Proposed Identification Algorithm Research Articles

Identification of Linear Dynamic Systems in State Sate

System identification has been growing in an engineering community over the last 60 years, and an intensive research has been published in this field. This study presents an identification method of a linear time-invariant continuous dynamic system directly from time series data. The considered system is represented in a state space form. All states are assumed to be measured. The proposed identification algorithm is demonstrated and investigated, with noise-free data and noisy data, on a MATLAB simulation environment. The results confirm that the simple procedures of the proposed algorithm give an effective and successful estimation of the system parameter matrices.

Frequency Domain Identification of Continuous-Time Hammerstein Systems With Adaptive Continuous-Time Rational Orthonormal Basis Functions

In this paper, we propose a new identification method for continuous-time Hammerstein systems. This method is based on the adaptive generalized rational orthonormal basis functions. In particular, we use the strategy in which poles of basis functions are selected one by one in terms of a criterion. By doing this, we not only take advantage of the variability of the generalized orthogonal basis functions, but also reduce the computational burden. The proposed algorithm is adaptive. Meanwhile, convergence analysis is performed with considering the sampling frequency, noise level, sampling length, and terms of basis functions. The obtained results guarantee the convergence of the whole Hammerstein system. Finally, a numerical example is presented to illustrate the usefulness of the proposed identification algorithm.

Identification of the error excitation in gear systems: A mediator algorithm between simulation and experiment

There are three primary sources of internal excitation in gear systems: time-varying mesh stiffness, meshing damping and tooth error. Compared with the first two excitations, it is more difficult to evaluate or measure the error excitation. The error excitation is usually assumed empirically or even neglected in the majority of dynamic models. To address this issue, we desired to propose a mediator algorithm between the simulation and the experiment to identify the error excitation of gear systems. Based on the second-order cyclostationarity of gear signals, a signal processing procedure is proposed to acquire the mediator signal with a high signal-to-noise ratio. The mediator signal is served as the “media” between the simulation and experiment. The error parameter is updated repeatedly in the iterative optimization until the optimal matching is found between the simulation and the experiment. Simultaneously, the pre-decomposition technique is proposed to accelerate the solution of the simulation dynamic model. The performance of the proposed identification algorithm is verified both numerically and experimentally. Moreover, noise immunity analysis shows that the proposed identification algorithm is robust to noise, damping and phase differences between the experiment and simulation. The proposed identification algorithm provides an indirect approach to estimating the error excitation when the direct measurement is unavailable.

Geometric error calibration and analysis of rotary axes on a five-axis dispensing machine

Geometric rotary axis errors are one of the main factors affecting the dispensing positioning accuracy of five-axis vision dispensing machines. Hence, the accurate detection and compensation of these error terms is an important and challenging problem. In this study, a decoupling identification method for both position-dependent geometric errors and position-independent geometric errors of a rotary dispensing table is proposed based on vision measurement technology. According to this strategy, the screw theory and exponential product formula are used to establish a kinematic model of the end-effector, followed by the definition of the mapping relationship between position-independent geometric errors and axis posture. Then, to separate the coupling error terms of the rotating axes, a step-by-step identification strategy is proposed based on the least squares method. Furthermore, a coordinate search-based error compensation algorithm is developed, which avoids the inverse singular value problem and is insensitive to the error model. Finally, the effectiveness of the proposed identification algorithm and compensation algorithm is experimentally validated.

IDENTIFICATION OF UNKNOWN PARAMETERS OF THE DYNAMIC MODEL OF MASS TRANSFER

An iterative algorithm for identifying unknown parameters of a mathematical model based on the Bayesian approach is proposed, which makes it possible to determine the most probable maximum informative estimates of these parameters. The example of the mathematical model of mass transfer dynamics shows the algorithm for finding the most probable and most informative estimate of the vector of unknown parameters, and also an analysis of the sequence of the corresponding steps is given. The results of computational experiments showed a significant dependence of the results of the calculations on the choice of the initial approximation point and slowing down the rate of convergence of the iterative process (and even its divergence) with an unsuccessful choice of the initial approximation. The validity of the obtained results is provided by analytical conclusions, the results of computational experiments, and statistical modeling. The results of computational experiments make it possible to assert that the proposed algorithm has a sufficiently high convergence for a given degree of accuracy and makes it possible to derive not only estimates of point values of mathematical model parameters based on a posteriori analysis, but also confidence intervals of these estimates. At the same time, it should be noted that the results of calculations depend significantly on the choice of the initial approximation point and the slowing of the convergence rate of the iterative process with an unsuccessful choice of the initial approximation. Analytical studies and results of calculations confirm the effectiveness of the proposed identification algorithm, which makes it possible, with the help of active, purposeful experiments, to build more accurate mathematical models. In accordance with the algorithm, a program was developed in the MatLab mathematics package and computational experiments were performed.

A Comparative of Two-Dimensional Statistical Moment Invariants Features in Formulating an Automated Probabilistic Machine Learning Identification Algorithm for Forensic Application

IBIS, ALIS, EVOFINDER, and CONDOR are the massive ballistics computerised technological machines that have typically been utilised in forensic laboratories to automatically locate similarities between images of cartridge cases and bullets. However, it imposed a long execution time and requires physical interpretation to consolidate the analysis results when employing these market-available technologies to accomplish ballistics matching tasks. Therefore, the principal objective of this study is to propose an improvised automated probabilistic machine learning identification algorithm by extracting the two-dimensional (2D) statistical moment invariants from the segmented region of interest (ROI) corresponding to the cartridge case and bullets images. To pursue this principal objective, several 2D statistical moment invariants have been compared and tested to determine the most suitable feature set applied in the proposed identification algorithm. The 2D statistical moment invariants employed include Orthogonal Legendre moments (OLM), Hu moments (HM), Tsirikolias-Mertzois moments (TMM), Pan-Keane moments (PKM), and Central Geometric moments (CGM). Moreover, the proposed identification algorithm is also tested in different scenarios, including based on the classification of strength association measurements between the extracted feature sets. The empirical results in this article revealed that the proposed identification algorithm applied with the CGM comprising the weak association classification yielded the best identification accuracy rates, which are >96.5% across all the sample sizes of the training set. These empirical results also conveyed that the superior proposed identification algorithm in this research could be developed as a mobile application for ballistics identification that can significantly reduce the time taken and conveniently perform the ballistics identification tasks.

Parameter estimation of fractional‐order Hammerstein state space system based on the extended Kalman filter

SummaryThis paper addresses the combined estimation issues of the parameters and states for fractional‐order Hammerstein state space systems with colored noises. An extended state estimator is derived by using the parameter estimates to replace the unknown system parameters in Kalman filter. The hierarchical identification principle is introduced to solve the unknown parameters of measurement noises. By introducing the forgetting factor, an extended Kalman filtering‐based hierarchical forgetting factor stochastic gradient algorithm is presented to estimate the unknown states, parameters and fractional‐order. A numerical example is respectively presented to demonstrate the feasibility of the proposed identification algorithm. It can be seen that the estimation errors are relatively small, which reflects the proposed algorithms have good identification effect.

Meta-learning of personalized thermal comfort model and fast identification of the best personalized thermal environmental conditions

The model of personalized thermal comfort can be learned via various machine learning algorithms and used to improve the individuals’ thermal comfort levels with potentially less energy consumption of HVAC systems. However, the learning of such a model typically requires a substantial number of thermal votes from the considered occupant, and the environmental conditions needed for collecting some votes may be undesired by the occupant in order to obtain a model with good generalization ability. In this paper, we propose to use a meta-learning algorithm to reduce the required number of personalized thermal votes so that a personalized thermal comfort model can be obtained with only a small number of feedback. With the learned meta-model, we derive a method based on the backpropagation of neural networks to quickly identify the best environmental and personal conditions for a specific occupant. The proposed identification algorithm has an additional advantage that the thermal comfort, indicated by the mean thermal sensation value, improves incrementally during the data collection process. We use the ASHRAE global thermal comfort database II to verify that the meta-learning algorithm can achieve an improved prediction accuracy after using 5 thermal sensation votes from an occupant to make adaptations. In addition, we show the effectiveness of the fast identification algorithm for the best personalized thermal environmental conditions with a thermal sensation generation model built from the PMV model.

Identification Modelling and Fault-Tolerant Predictive Control for Industrial Input Nonlinear Actuator System

Industrial actuator systems play an important role in mechanical manufacture, chemical production and other industrial processes. There is important theoretical research significance and engineering application value in accurately modeling and accurately controlling for an industrial actuator system with dead-zone input nonlinearity. The structure and order of the system are determined by the mechanism relationship of the system. Based on sampled data, an identification algorithm is proposed to describe the main dynamic characteristics of the system output. The convergence property of the proposed identification algorithm is also analyzed. Process faults may reduce the tracking control accuracy of the industrial actuator system. By using an intermediate observer to estimate the faults, a fault-tolerant synchronous control feedback rate is designed to compensate faults. The input dead-zone block may weaken the feedback control performance of the input signal and reduce the control precision. According to the dead-zone input nonlinearity model parameter, a compensator is introduced to transform the dead-zone function into a linear function passing through the origin of coordinates. The transformed and dynamic linear segment of the system constitute the generalized linear system. The model predictive control (MPC) strategy is designed to achieve robust and precise control by eliminating the effects of measurement noise. The results of numerical simulation and experimental test verify the superiority and merit of the modeling and fault-tolerant control strategy. The research results of this paper can provide a good reference and guidance for other complex systems in theoretical research and engineering applications.

Method for identification of sinusoidal signal parameters with variable unknown amplitude

A new method proposed for identifying the parameters of sinusoidal signal with unknown variable amplitude. The problem of estimating the parameters of sinusoidal signals is relevant in the problems of dynamic positioning and disturbance compensation, for the synthesis of control laws that take into account external disturbances. In the proposed method, the restriction on the signal amplitude is removed. In contrast to known approaches, where the amplitude must be fixed, in the proposed method the signal amplitude can be variable. To implement the proposed identification algorithm, the Jordan matrix form and delay operators are used. During parameterization, a regression model is formed containing unknown stationary parameters. To search for unknown parameters, the method of dynamic expansion of the regressor and mixing is used. The results of computer simulation demonstrate the efficiency of the proposed algorithm. The simulation results confirmed the convergence of parameter estimation to the true values. The proposed approach can be applied to a wide class of applied problems related to disturbance compensation in vibration protection systems, monitoring systems in determining the parameters of high-rise or large-span building structures, and in robotic object control systems.

Research on potential user identification and optimal planning of the multiple time scale cloud‐based location sharing energy storage

AbstractCloud energy storage is considered a promising application in future power systems. It focuses on optimally leveraging the capacity of centralized large‐scale energy storage compared with the requirements of small‐scale localized users. In this paper, to satisfy the small‐ and medium‐scale timely energy storage requirement from localized users, the concept of the cloud‐based location sharing energy storage is proposed. The modular mobile energy storage system is flexibly configured and deployed at different sites to fulfil the long‐term seasonally dynamic transformer capacity increment and short‐term daily energy arbitrage based on economic values. To optimize the overall incomes of the energy storage investment, a two‐step user potential identification algorithm is proposed to discover the most valuable users at different time scales from the regional power usage profile. Then, the number/capacity optimal planning algorithm is proposed to optimally share the mobile energy storage system among users seasonally and total profits are further increased through daily energy arbitrage at less valuable seasons. Finally, the structure and dispatching strategy of the cloud‐based location sharing energy storage management system is illustrated. Case study results prove that the proposed identification algorithm can excavate the most valuable users at different time scales and the optimal planning and operation strategy is able to guarantee the overall income benefits.

On-line Identification of Delay Attacks in Networked Servo Control

The paper discusses attacks on networked control loops by increased delay, and shows how existing round trip jitter may disguise such attacks. The attackers objective need not be de-stabilization, the paper argues that making settling time requirements fail can be sufficient. To defend against such attacks, the paper proposes the use of joint recursive prediction error identification of the round trip delay and the networked closed loop dynamics. The proposed identification algorithm allows general defense, since it is designed for delayed nonlinear dynamics in state space form. Simulations show that the method is able to detect a delay attack on a printed circuit board component mounting servo loop, long before the attack reaches full effect.

Time-domain system identification of Li-ion batteries from non-zero initial conditions

Electrochemical Impedance Spectroscopy (EIS) allows characterizing electrochemical behavior of Lithium-ion batteries. However, it requires the battery to be at a relaxed state which is quite time-consuming during data acquisition process at different State-Of-Charge (SOC) levels. A time relaxation is usually observed of about 1h at each SOC level. In the prospect to reduce the measurement time, this paper presents a time-domain identification method using non-zero initial conditions of a fractional-order equivalent circuit model (FO-ECM). A two-stage iterative algorithm is developed. It estimates at one stage the system free response, due to the past initialization, and the system forced response due to the input/output signal. It uses, at the second stage, an output error model to estimate model parameters. The algorithm is iterated until convergence. The proposed identification algorithm is applied to identify a Li-ion battery FO-ECM using experimental data.

Research on Tire–Road Parameters Estimation Algorithm for Skid-Steered Wheeled Unmanned Ground Vehicle

Skid-steered wheeled vehicles can be applied in military, agricultural, and other fields because of their flexible layout structure and strong passability. The research and application of vehicles are developing towards the direction of “intelligent” and “unmanned”. As essential parts of unmanned vehicles, the motion planning and control systems are increasingly demanding for model and road parameters. In this paper, an estimation method for tire and road parameters is proposed by combining offline and online identification. Firstly, a 3-DOF nonlinear dynamic model is established, and the interaction between tire and road is described by the Brush nonlinear tire model. Then, the horizontal and longitudinal stiffness of the tire is identified offline using the particle swarm optimization (PSO) algorithm with adaptive inertia weight. Referring to the Burckhardt adhesion coefficient formula, the extended forgetting factor recursive least-squares (EFRLS) method is applied to identify the road adhesion coefficient online. Finally, the validity of the proposed identification algorithm is verified by TruckSim simulation and real vehicle tests. Results show that the relative error of the proposed algorithm can be well controlled within 5%.

Compensation-Signal-Based Dual-Rate Operational Feedback Decoupling Control for Flotation Processes

The flotation industrial process is a strong nonlinear and coupling, multivariable cascade process with device and operational layers structrue. To address its operational control problem, this paper describes two layers nonlinear dynamic characteristics and disturbances as the previous sample unmodeled dynamics and its change rate and proposes a compensation signal based dual-rate operational feedback adaptive decoupling control approach. First, a device layer controller is designed consisting of a controller driven model, a PI controller, an unmodeled dynamics compensator (UDC) and an unmodeled dynamics change rate compensator (UDCRC). Then, the device layer closed-loop system and the operational layer system are unified in the same timescale and hence a controlled plant model is obatianed. A system identification algorithm is proposed to obtain the parameters of this model. With these parameters, an operational layer controller is designed consisting of a controller driven model, a PID controller, a feedback decoupling controller, UCD and UDCRC. Finally, convergence proofs of the proposed identification algorithm and the closed-loop system stability analysis are given, and emulation compared experiments in a hardware-in-the-loop system are used to verify the effectiveness of the proposed approach.

A VFM-based identification method for the dynamic anisotropic plasticity of sheet metals

The anisotropic plasticity of sheet metals at high strain rates is a critical factor that can influence the fabrication and service safety of engineering parts under dynamic loading conditions. The characterization of dynamic anisotropic plasticity using conventional testing methods usually suffers from the limitations resulted from the homogeneous deformation state and one-dimensional wave propagation preconditions. In this work, a highly efficient approach is proposed for the simultaneous identification of the anisotropic yielding and hardening constitutive parameters at high strain rates based on the heterogeneous impact test and the virtual fields method. First, the identification framework that combines the selected anisotropic plasticity constitutive models and the principle of virtual work concerning the dynamic kinematics term is introduced. In order to validate the proposed identification algorithm, simulated virtual impact tests were performed to provide reference parameters and unpolluted inertial acceleration/strain/strain rate field data. These data were then processed to extract the target dynamic anisotropic parameters. The results show that using the double-notched impact configuration and the proposed identification algorithm the selected anisotropic yielding and hardening parameters can be reasonably identified from a single impact test, significantly simplifying the testing procedures compared to conventional dynamic testing methods. Also, the effects of different processing parameters and influential factors on the identification accuracy have been investigated. From the sensitivity studies, the preferred ranges of the processing parameters have been determined and the robustness of the proposed method within these verified.

Identification of Solid-State Wave Gyroscopes Resonator Mechanical Errors in the Mode of Free Run-out of Standing Waves

To improve the accuracy of the output signals of solid-state wave integrating gyroscopes, various options for algorithms to identify mechanical errors of their resonators in the mode of occasionally switched mode of standing waves free run-out are considered. The measured physical parameters selected are: multi-frequency, Q factor and multi-potency, viscosity axes and stiffness axes of gyroscope resonators. Most algorithms are designed for methods of resonator mechanical errors production control and include the following stages: swinging a standing wave in the selected angular direction; circuit cut off of its active excitation; signal recording and analysis within two axes of measuring device; identification of mathematical model parameters for standing waves, recorded in their slowly changing amplitudes. Additionally, to search for stiffness axes, a technique with adjustable gyroscope resonator rotation axis is given. As a mathematical basis for the derivation and justification of the proposed identification algorithms, the necessary theoretical dependencies are given, explaining the processes of formation and processing the internal measuring signals in solid-state wave gyroscopes. As a practical testing of the most common identification technique, a laboratory study of one technological specimen of a quartz hemispherical gyroscope resonator was performed. To measure its free oscillations, a measuring system of the balancing stand was used, with measurement error up to 1% and a means of analog signals measurment, with an error of 0.05% when measuring a DC voltage of 10V. The experiment carried out showed the effective use of electrode excitation lines of standing wave for four angles {0°, 22.5°, 45°, 67.5°}. At the same time, the measurement time at each angular position was 60 seconds, and the sampling frequency of a separate measuring channel was 33,333 Hz. Herewith, each value of the wave variables was identified at 340 periods of resonant oscillations.

Identification of a Class of Precision Motion Systems with Uncertain Hysteretic Nonlinearities

In this paper, we propose a nonrecursive identification algorithm for a class of precision motion systems with uncertain hysteresis nonlinearities. This class of precision motion systems can be modeled by a Hammerstein system with a hysteresis nonlinearity. The hysteresis nonlinearity can be symmetric, asymmetric, rate-independent, or rate-dependent. The proposed identification algorithm is a two-stage algorithm that identifies the hysteresis nonlinearity and the linear system using measurements of the input and output of the Hammerstein system. The first stage of the algorithm uses a delayed impulse signal with simple least squares to identify the linear part of the Hammerstein system. The delayed impulse signal is shown to be persistently exciting of a sufficient order for the considered model structure. The second stage of the algorithm uses a sinusoidal input and the identified linear model obtained from the first stage to construct an estimate of the unknown intermediate signal, which represents the output of the hysteresis nonlinearity. Finally, a model of the hysteresis nonlinearity is obtained using the sinusoidal input and the constructed estimate of the intermediate signal. The identification algorithm is demonstrated on an experimental setup that includes a piezoelectric actuator, which exhibits a hysteresis nonlinearity and badly damped dynamics.

Идентификация квадратичного ядра уравнения Вольтерра для моделирования нелинейных динамических систем

In the last two decades, integral models have been used to describe the dynamics of stationary nonlinear systems in which the terms of the Volterra series are the kernels. The most commonly used are the linear term (the impulse transition function depends on one variable) and the quadratic term (depending on two variables). An active experiment in which a special combination of rectangular pulses is fed to the input of the system is carried out to select two of its components in the output signal of the identified system - the output of the linear "subsystem" and the output of the "quadratic" subsystem. After isolating the output of the "quadratic" subsystem, the identification of the quadratic term of the Volterra series is reduced to solving a two-dimensional integral equation of the first kind. In the literature, inversion formulas are given in which the quadratic kernel function is obtained as a result of arithmetic operations with second-order derivatives of the output signal. Differentiation of functions is an incorrectly posed problem, when small errors in the assignment of a function (measurement noise) cause large errors in derivatives (especially in second-order derivatives). The paper proposes the use of smoothing cubic splines for stable calculation of derivatives. To calculate the mixed second-order derivative, a spline with two variables is built - a smoothing bicubic spline. The main problem that arises in practice when processing the data of a real experiment is the selection of a smoothing parameter, on the value of which a smoothing error of noisy data depends. As a rule, the value of the variance of the measurement noise is unknown in the experiment. Therefore, in this work, it is proposed to use an algorithm based on the L-curve method to select the smoothing parameter in the constructed splines (especially in the bicubic one), which does not require setting the variance of the measurement noise. The proposed identification algorithm has a high computational efficiency. The performed computational experiment showed a small methodical error (about 1%) and good resistance to noise in measurements of the output signals of the identified system. To reduce the random component of the identification error, it is proposed to use post-processing with a local-spatial compound filter.

Identification of Key Nodes in a Power Grid Based on Modified PageRank Algorithm

For avoiding the occurrence of large-scale blackouts due to disconnected nodes in the power grid, a modified PageRank algorithm is proposed to identify key nodes by integrating the topological information and node type. The node betweenness index is first introduced based on complex network theory, which is modified to reflect the node topological information in the power grid. Then, according to the characteristics of different node types in the power grid, a modified PageRank algorithm is proposed to rapidly identify key nodes, which takes the generator nodes, load nodes, and contact nodes into account. IEEE 39-Bus system and IEEE 118-Bus system are used for the simulations. Simulation results showed that the network transmission efficiencies of the power grid are reduced from 64.23% to 5.62% and from 45.4% to 5.12% in the two simulation systems compared with other methods. The proposed identification algorithm improved the accuracy, and a provincial power grid simulation system in China is used to verify the feasibility and validity. The identified nodes are removed, which split the power grid according to importance index values. The proposed method in this paper is helpful to prevent the occurrence of cascading failure in the power system, and it can also be used to power systems with renewable energy sources and an AC/DC hybrid power grid.