On-line Identification Method Research Articles

Adaptive control for refrigeration via online identification

Vapor compression refrigeration systems (VCRS) occupy a crucial position in modern society and in the field of thermal sciences. However, the operation of VCRS is subjected to both external disturbances (dynamic-changing environment) and inherent system characteristics (coupling or nonlinear features), leading to issues like reduced refrigeration efficiency and significant fluctuations in cooling capacity. To address these challenges and enhance the adaptability of VCRS in dynamically changing environments, this study establishes a dynamic simulation model for VCRS based on the Switched Moving-Boundary method. The impact of external environmental disturbances on refrigeration performance is investigated, and continuous online identification methods are employed to elucidate its internal coupling characteristics and nonlinear features. The adaptive temperature control method is introduced, benefiting from the developed recursive least squares method with a forgetting factor for online identification, achieving precise model identification which facilities the real-time parameters tuning of adaptive controller. The results indicate that the hybrid paradigm of online identification and adaptive control algorithm not only effectively handles various disturbances but also reduces overshoot and IAE by 2-3 orders of magnitude compared to traditional PID controllers. Adaptive PID control maintains overshoot in the 10-4 order of magnitude and IAE in the 10-5 order of magnitude.

Online Mechanical Resonance Frequency Identification Method Based on an Improved Second-Order Generalized Integrator—Frequency-Locked Loop

To address the issue of mechanical resonance frequency detection in dual-inertia servo systems, this paper proposes an online identification method for mechanical resonance frequency using a low-pass filter and cascaded second-order generalized integrator—frequency-locked loop (LPF-CSOGI-FLL). Initially, the cascaded second-order generalized integrator—frequency-locked loop (CSOGI-FLL) is employed to eliminate the interference of direct current (DC) bias in resonance frequency identification. From a dual-stage structural perspective, the first second-order generalized integrator (SOGI-FLL) acts as a band-pass pre-filter to extract the mechanical resonance signal from the signal to be tested. The second SOGI-FLL generates a signal with equal amplitude and frequency to the mechanical resonance and obtains the frequency of the resonance signal through the frequency-locked loop. Subsequently, a low-pass filter (LPF) is applied to the frequency feedback loop of the second-stage SOGI-FLL, effectively reducing the oscillation of the estimated frequency. Finally, combining the CSOGI-FLL with an LPF forms a novel structure, namely, LPF-CSOGI-FLL. The results demonstrate that the proposed method significantly improves the detection accuracy of mechanical resonance frequency under various conditions. Compared to traditional offline techniques, this method overcomes the impact of resonance frequency drift and enhances system stability.

Adaptive Dynamic Model-Based Path Following Controller Design for an Unmanned Surface Vessel

Abstract The effective design of a path following controller for Unmanned Surface Vessels (USVs) under uncertain influences induced by various factors such as environmental disturbances is a challenging task. In this study, we propose to ful?lling this task through taking benefits from an online parameter identi?cation technique, the discrete-time sliding mode control (DSMC) method, and the improved line of sight (LOS) algorithm. The Particle Swarm Optimization algorithm (PSO) was adopted to provide initial settings for the straightforward online identification method, i.e., the Forgetting Factor Recursive Least Square method (FFRLS). In order to handle the time-varying sideslip angle of a ship that exists in reality due to environmental disturbances, a multi-model course control scheme is proposed to improve the control performance. For this control scheme, a flexible selection mechanism in-between a heading angle or a course angle tracking controller based on the DSMC method is designed. A solution to ?xing the tracking deviation problem of the LOS guidance law is investigated for which the gradient descent method is introduced. A series of experiments are carried out at sea with a USV called Orca to verify and validate the proposed hybrid path following approach. The results showed that tracking errors mainly induced by environmental disturbances existed but the maximum magnitude among them was small enough and remaining within the acceptable range especially from the marine engineering point of view. These results, to a high degree, validated the robustness and precision of the proposed controller.

Rules-reduced fuzzy neural network-based learning control for multiple constraints robots using online identification and compensation methods

This paper presents a novel fuzzy-learning adaptive control approach for uncertain robotic systems with input dead zone and output saturation constraints. To address the input dead zone and output saturation and improve the control stability of the robot, an internal model compensation method was proposed, which utilizes online identification of an arctanh function to approximate the output saturation of actuators. The paper also introduces an adaptive learning control system based on a rules-reduced fuzzy neural network (RRFNN) algorithm, which considers the dead zone and output saturation function to enhance control performance, and an adaptive law driven by approximation mistakes is employed to handle multiple constraints. Furthermore, the controller utilizes the integral Lyapunov stability theorem and RRFNN to design the fuzzy-learning adaptive control law, ensuring global convergence and stability of the control system. Extensive simulations and experiments on a robotic manipulator are conducted to verify the feasibility of the proposed control method. Without the proposed method, the robot’s joint tracking error exceeds 0.5 rad, while with the proposed controller, it is less than 0.05 rad and does not violate output saturation constraints. Thus, the proposed method can realize the desired performance and overcome multiple constraints.

Online identification and extraction method of regional large-scale adjustable load-aggregation characteristics

This article introduces the concept of load aggregation, which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems. The online identification method is a computer-involved approach for data collection, processing, and system identification, commonly used for adaptive control and prediction. This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration, aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods. The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics, economic efficiency, and comfort. The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes, the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57, indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term. Overall, the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.

Online identification and feed-forward compensation of nonlinear friction in servo system based on RBF neural network model

In this paper, an online identification and compensation method of nonlinear friction based on radial basis function (RBF) neural network model is proposed for the influence of nonlinear friction on machining accuracy in the low speed process of servo feed system of CNC machine tools. First, a three-layer single-input-output RBF neural network model is established for describing the nonlinear friction of servo feeding system. Second, the neural network online learning algorithm is improved based on adaptive gain, which improves the stability and accuracy of the algorithm. Finally, experiments were carried out on a three-axis milling machine to compensate the friction in the servo feed system in real time based on the online identification results. The results show that the method can effectively improve the online identification accuracy and convergence rate, and effectively improved the low-speed performance of the servo feed system.

Variable Pressure Difference Control Method for Chilled Water System Based on the Identification of the Most Unfavorable Thermodynamic Loop

A variable pressure differential fuzzy control method is proposed based on the online identification method for key parameters and the fuzzy subset inference fuzzy control method of the chilled water system network model. Firstly, a phase plane fuzzy identification method is proposed for the most unfavorable thermal loop. The study focuses on analyzing the trend of room temperature deviation and deviation change in different quadrants in the phase plane. Furthermore, we establish a chilled water pipe network model that recalculates flow variation in both the main pipe and each branch pipe section to eliminate the most unfavorable thermal loop. Finally, the test platform for the fan coil variable flow air conditioning water system was designed and constructed to meet the requirements of energy-saving regulation. Additionally, the network monitoring system for the test platform was completed. The calibration and debugging results demonstrate that the monitoring error is within ±5.0%, ensuring precise control of room temperature at the end of the branch within ±0.5 °C. Results demonstrate that our novel method exhibits superior stability in room temperature control compared to traditional linear variable pressure differential set point controls while achieving energy saving ranging from 4.7% to 6.5%.

Parameters online identification‐based data‐driven backstepping control of hypersonic vehicles

SummaryThe control problem of the hypersonic vehicles is studied in this article. A new control approach is presented. This approach consists of a data‐driven dynamic model established by multiple neural networks, an online identification method for system parameters, and a basic backstepping controller. The implementation of this approach requires a dynamic model and system parameters including the moment of inertia and aerodynamic parameters of the hypersonic vehicles. The parameter identification problem is regarded as a dynamic optimization process. The loss function is designed by the Lagrange criterion, and its constraints are determined by the physical and the numerical values. In the case of model mutation, the system parameters identified online are used as the nominal values of the output of the neural network in the data‐driven model to adjust the controller through its gradient descent. Simulation comparisons are given to show the effectiveness of the proposed data‐driven approach.

An Improved On-Line Recursive Subspace Identification Method Based on Principal Component Analysis and Sliding Window for Polymerization

Polymerization products are indispensable for our daily life, and the relevant modeling process plays a vital role in improving product quality. However, the model identification of the related process is a difficult point in industry due multivariate, nonlinear and time-varying characteristics. As for the conventional offline subspace identification methods, the identification accuracy may be not satisfying. To handle such a problem, an enhanced on-line recursive subspace identification method is presented on the basis of principal component analysis and sliding window (RSIMPCA-SW) in this paper to obtain the state space model for polymerization. In the proposed on-line subspace identification approach, the initial L-factor is acquired by the LQ decomposition of the sampled historical data, firstly, and then it is updated recursively through the bona fide method after the new data have been handled by the sliding window rule. Subsequently, principal component analysis (PCA) is introduced to calculate the extended observation matrix, and finally the on-line model parameters are extracted. Compared with the traditional subspace schemes, smaller computation complexity and higher identification precision are anticipated in the proposed method. A case study on the modeling of the ethylene polymerization verifies the effectiveness of the developed approach, in which the related statistical indexes of the obtained identification model are better.

An Online Feedback-Based Local Method for Topology Identification and Var/Volt Control in Radial Wind Farms

An Online Feedback-Based Local Method for Topology Identification and Var/Volt Control in Radial Wind Farms

Accurate tool wear and breakage monitoring method for milling process based on vision and laser sensor fusion

Accurate online acquisition of tool wear degradation indicators is an important prerequisite for tool wear monitoring and tool remaining life prediction. The tool degradation process is usually accompanied by the flank face of cutting tool wear and blade breakage, however, the existing degradation indicators only consider the flank face wear value, but not the tool breakage value, resulting in the lack of accuracy of degradation indicators. To this end, an online identification method of tool degradation indicators based on the fusion of image sensors and laser displacement sensors is proposed, which adopts the VGG16-UNet network to identify the wear value in the image and obtains the tool breakage value based on the time series data of the laser displacement sensor. Finally, the tool wear and breakage degradation label containing wear and breakage values is established. Compared to manual measurements, the absolute average error is within 15 µms for cutter face damage values and within 3 µms for cutter face wear values.

Online identification methods for low-order wheel polygons coupled with axle cracks

Wheel polygons pose a threat to the safety of train operations due to wheel fatigue, necessitating online identification. The online identification of wheel polygons predominantly relies on the axle box vertical nx (n-multiple integer of axle rotation frequency) signals generated by wheel polygons. The low-order vertical nx signals are easy coupling to other faults like axle cracks, leading to errors in wheel polygon identification. A signal decoupling method is crucial for removing the crack components from the vertical nx signal to obtain more accurate identification results for low-order wheel polygons. Employing principles of signal superposition for different faults, this article proposes a method to decouple the wheel polygon nx signal from the vertical nx signal by subtracting the crack-related portion. This method has been validated through an ABAQUS + SIMPACK online rigid-flexible coupling model, indicating that it significantly improves the accuracy of wheel polygon identification. The 1x amplitude of the decoupled signal decreased by 60.78%, and the 2x amplitude decreased by 55.79%.

Transient angle stability analysis of maximum Lyapunov exponent based on the key branch response information

Based on real-time access to response information on wide-area tributaries, in order to assess the system stability more quickly, this paper combines the simplified branch transient transmission capability (sBTTC) index and the largest Lyapunov exponent (MLE). An online identification method for transient power angle stability is proposed. Firstly, the method is based on a two-machine system to study the consistency of the power angle difference between the two ends of the critical branch and the change of the phase difference of the fleet. Secondly, the transient power angle stability problem is transformed into the MLE analysis problem according to the change rule of sBTTC index of the key branch. Then, by combining the characteristics of MLE curve and sBTTC index curve, the transient power angle stability criterion is given. Finally, the proposed method is verified and analysed by simulation examples. The method has strong industrial applicability because it does not need system model, the information source is easy to measure, it has strong anti-noise ability, and it overcomes the defect of the traditional method that needs to set a fixed symbolic observation window.

A Multi-Faults Online Detection and Identification Method for Concentrated Winding PMSM using Search Coil Array

A Multi-Faults Online Detection and Identification Method for Concentrated Winding PMSM using Search Coil Array

An Adaptive PID Control System for the Attitude and Altitude Control of a Quadcopter

Abstract In adaptive model-based control systems, determining the appropriate controller gain is a complex and time-consuming task due to noise and external disturbances. Changes in the controller parameters were assumed to be dependent on the quadcopter mass, which was the process variable. A nonlinear model of the plant was used to identify the mass, employing the weighted recursive least squares (WRLS) method for online identification. The identification and control processes involved filtration using differential filters, which provided appropriate derivatives of signals. Proportional integral derivative (PID) controller tuning was performed using the Gauss–Newton optimisation procedure on the plant. Differential filters played a crucial role in all the developed control systems by significantly reducing measurement noise. The results showed that the performance of classical PID controllers can be improved by using differential filters and gain scheduling. The control and identification algorithms were implemented in an National Instruments (NI) myRIO-1900 controller. The nonlinear model of the plant was built based on Newton’s equations.

Nondestructive and rapid detection of foreign materials in wolfberry by hyperspectral imaging combing with chemometrics

The foreign materials in wolfberries, such as wolfberry flowers, leaves, branches and other plant fruits, can affect the overall grade and economic value of wolfberry. Identifying and removing these foreign materials is a crucial step after wolfberry picking. This study aims to provide a method of hyperspectral imaging combing with chemometrics (HSIcC) for identification of foreign materials in wolfberries. Wolfberry flower, leaf, branch and dogwood are taken as the foreign materials in this paper. Hyperspectral images of wolfberries and four foreign materials are collected in wavelength range of 370–1060 nm. And the analytical model is established. Different spectral preprocessing algorithms, including vector normalization (VN), the savitzky–golay (SG) method, the first-derivative, the second-derivative, and standard normal variable (SNV) transformation, are used and compared. Principal component analysis (PCA) and competitive adaptive reweighted sampling (CARS) are employed as data dimension reduction methods. K-Nearest Neighbor (KNN), Random Forest (RF) and Support Vector Machine (SVM) are utilized for modeling. The optimal results are obtained by “SG+CARS +KNN” model, with accuracy for training, validation, and testing set of 100%, 100% and 100%, respectively. The results show that HSIcC can provide a rapid and nondestructive on-line detection method for foreign materials identification of wolfberries.

Online Network-Based Identification and its Application in Satellite Attitude Control Systems

Most satellite attitude control models are built analytically, which requires a clear understanding of the kinematic equations of the satellites and the various disturbance models to deal with the interferences and uncertainties in the space environment. This paper studies a system identification method to build a system model for satellite attitude control. The satellite model is based on a neural network model, i.e., the RBF-ARX model, which requires only observation data. Unlike the existing off-line identification methods for the RBF-ARX model, this paper proposes an on-line identification method that can constantly correct the model according to the newest data. To reduce the computational complexity of identifying the satellite model, a decomposition scheme is also developed. The computational efficiency of the proposed method is analyzed. Finally, the efficacy of the proposed identification method is demonstrated by applying it to two satellite attitude control systems.

On-line identification method of railway tunnels and slopes based on Brillouin scattering signal

Wide coverage and complex environment along the railway make a huge demand for calibration in critical areas such as tunnels or slopes. In this paper, an on-line identification method of railway tunnels and slopes based on Brillouin scattering signal is proposed. By monitoring Brillouin scattering signal of a redundant communication optical fiber, tunnels and slopes can be recognized by feature analysis. Experimental results indicate that the on-line identification method of railway tunnels and slopes based on Brillouin scattering signal is effective, invulnerable and accurate.

An online identification method for establishing a microgrid equivalent model based on the hybrid particle swarm optimization butterfly algorithm

AbstractThe frequency response model of a microgrid system is an indispensable tool for designing secondary frequency controllers and analyzing system frequency stability. Owing to the complexity of a microgrid system structure, time‐varying operation state, and difficulty in obtaining the inverter parameters, it is difficult to model the microgrid system. To address the abovementioned problems, this study constructs the equivalent model of a microgrid system based on mechanism analysis and then identifies the equivalent model parameters using an online identification method based on a hybrid particle swarm optimization butterfly algorithm according to the sampled operation data to obtain the specific equivalent model of a microgrid for the controller. This modeling method has strong adaptability and can yield accurate identification modeling when the operational structure of the microgrid changes.

Spotting priming-active compounds using parsley cell cultures in microtiter plates

BackgroundConventional crop protection has major drawbacks, such as developing pest and pathogen insensitivity to pesticides and low environmental compatibility. Therefore, alternative crop protection strategies are needed. One promising approach treats crops with chemical compounds that induce the primed state of enhanced defense. However, identifying priming compounds is often tedious as it requires offline sampling and analysis. High throughput screening methods for the analysis of priming-active compounds have great potential to simplify the search for such compounds. One established method to identify priming makes use of parsley cell cultures. This method relies on measurement of fluorescence of furanocoumarins in the final sample. This study demonstrates for the first time the online measurement of furanocoumarins in microtiter plates. As not all plants produce fluorescence molecules as immune response, a signal, which is not restricted to a specific plant is required, to extend online screening methods to other plant cell cultures. It was shown that the breathing activity of primed parsley cell cultures increases, compared to unprimed parsley cell cultures. The breathing activity can by monitored online. Therefore, online identification of priming-inducing compounds by recording breathing activity represents a promising, straight-forward and highly informative approach. However, so far breathing has been recorded in shake flasks which suffer from low throughput. For industrial application we here report a high-throughput, online identification method for identifying priming-inducing chemistry.ResultsThis study describes the development of a high-throughput screening system that enables identifying and analyzing the impact of defense priming-inducing compounds in microtiter plates. This screening system relies on the breathing activity of parsley cell cultures. The validity of measuring the breathing activity in microtiter plates to drawing conclusions regarding priming-inducing activity was demonstrated. Furthermore, for the first time, the fluorescence of the priming-active reference compound salicylic acid and of furanocoumarins were simultaneously monitored online. Dose and time studies with salicylic acid-treated parsley cell suspensions revealed a wide range of possible addition times and concentrations that cause priming. The online fluorescence measuring method was further confirmed with three additional compounds with known priming-causing activity.ConclusionsDetermining the OTR, fluorescence of the priming-active chemical compound SA and of furanocoumarins in parsley suspension cultures in MTPs by online measurement is a powerful and high-throughput tool to study possible priming compounds. It allows an in-depth screening for priming compounds and a better understanding of the priming process induced by a given substance. Evaluation of priming phenomena via OTR should also be applicable to cell suspensions of other plant species and varieties and allow screening for priming-inducing chemical compounds in intact plants. These online fluorescence methods to measure the breathing activity, furanocoumarin and SA have the potential to accelerate the search for new priming compounds and promote priming as a promising, eco-friendly crop protection strategy.