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Developing variable moving window PLS models: Using case of NOx emission prediction of coal-fired power plants

FUEL 2021年 296卷 120441-120441页

作者： Li, Zhe Lee, Yi-Shan Chen, Junghui Qian, Yongwu Yangzhou Univ Coll Elect Energy & Power Engn Yangzhou 225127 Jiangsu Peoples R China Chung Yuan Christian Univ Dept Chem Engn Taoyuan 32023 Taiwan Jiangsu Guoxin Jingjiang Power Generat Co Ltd Jingjiang City 214513 Jiangsu Peoples R China

The efficiency of selective catalytic reduction denitrification heavily depends on the amount of ammonia injection, which is determined by the emission of nitrogen oxides (NOx) in flue gas. To accurately predict NOx emission of the coal-fired boiler with strong variable correlations, nonlinear and time-varying characteristics for determining the amount of injected ammonia, a soft sensor modeling method based on the moving window partial least squares (MWPLS) and locally weighted regression, referred to as variable exponentially weighted MWPLS (VEW-MWPLS), is proposed. It adjusts the window size in disguised form by automatically strengthen or weaken the information in the selected window according to the prediction errors of the current model. In this work, with the added new sample and the removed old sample, a series of rank-1 modification formulations are used to calculate both, the means and standard deviations among the input and output variables and the covariance and cross-covariance matrices so that a quick and adaptive update can be done for the PLS model. The numerical results have demonstrated that VEW-MWPLS algorithm can reduce the mean and standard deviation of prediction errors by one order of magnitude, and reduce the computational complexity by two orders of magnitude when being compared with other algorithms. Thus the proposed VEW-MWPLS is more suitable for predicting NOx emission online.

关键词： Exponentially weighted moving window NOx emission PLS Selective catalytic reduction variable forgetting factor

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Adaptive Notch Filter for Piezo-Actuated Nanopositioning System via Position and Online Estimate Dual-Mode

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MICROMACHINES 2021年第12期12卷 1525-1525页

作者： Huang, Chengsi Li, Hongcheng Guangdong Univ Technol Sch Electromech Engn Guangzhou 510006 Peoples R China

Due to the excellent advantages of high speed, high precision, and driving force, piezoelectric actuators nanopositioning systems have been widely used in various micro/nanomachining fields. However, the inherent resonance dynamic of the nanopositioning system generated by the flexure-hinge greatly deteriorates the positioning performance and limits the closed-loop bandwidth. Even worse, the notch filter for eliminating the effect of resonance does not work due to the varying resonant frequency resulting from the external disturbance or mass load. To this end, an adaptive notch filter for piezo-actuated nanopositioning system via position and online estimate dual-mode (POEDM) has been proposed in this paper, which can estimate the varying resonant frequency in real-time and suppress the resonance to improve the closed-loop bandwidth. First, a novel variable forgetting factor recursive least squares (VFF-RLS) algorithm for estimating resonant frequency online is presented, which is robust to the noise and provides the performances of both fast tracking and stability. Then, a POEDM method is proposed to achieve the online identification of the resonant frequency in the presence of noise and disturbance. Finally, a series of validation simulations are carried out, and the results indicate that, the frequency of input signal and the bandwidth have been achieved up to 12.5% and 87.5% of the first resonant frequency, respectively.

关键词： online system identification adaptive notch filter variable forgetting factor recursive least squares algorithm piezoelectric actuator nanopositioning

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Adaptive Predictive Control of a data center cooling unit

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CONTROL ENGINEERING PRACTICE 2021年 107卷 104674-104674页

作者： Martinez-Garcia, Fernando Badawy, Ghada Kheradmandi, Masoud Down, Douglas G. McMaster Univ Dept Comp & Software Hamilton ON L8S 4L8 Canada McMaster Univ Comp Infrastruct Res Ctr Hamilton ON L8P 0A1 Canada

As server densities increase to support the rising demand for high density computing, traditional room cooling infrastructure is struggling to keep up. Rack-mounted cooling units are increasingly being deployed in data centers. These cooling units are located closer to the heat sources (the servers) which allow them to cool more efficiently than traditional cooling infrastructure. In this work, a Multi-Input Single-Output (MISO) Adaptive Predictive Controller (APC) for rack-mounted cooling units is investigated and implemented using a low-cost general-purpose microcontroller. The proposed APC is implemented using Weighted Recursive Least Squares (WRLS) and a sub-optimal but fast algorithm based on the General Predictive Controller (GPC) approach. These are combined with a variable forgetting factor and variable prediction horizon algorithms. In addition, methods are proposed to handle stability issues arising due to practical hardware limitations. The controller is implemented on a real single rack system and challenges for practical implementation are addressed and illustrated. The proposed APC is also compared (via simulation) to a standard APC with equivalent complexity and a split-range PI controller. The results show that the proposed controller outperforms both the standard APC and the split-range PI controllers with respect to Mean Squared Error (MSE).

关键词： Adaptive Predictive Controller variable prediction horizon variable forgetting factor Data center Temperature control

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Adaptive thermal error prediction for CNC machine tool spindle using online measurement and an improved recursive least square algorithm

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CASE STUDIES IN THERMAL ENGINEERING 2024年 56卷

作者： Wei, Xinyuan Ye, Honghan Wang, Gao Hu, Weidong Anhui Univ Technol Sch Elect & Informat Engn Maanshan 230009 Peoples R China Univ Wisconsin Coll Letters & Sci Sch Comp Data & Informat Dept Stat Madison WI 53705 USA Anhui Prov Key Lab Special Heavy Load Robot Maanshan 243032 Peoples R China

Establishing models for predicting and compensating for spindle thermal errors is cost-effective and necessary to improve the accuracy of machine tools for smart manufacturing. However, the prediction performance of existing methods deteriorates significantly with dynamic working conditions of machine tools because training from static conditions leads to the inability to adapt to dynamic conditions. Therefore, an adaptive thermal error modeling method using online measurement and an improved recursive least square algorithm is proposed to fill this research gap, which updates the thermal error model adaptively to ensure that dynamic working conditions are learned in real time. Particularly, Spearman's rank correlation coefficient method is first adopted for temperature -sensitive point selection to capture the nonlinear relationship between temperature and thermal error variables. Furthermore, a variable -forgetting factor -based recursive least square (VFF-RLS) algorithm is proposed to improve the prediction performance, in which the proposed variable forgetting factor is adaptively updated according to real-time thermal error data collected by online measurement. The experimental results showed that the proposed VFFRLS method can maintain a high prediction accuracy of 1.75 mu m and robustness of 0.16 mu m on both constant and dynamic working conditions. The effectiveness of the VFF-RLS method is validated by verification experiments.

关键词： CNC machine tools Thermal error modeling Adaptability Recursive least square algorithm variable forgetting factor

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Non-linear adaptive control of a heat exchanger

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INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 2001年第8期15卷 883-906页

作者： Fink, A Nelles, O Fischer, M Isermann, R TH Darmstadt Inst Automat Control Lab Control Syst & Proc Automat D-64283 Darmstadt Germany

In this contribution, two methods for adaptation of non-linear adaptive controllers are presented and compared, namely the data-driven and the knowledge-based adaptation. A dynamic Takagi-Sugeno fuzzy model is utilized to model the non-linear process behaviour. Based on this model, a non-linear predictive controller is designed to control the process. In the presence of time-variant process behaviour and changing unmodelled disturbances, high control performance can be achieved by performing an on-line adaptation of the fuzzy model. First, a local weighted recursive least-squares algorithm is used for adaptation. It exploits the local linearity of the Takagi-Sugeno fuzzy model. In the second approach, process knowledge that is obtained from theoretical insights is utilized to design a knowledge-based adaptation strategy. Both approaches are compared and their effectiveness and real-world applicability are demonstrated by application to temperature control of a heat exchanger. Copyright (C) 2001 John Wiley Sons, Ltd.

关键词： adaptive control non-linear control predictive control learning system Takagi-Sugeno fuzzy model system identification on-line adaptation recursive least squares (RLS) supervision variable forgetting factor knowledge-based adaptation heat exchanger

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Recursive identification of inertia tensor parameters of space solar power satellite based on distributed placement of attitude sensors

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AEROSPACE SCIENCE AND TECHNOLOGY 2022年第0期130卷

作者： Ni, Zhiyu Wu, Chenchen Wu, Shunan Shenyang Aerosp Univ Coll Aerosp Engn Shenyang Peoples R China Chinese Acad Sci Shenyang Inst Automat Shenyang Peoples R China Sun Yat sen Univ Sch Aeronaut & Astronaut Shenzhen Peoples R China

This study addresses the issue of recursive identification of the inertia tensor parameters for a space solar power satellite with planar configuration based on a distributed placement of its attitude sensors. Different from the centralized configuration of the attitude sensors in a traditional rigid spacecraft, multiple attitude sensors are used to reduce the impact of the vibration signals of the large flexible structure on the measured attitude signals and improve the identification accuracy. By establishing an attitude-vibration dynamic model of the system, achieving optimal placement of multiple attitude sensors is investigated and a corresponding optimization criterion is developed. Subsequently, based on the least squares theory, the variable forgetting factor is derived by minimizing the system mean square deviation. Moreover, an improved recursive least squares method is developed to estimate the system inertia tensor parameters using the coupled least squares estimation. Numerical simulations are conducted, and based on the attitude output signals obtained from the optimal sensor placement results, the inertia tensor parameters are identified using the developed algorithm. The results demonstrate that the structural flexibility significantly affects the identification accuracy of the inertia tensor parameters, and the distributed placement of the multiple attitude sensors reduces this influence to a certain extent. Compared to a single group of attitude sensors, the ten groups of sensors for identifying the inertia tensor parameters reduce the absolute and relative values of the average relative error by approximately 3.3%-5.4% and 36.5%-55.6%, respectively. In addition, the identification results reveal that the absolute value of the average relative error is decreased by approximately 2.8%-3.5% on using the proposed recursive algorithm compared to that obtained using the conventional recursive least squares method when the signal-to-noise-ratio is 10 dB.

关键词： Inertial parameter identification Optimal sensor placement Space solar power satellite Recursive least squares method variable forgetting factor

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Milling chatter detection with WPD and power entropy for Ti-6Al-4V thin-walled parts based on multi-source signals fusion

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2022年 177卷 1页

作者： Hao, Yanpeng Zhu, Lida Yan, Boling Qin, Shaoqing Cui, Dayu Lu, Hao Northeastern Univ Sch Mech Engn & Automation Shenyang 110819 Peoples R China

Unstable cutting has a relatively large negative impact on the machining quality and efficiency of thin-walled parts. It can easily cause severe vibration of milling systems, resulting in poor surface roughness of workpieces. Although some promising signal processing methods, such as wavelet packet decomposition (WPD), have been applied to process nonlinear signals, few studies have paid attention to determining a wavelet basis and the number of decomposition layers in WPD. Parameters of WPD play a crucial role and they are often empirically determined. In this paper, an adaptive denoising model based on WPD and recursive least squares with a variable forgetting factor (RLSVFF) is firstly established, and the influence of the forgetting factor in the model on signal denoising are investigated. Then, a novel chatter detection method based on multi-source signals fusion using WPD and power entropy is presented. On this basis, an automated selection method based on a margin indicator and power is proposed for applications to WPD. And the influence of different parameters of WPD on a margin indicator and power are investigated. In order to improve the efficiency of different levels of signal acquisition, a method based on a stability lobe diagram (SLD) is used to design experimental parameters. Compared with tradi-tional denoising models and chatter detection methods based on a single signal, simulation and experimental results show that the adaptive denoising model and chatter detection method based on multi-source signals fusion proposed in this paper can more reliably detect the occurrence of early chatter and different levels of chatter.

关键词： Milling chatter detection Wavelet packet decomposition Power entropy Multi source signals Adaptive denoising variable forgetting factor

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State of charge estimation for lithium-ion battery based on improved online parameters identification and adaptive square root unscented Kalman filter

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JOURNAL OF ENERGY STORAGE 2024年 77卷

作者： Wang, Juntao Song, Jifeng Li, Yuanlong Ren, Tao Yang, Zhengye North China Elect Power Univ Sch New Energy Beijing 102206 Peoples R China North China Elect Power Univ Inst Energy Power Innovat Beijing 102206 Peoples R China

Accurate state of charge estimation is crucial for the safe and efficient operation of lithium-ion batteries. The Kalman filtering algorithm has been widely used in state-of-charge (SOC) estimation. To solve the problem of filter divergence and sensitivity to noise, the joint SOC estimation method is proposed to achieve accurate and robust estimation of SOC, which is composed of improved variable forgetting factor recursive least square (VFFRLS) and adaptive square root unscented Kalman filter (ASRUKF). The improved VFFRLS method uses the mean square value of the multi-step voltage residual instead of the traditional one-step voltage residual to correct the forgetting factor, and the ASRUKF improves the accuracy and stability of estimation by introducing the square root method and the adaptive method. Estimations under three typical working conditions are quantitatively studied, the experimental results show that the proposed method has good accuracy and stability. The maximum mean absolute error (MAE) is 0.0061, and the corresponding root mean square error (RMSE) is 0.0090. Furthermore, the experimental verification after adding offset errors to measured data shows that the proposed method can still track the reference SOC with satisfactory accuracy in the range of voltage offset error less than +/- 0.5 % and current offset error less than +/- 3 %, both for the constant offset error and the random error, and the SOC error is less than 2.5 %.

关键词： Lithium-ion battery State of charge Multi-step voltage residuals variable forgetting factor Adaptive square root unscented Kalman filter

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A novel variable step size least mean square method for adaptive micro-vibration control

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JOURNAL OF VIBRATION AND CONTROL 2022年第19-20期28卷 2925-2939页

作者： Fang, Yubin Zhu, Xiaojin Zhang, Xiaobing Nanjing Univ Sci & Technol Sch Intelligent Mfg 200 Xiaolingwei St Nanjing 210094 Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai Peoples R China Nanjing Univ Sci & Technol Sch Energy & Power Engn Nanjing Peoples R China

The variable step size least mean square algorithm has been suggested since a number of years as a potential solution for improving the performance of least mean square algorithm. In this article, the variable step size least mean square algorithm is classified by the techniques which are used to update step size. Unfortunately, for variable step size least mean square algorithms with forgetting factor, a constant forgetting factor may slow down its convergence speed. For this reason, a variable forgetting factor method for variable step size least mean square is proposed in this article. First, the convergence analysis of a new variable step size least mean square algorithm with the variable forgetting factor is provided. Then, simulations expose the characteristics of this variable forgetting factor method. Last, a micro-vibration control experimental system is established. Four typical variable step size least mean square algorithms and their variable forgetting factor modified version are verified through experiments. The results show that the proposed variable forgetting factor method can effectively improve convergence speed while maintaining the steady-state performance of the variable step size least mean square algorithm with the constant forgetting factor.

关键词： variable step size least mean square variable forgetting factor adaptive filter system identification active vibration control

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Iterative Identification of Output Error Model with Time Delay

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IFAC-PapersOnLine 2015年第8期48卷 888-893页

作者： Shijian Dong Tao Liu Fengwei Chen Institute of Advanced Control Technology Dalian University of Technology Dalian 116024 China

In this paper an iterative identification method is proposed to simultaneously estimate the parameters of an output error model together with a delay parameter for industrial processes with time delay, to facilitate model based control design. An extended observation vector is constructed to establish a negative-gradient based iterative identification algorithm. An auxiliary model is established to estimate the unknown noise-free output of the system for consistent estimation. Moreover, a variable forgetting factor (VFF) is introduced to enhance the convergence rate and identification accuracy against measurement noise. The convergence of the proposed algorithm is analyzed with a strict proof. An illustrative example is shown to demonstrate the effectiveness of the proposed identification method.

关键词： Time delay output error model auxiliary model variable forgetting factor convergence

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