gradient is developed. At first, the q-gradient of the mean square error cost function is derived from the definition of Jackson's derivative. Then, the modified algorithm is developed by replacing the conventional gradient with the q-gradient on a fixed-point iteration scheme using Karush-Kuhn-Tucker condition. Simulations for stationary and non-stationary system identification are conducted to illustrate the effectiveness of the developed algorithm for constrain the system parameters to be non-negative and accelerate the convergence speed. Besides, it can balance the convergence rate for widely distributed parameters, especially for the convergence of small parameters. For practical application, the accuracy of system identification when the order of the adaptive filter is larger than the order of the system is also verified by the simulation.

System Identification and Fractional-Order Proportional–Integral–Derivative Control of a Distributed Piping System

The vibration of piping systems is one of the most important causes of accelerated equipment wear and reduced work efficiency and safety. In this study, an active vibration control method based on a fractional-order proportional–integral–derivative (PID) controller was proposed to suppress pipeline vibration and reduce pipeline damage. First, a mathematical model of the distributed piping system was established using the finite element analysis method, and the characteristics of the distributed piping system were studied effectively. Further, the time-frequency domain parameter identification method was used to realise the system identification of the cross-point vibration transfer function between the brake and sensor, and the particle swarm optimisation algorithm was utilised to further optimise the transfer function parameters to improve the system identification accuracy. Therefore, a fractional-order PID controller was designed using the D-decomposition method, and the optimal controller parameters were obtained. The experimental and numerical simulation results show that the improved system identification algorithm can significantly improve modelling accuracy. In addition, the designed fractional-order PID controller can effectively reduce the system’s overshoot, oscillation time, and adjustment time, thereby reducing the vibration response of piping systems.

Adaptive Control Strategy of a 2-Axes Gimbal Structure Loading Device for a Stair-Climbing Delivery Robot

This paper studies an adaptive control strategy for a 2-axes gimbal structure loading device for a stair-climbing delivery robot. The properties of the loading device change depending on the luggage, and the loaded luggage cannot be specified. It is difficult to design an appropriate controller with fixed PID gains. To optimize the controller regardless of luggage condition, the adaptive control strategy is conducted by system identification and gain scheduling. The system identification estimates the parameters of the dynamic equations. The gain scheduler optimizes the PID controller using the estimated parameters. The system identification technique is based on the least-squares method. The accuracy of system identification is improved by a null-space solution. The gain scheduler consists of surface functions defined by interpolation of the optimized PID gains. The system identification techniques are verified by simulation, comparing a specific system assumed as a real system and the estimated system. The experiments using the motion platform verify the adaptive control strategy. This proposed control strategy adapts the controller to the system of the loading device.

An Improved VM Obstacle Identification Method for Reflection Road

An obstacle detection method based on VM (VIDAR and machine learning joint detection model) is proposed to improve the monocular vision system's identification accuracy. When VIDAR (Vision-IMU-based detection and range method) detects unknown obstacles in a reflective environment, the reflections of the obstacles are identified as obstacles, reducing the accuracy of obstacle identification. We proposed an obstacle detection method called improved VM to avoid this situation. The experimental results demonstrated that the improved VM could identify and eliminate unknown obstacles. Compared with more advanced detection methods, the improved VM obstacle detection method is more accurate. It can detect unknown obstacles in reflection, reflective road environments.

Real-time parameter identification of ship maneuvering response model based on nonlinear Gaussian Filter

In order to solve the problem of parameter identification of nonlinear ship motion model in ship autonomous navigation control, a real-time parameter identification method based on nonlinear Gaussian filtering algorithm and nonlinear ship response model is proposed. It is proved theoretically that the influence of parameter drift on parameter identification can be reduced by increasing the number of observers and filters, and the system identification accuracy can be improved. The validity of the proposed method is verified by parameter identification experiments based on Zig-zag motion simulation data of Mariner standard ship model. Simulation results show that compared with EKF, the nonlinear Gaussian filter algorithm can effectively improve the parameter identification accuracy and reduce the computational complexity. The application of parallel structure is helpful to improve the identification accuracy and convergence rate of nonlinear Gaussian filter algorithm.

Application of powerline noise cancellation method in correlation identification of controlled source electromagnetic method

Abstract Powerline interference in the controlled source electromagnetic method has traditionally been one of the biggest conundrums plaguing geophysicists, and its conventional denoising methods primarily include filtering and noise estimation. The filter method leaches noise at specific frequency points, which might also filter useful signals; the noise estimation method significantly eliminates interference, whereas the premise is that the noise is stable after a short time and a recorder is necessary in the field. In the present study, using the periodicity and symmetry of powerline noise, we propose a subtraction and an addition method for cancellation of the powerline noise. First, the transmitted signal is optimized so that the equivalent transmitted signal is an m sequence; then the response signal is processed by using the cancellation method; subsequently, the correlation identification is applied and finally, we solve the earth impulse response by means of the Wiener filter deconvolution method. Simulation experiments and field data tests demonstrate that the powerline noise can be well suppressed by the cancellation method proposed in the present study, so that the system identification accuracy is greatly improved. The method is simple in principle and effective in removing powerline noise, which presents a novel perspective on noise elimination for system identification.

Research on Security Verification Mechanism of Perception Layer Terminal of Power Internet of Things Based on Device Operation Fingerprint

Integrating the Internet of things technology into the power grid is a major development trend in the future, which can effectively improve the overall performance of the existing power grid. However, due to the traditional Internet of things mainly uses the form of digital password for security protection, due to the low security problems, there are great hidden dangers to the operation of the equipment. For this reason, this paper proposes a security verification mechanism for the perception layer terminal of the power Internet of things based on the device operation fingerprint. In this paper, the biometric fingerprint identification technology and Internet of things architecture are deeply studied, and the fingerprint identification algorithm process and steps are analyzed in detail, and the processing time of each step is compared. In order to improve the comprehensive performance of the security verification of the perception layer terminal of the Internet of things, according to the actual security requirements of the power Internet of things, combined with the biometric fingerprint identification technology, a new security verification system is established. In this paper, the security mechanism of RFID system is adopted, and the performance of fingerprint image filtering is improved by optimizing calculation method and simplifying algorithm. By improving the identity authentication mode and optimizing the data encryption transmission mechanism, the security protection performance of the perception layer of the power Internet of things is further improved. In the simulation test, the system identification accuracy rate of this paper reaches 99.75%, and the software implementation efficiency test results are good, which shows that the system in this paper has high security protection performance, and can meet the current security protection needs of the power Internet of things.

ELM speaker identification for limited dataset using multitaper based MFCC and PNCC features with fusion score

In current scenario, speaker recognition under noisy condition is the major challenging task in the area of speech processing. Due to noise environment there is a significant degradation in the system performance. The major aim of the proposed work is to identify the speaker’s under clean and noise background using limited dataset. In this paper, we proposed a multitaper based Mel frequency cepstral coefficients (MFCC) and power normalization cepstral coefficients (PNCC) techniques with fusion strategies. Here, we used MFCC and PNCC techniques with different multitapers to extract the desired features from the obtained speech samples. Then, cepstral mean and variance normalization (CMVN) and Feature warping (FW) are the two techniques applied to normalize the obtained features from both the techniques. Furthermore, as a system model low dimension i-vector model is used and also different fusion score strategies like mean, maximum, weighted sum, cumulative and concatenated fusion techniques are utilized. Finally extreme learning machine (ELM) is used for classification in order to increase the system identification accuracy (SIA) intern which is having a single layer feedforward neural network with less complexity and time consuming compared to other neural networks. TIMIT and SITW 2016 are the two different databases are used to evaluate the proposed system under limited data of these databases. Both clean and noisy backgrounds conditions are used to check the SIA.

An integrating genetic algorithm and modified Newton method for tracking control and vibration suppression

With the trend toward taller and more flexible building structures, a mass-damper shaking table system has been considered as means for vibration suppression to external excitation and disturbances in recent years. However, there are few researches on the control of nonlinear structure using active mass damper under earthquake excitation, especially for high-rise building. This study presents a model combining the advantages of adaptive genetic algorithm and modified Newton method is developed for system identification and vibration suppression of a building structure with an active mass damper. The genetic algorithm with adaptive reproduction, crossover, and mutation operators is to search for initial weight and bias of the neural network, while the modified Newton method, similar to BFGS algorithm, is to increase network training performance. Experimental results show that the controller performance is strongly influenced by the accuracy of system identification. The controller is also shown to be robust to variations in system parameters.

A Comparison of Three Different Methods for the Identification of Hysterically Degrading Structures Using BWBN Model

Structural control and health monitoring scheme play key roles not only in enhancing the safety and reliability of infrastructure systems when they are subjected to natural disasters, such as earthquakes, high winds and sea waves, but it also optimally minimize the life cycle cost and maximize the whole performance through the full life cycle design. In this scheme, system identification is regarded as a major technique to identifiy system states and related parameter variables, thus preventing degradation of structural or mechanical systems when unexpected disturbances occur. In this paper, three different strategies are proposed to identify general hysteretic behavior of a typical shear structure subjected to external excitations. Different case studies are presented to analyze the dynamic responses of a time varying shear structural system with the early version of Bouc-Wen-Baber-Noori (BWBN) hysteresis model. By incorporating a “Grey Box” strategy utilizing an Intelligent Parameter Varying (IPV) and Artificial Neural Network (ANN) approach, a Genetic algorithm (GA) and a Transitional Markov Chain Monte Carlo (TMCMC) based Bayesian Updating framework system identification schemes are developed to identify the hysteretic behavior of the structural system. Hysteresis characteristics, computational accuracy and algorithm efficiency are further discussed by evaluating the system identification results. Results show that IPV performs superior computational efficiency and system identification accuracy over GA and TMCMC approaches.

Evolutionary Design of a PID Controller for Two-Inertia Systems

In the vehicle development, it is important to shorten the development period of the powertrain components such as transmission, torque converter. In order to simulate real road loads of the powertrain components, it is necessary that the dynamometer of the drivetrain bench emulates the moment of inertia of a tire. To emulate the desired moment of inertia, controller parameters are calculated based on system identification in conventional scheme. In the aforementioned control scheme, high accuracy of system identification is required. This paper proposes a data-oriented control system whose controller parameters are directly calculated without system identification. In the proposed scheme, the fictitious exogenous signal is calculated using closed-loop data, and controller parameters and system parameters are simultaneously obtained. Finally, the effectiveness of the proposed scheme is verified by using a numerical example.

A Novel Neural Networks Ensemble Approach for Modeling Electrochemical Cells.

Accurate modeling of electrochemical cells is nowadays mandatory for achieving effective upgrades in the fields of energetic efficiency and sustainable mobility. Indeed, these models are often used for performing accurate State-of-Charge (SoC) estimations in energy storage systems used in microgrids or powering pure electric and hybrid cars. To this aim, a novel neural networks ensemble approach for modeling electrochemical cells is proposed in this paper. Herein, the system identification has been faced by means of a gray box technique, in which different and specialized neural networks are used for identifying the unknown internal behaviors of the cell. In particular, the a priori knowledge on the system dynamic is used for defining the network architecture. Specifically, each nonlinear function appearing in the system equations is approximated by a distinct neural network. The proposed model has been validated upon three different data sets both in terms of model accuracy and effectiveness in the SoC estimation task. The achieved performances have been compared with those of other computational intelligence approaches proposed in the literature. The results prove the effectiveness of the gray box scheme, achieving very promising performances in both the system identification accuracy and the SoC estimation task.

State-Space Adaptive Nonlinear Self-Interference Cancellation for Full-Duplex Communication

Full-duplex transmission comprises the ability to transmit and receive at the same time on the same frequency band. It allows for more efficient utilization of spectral resources, but raises the challenge of strong self-interference (SI). Cancellation of SI is generally implemented as a multi-stage approach. This work proposes a novel adaptive SI cancellation algorithm in the digital domain and a comprehensive analysis of state-of-the-art adaptive cancellation techniques. Inspired by recent progress in acoustic echo control, we introduce a composite state-space model of the nonlinear SI channel in cascade structure. We derive a SI cancellation algorithm that decouples the identification of linear and nonlinear elements of the composite state. They are estimated separately and consecutively in each adaptation cycle by a Kalman filter in DFT domain. We show that this adaptation can be supported by a-priori signal orthogonalization and decoding of the signal-of-interest (SoI). In our simulation results, we analyze the performance by evaluating residual interference, system identification accuracy and communication rate. Based on the results, we provide recommendations for system design. In case of input orthogonalization, our Kalman filter solution in cascade structure delivers best performance with low computational complexity. In this configuration, the performance lines up with that of the monolithic (parallel) Kalman filter or the recursive-least squares (RLS) algorithms. We show that the Kalman-based algorithm is superior over the RLS under time-variant conditions if the SoI is decoded and in this way the covariance information required by the Kalman filter can be provided to it.

Identification and Control of a Fuel Cell System in the Presence of Time-Varying Disturbances

For most fuel cell power systems, the control implementation is essential because it can cope with electrical loads and prevent the negative effects due to over temperature. In this work, a time-varying Hammerstein model is developed to identify a fuel cell system in the presence of time-varying disturbances since the load variations are usually time-varying and unknown. The static nonlinearity part is approximated by a series of piecewise linear functions. A moving-horizon least-squares estimator for parameter estimation is added to update the linear dynamic elements as well as identify the dynamic behavior of time-varying disturbances. The composite feedback control, which consists of the inverse function and the internal model control (IMC) structure with updated parameters, is implemented to control the stack temperature. Simulations show that the accuracy of system identification is guaranteed and the control performance is superior to the conventional model-based control.

Identification of structural systems with full characteristic matrices under single point excitation

The aim of “System Identification” is to determine modal and system properties of structural systems. This is while in “Damage Detection”, the identification of system characteristic matrices is as important as or even more important than the identification of frequency characteristics. Because of various constraints – i.e. difficulties in force excitation of structures due to their large size, geometry, and location – in practice only single excitation and partial measurement, at selected degrees of freedom, is possible. In this paper, a single dynamic load was applied to identify a structural system only along one of the degrees of freedom of the structure. Further, responses corresponding to a few degrees of freedom were also measured. To identify a system with this sort of restricted information, a new approach was introduced enabling identification of the structure׳s parameters of mass, damping and stiffness. Taking into account the significant effect of noise reduction in improving system identification accuracy levels, a noise reduction technique was also proposed. The accuracy of the method was also assessed against noise level and location of single excitation. It was shown that as noise level increases, identification errors will also increase (less than 3.5 percent). It was further observed that applying single force at the first storey of the flexural structure would yield the lowest error levels in the identification results. Later, the method׳s efficiency and precision were examined through the application of a “closed loop solution” to a six-storey flexural structure, and a four-span Pratt truss. The obtained results showed that the proposed method could act as an effective model in identification of system properties.

Dual-type sensor placement for multi-scale response reconstruction

Although various types of sensors are now available to monitor a structure, structural responses are usually recorded in a limited number of locations which are much less than the total number of degrees of freedom (DOF) of the structure. The lack of information on multi-scale responses of the structure at all its key locations may hamper subsequently the accuracy of system identification, the reliability of damage detection or the effectiveness of structural control. Therefore, multi-scale response reconstruction at the key locations of a structure based on limited measured responses is essential. A dual-type sensor placement method with the aim of the possibly best reconstruction of multi-scale structural responses is investigated experimentally in this paper in terms of a simply supported overhanging steel beam. The numbers and locations of strain gauges and displacement transducers for the possibly best reconstruction of the multi-scale responses are first determined in terms of the proposed method and the updated finite-element (FE) model of the overhanging beam. An experimental work is then designed with the simply supported overhanging beam. The determined dual-type sensors are installed on the physical beam accordingly. The structural responses from these sensors are recorded during the experiments and employed to reconstruct the multi-scale responses of the beam at all its key locations. Some of these reconstructed responses are finally compared with the measured ones to verify the proposed method. The results show that the proposed method is feasible and efficient.

Identification of Precision Positioning Stage Based on ARX Model

In order to realize the characteristics of the positioning stage, the system identification has been developed. The location data has been interpolated and normalized so as to improve the accuracy of system identification. After data preprocessing, the identification based on ARX model and the least square method has been carried out. The results indicate that the ARX model is appropriate to describe the characteristics of positioning system.

Feasibility of an Ingestible Sensor-Based System for Monitoring Adherence to Tuberculosis Therapy

Poor adherence to tuberculosis (TB) treatment hinders the individual’s recovery and threatens public health. Currently, directly observed therapy (DOT) is the standard of care; however, high sustaining costs limit its availability, creating a need for more practical adherence confirmation methods. Techniques such as video monitoring and devices to time-register the opening of pill bottles are unable to confirm actual medication ingestions. A novel approach developed by Proteus Digital Health, Inc. consists of an ingestible sensor and an on-body wearable sensor; together, they electronically confirm unique ingestions and record the date/time of the ingestion. A feasibility study using an early prototype was conducted in active TB patients to determine the system’s accuracy and safety in confirming co-ingestion of TB medications with sensors. Thirty patients completed 10 DOT visits and 1,080 co-ingestion events; the system showed 95.0% (95% CI 93.5–96.2%) positive detection accuracy, defined as the number of detected sensors divided by the number of transmission capable sensors administered. The specificity was 99.7% [95% CI 99.2–99.9%] based on three false signals recorded by receivers. The system’s identification accuracy, defined as the number of correctly identified ingestible sensors divided by the number of sensors detected, was 100%. Of 11 adverse events, four were deemed related or possibly related to the device; three mild skin rashes and one complaint of nausea. The system’s positive detection accuracy was not affected by the subjects’ Body Mass Index (p = 0.7309). Study results suggest the system is capable of correctly identifying ingestible sensors with high accuracy, poses a low risk to users, and may have high patient acceptance. The system has the potential to confirm medication specific treatment compliance on a dose-by-dose basis. When coupled with mobile technology, the system could allow wirelessly observed therapy (WOT) for monitoring TB treatment as a replacement for DOT.