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modeling and identification of Temperature-Dependent Hysteresis in Piezoelectric Materials Considering Parameter Sensitivity

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IEEE ACCESS 2020年 8卷 40901-40908页

作者： Liu, Yanfang Qi, Naiming Li, Zhi Harbin Inst Technol Sch Astronaut Harbin 150001 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

Hysteresis is an important nonlinearity effect in piezoelectric materials and is sensitive to temperature. This paper extends the saturated capacitor model to capture temperature-dependent hysteresis by replacing the constant parameters with functions of temperature. To obtain these functions, several sets of constant parameters are identified at different temperatures, and then each term of the parameters is fitted as a function with respect to temperature. In the curve fitting process, the parameter sensitivity is employed as a weight factor. The effectiveness of the proposed modeling and parameter determination procedure is verified with experimental data. The overall normalized root mean square error is approximately 5%, including 3% error caused by the identified parameters.

关键词： Hysteresis Capacitance Creep Piezoelectric materials Capacitors Temperature sensors Temperature Temperature-dependent hysteresis modeling and identification piezoelectric materials saturated capacitor hysteresis model

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modeling and identification of solar energy water heating system incorporating nonlinearities

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SOLAR ENERGY 2007年第5期81卷 570-580页

作者： Sanino, Luis A. Medinelli Reischel, Ricardo A. Rojas Univ Tecn Federico Santa Maria Dept Elect Valparaiso Chile

Prediction accuracy is a fundamental modeling requirement. This work explores different models for a solar domestic water heating system located near Vina del Mar, Chile, in order to improve model prediction accuracy over some existing alternatives. The main approach is semi-physical modeling, which combines phenomenological modeling and system identification. The former helps to organize in a conceptual form the available system knowledge, and the latter allows to adjust that knowledge into a particular model structure for a working system. Thus, with semi-physical modeling we take advantage of a basic property of classical identification models: they are linear-in-the-parameters, but may contain nonlinear regressors. Hence, while physical knowledge suggests nonlinear data regressors, system identification adjusts linear weighting parameters. The models proposed here incorporate nonlinearities based on physical system knowledge and they include, among other inputs and disturbances, air wind speed (v) and air relative humidity (RH), signals not usually considered in these model structures. Specifically, this work shows model predictive accuracy of storage tanks temperature for three model types: semi-physical, state-space and, a combination of semi-physical and a feedforward neural network with one hidden layer and eight neurons. The best models found here, according to prediction accuracy, are of semi-physical nature, and are obtained using stepwise regressor elimination algorithms which retain disturbances such as v and RH. Additionally, final models are validated with classical statistical tests such as AIC and correlation analysis. (c) 2007 Elsevier Ltd. All rights reserved.

关键词： flat-plate solar collector modeling and identification semi-physical modeling

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modeling and identification of the multi-axis drive system using support vector machine and granular computing

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JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS 2016年第4期39卷 498-507页

作者： Zhang, Li-Bing Wu, Ting Jiaxing Univ Coll Mech & Elect Engn Jiaxing Peoples R China

Advanced manufacturing technology requires high-precision capability in multi-axis computer numerical control (CNC) machine tools. At present, the modeling and identification for the drive system of CNC machine tools has some defects. In order to solve the problem, some interdisciplinary theories and methods, such as support vector machines, granular computing, artificial immune algorithms, and particle swarm optimization algorithms, have been used to model and identify multi-axis drive systems for CNC machine tools. An identification method using a support vector machine, based on granular computing, is presented to identify a multi-axis servo drive system model for improving the precision of model identification, and an immune particle swarm optimization algorithm, based on crossover and mutation functions, is proposed to optimize the structure parameters of the support vector machine based on granular computing. The proposed identification method was evaluated by experiments using the multi-axis servo drive system. The experimental results showed that the proposed approach is capable of improving modeling and identification precision.

关键词： modeling and identification support vector machine granular computing drive system

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Comprehensive approach to modeling and identification of a two-axis piezoelectric fast steering mirror system based on multi-component analysis and synthesis

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2019年 127卷 50-67页

作者： Wang, Geng Wang, Ying Zhou, Hong Bai, Fuzhong Chen, Guoqiang Ma, Junjin Henan Polytech Univ Sch Mech & Power Engn Jiaozuo 454003 Henan Peoples R China Chinese Acad Sci Inst Opt & Elect Lab Adapt Opt Chengdu 610209 Sichuan Peoples R China Chinese Acad Sci Key Lab Adapt Opt Chengdu 610209 Sichuan Peoples R China Inner Mongolia Univ Technol Coll Mech Engn Hohhot 010051 Peoples R China

In the application of accurate beam pointing, an important requirement for precise modeling and identification of the complex piezoelectric fast steering mirror (PFSM) system is still a critical task. A comprehensive model is proposed for the two-axis PFSM system through synthetical consideration on each physical component. The modeling precision of the system is greatly improved because the five main influencing factors are systematically considered. With the testing data, a specially designed identification methodology is presented by analyzing the influence of each component on the output and their interaction on different time scales. The parameter identification is finally transformed into an optimization problem. The identification result is evaluated by comparing the measured data with simulated outputs under different excitation conditions. The experimental results confirm the validity and correctness of the modeling and identification approaches. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： modeling and identification Piezoelectric fast steering mirror system Multi-component analysis Cross coupling Comprehensive model

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Comparative study of modeling and identification of the pneumatic artificial muscle (PAM) manipulator using recurrent neural networks

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2008年第7期22卷 1287-1298页

作者： Ahn, Kyoung Kwan Anh, Ho Pham Huy Univ Ulsan Sch Mech & Automot Engn Ulsan South Korea

The paper deals with the PAM manipulator modeling and identification based on autoregressive recurrent neural networks. For the first time, the most powerful types of neurat-network-based nonlinear autoregressive models, namely, NNARMAX, NNOE and NNARX models, will be applied comparatively to the PAM manipulator identification. Furthermore, the evaluation of different nonlinear neural network auto-regressive models of the PAM manipulator with different number of neurons in hidden layer is completely discussed. On this basis, the merits of each identified model of the highly nonlinear PAM manipulator have been analyzed and compared. The results show that the nonlinear NNARX model yields better performance and higher accuracy than the other nonlinear NNARMAX and NNOE model schemes. These results can be applied to model and identify not only the PAM manipulator but also to control other nonlinear and time-varying industrial systems.

关键词： pneumatic artificial muscle (PAM) manipulator autoregressive recurrent neural networks NNARMAX model NNARX model NNOE model modeling and identification

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Inverse Double NARX Fuzzy modeling for System identification

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2010年第1期15卷 136-148页

作者： Ahn, Kyoung Kwan Anh, Ho Pham Huy Univ Ulsan Sch Mech & Automot Engn Ulsan 680749 South Korea

In this paper, a novel inverse double nonlinear autoregressive with exogenous input (NARX) fuzzy model is applied to simultaneously model and identify both joints of the prototype two-axis pneumatic artificial muscle (PAM) robot arm's inverse dynamic model. Highly nonlinear features of both joints of the nonlinear manipulator system are identified by the proposed inverse double NARX fuzzy (IDNF) model based on experimental input-output training data. The modified genetic algorithm (GA) optimally generates the appropriate fuzzy if-then rules to perfectly characterize the dynamic features of the two-axis PAM manipulator system. The evaluation of different IDNF models with various ARX model structures will be discussed. For the first time, the nonlinear IDNF model of the two-axis PAM robot arm is investigated. The results show that the nonlinear IDNF model that is trained by GA performs better and has a higher accuracy than the conventional inverse fuzzy model.

关键词： Dynamic system genetic algorithm (GA) inverse double nonlinear autoregressive with exogenous input (NARX) fuzzy (IDNF) model modeling and identification two-axis pneumatic artificial muscle (PAM) robot arm

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Heterogeneous Mixtures of Dictionary Functions to Approximate Subspace Invariance in Koopman Operators: Why Deep Koopman Operators Works

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JOURNAL OF NONLINEAR SCIENCE 2025年第4期35卷 1-52页

作者： Johnson, Charles A. Balakrishnan, Shara Yeung, Enoch Univ Calif Santa Barbara Dept Mech Engn Santa Barbara CA 93106 USA Mithrl Inc 44 Montgomery St San Francisco CA 94104 USA

Koopman operators model nonlinear dynamics as a linear dynamic system acting on a nonlinear function as the state. This nonstandard state is often called a Koopman observable and is usually approximated numerically by a superposition of functions drawn from a dictionary. In a widely used algorithm, extended dynamic mode decomposition (EDMD), the dictionary functions are drawn from a fixed class of functions. Deep learning combined with EDMD has been used to learn novel dictionary functions in an algorithm called deep dynamic mode decomposition (deepDMD). The learned representation both (1) accurately models and (2) scales well with the dimension of the original nonlinear system. In this paper, we analyze the learned dictionaries from deepDMD and explore the theoretical basis for their strong performance. We explore State-Inclusive Logistic Lifting (SILL) dictionary functions to approximate Koopman observables. Error analysis of these dictionary functions show they satisfy a property of subspace approximation, which we define as uniform finite approximate closure. Typically, a Koopman dictionary's nonlinear functions are homogeneous. In this paper, we discover that structured mixing of heterogeneous dictionary functions drawn from different classes of nonlinear functions achieve the same accuracy and dimensional scaling as the deep-learning-based deepDMD algorithm Yeung et al. ( In: 2019 American Control Conference (ACC), 2019). We specifically show this by building a heterogeneous dictionary comprised of SILL functions and conjunctive radial basis functions (RBFs). This mixed dictionary achieves similar accuracy and dimensional scaling to deepDMD with an order of magnitude reduction in parameters, while maintaining geometric interpretability. These results strengthen the viability of dictionary-based Koopman models to solving high-dimensional nonlinear learning problems.

关键词： Koopman operator Deep dynamic mode decomposition identification methods Subspace approximation Invariant subspaces Nonlinear system identification Semigroup and operator theory Neural networks modeling and identification

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identification and modeling of Co-Rhythmic Genes from Micro-array Time Series Data

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IFAC Proceedings Volumes 2008年第2期41卷 9695-9700页

作者： Wenxue Wang Bijoy K. Ghosh Texas Tech University Department of Mathematics and Statistics Lubbock TX 79409 USA

‘Circadian Rhythm’ is a biological phenomenon observed in a large number of organisms ranging from unicellular bacteria to human beings. In this paper, transcriptome data from Cyanothece, a photosynthetic cyanobacteria, has been analyzed for the purpose of discovering genes whose expressions are rhythmically close (co-rhythmic). Subsequently we study if these rhythms can be modeled, up to phase, using a cascade of three phase oscillators. One of the phase oscillator in the network is derived from the model of a ‘limit cycle oscillator’ using KaiC protein (the master clock). We conclude that ‘Circadian Rhythms in Cyanothece transcriptome data can be dynamically modeled up to phase using a single master clock derived from limit cycle oscillator using KaiC protein cascaded with a pair of interconnected phase oscillators'. Biologically substrates of the phase oscillators are presently unknown.

关键词： modeling and identification Bioinformatics Dynamics and Control System Biology Circadian Oscillation Network

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modeling of disturbance torque in an aerostatic bearings-based nano-satellite simulator

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Journal of Systems Engineering and Electronics 2018年第3期29卷 618-624页

作者： LIU Yanfang HUO Mingying QI Naiming Department of Aerospace Engineering Harbin Institute of Technology

The disturbance torque of aerostatic bearings is in the same order of the reaction wheel, which causes difficulty in evaluation of the designed attitude control strategy of a nano-satellite based on the aerostatic bearing. Two approaches are proposed to model the disturbance torque. Firstly, the gravity induced moment,the vortex torque, and the damping moment are modeled separately. However, the vortex torque and the damping moment are coupled with each other as both of them are caused by the viscosity. In the second approach, the coupling effect is considered. A nano-satellite is constructed based on aerostatic bearing. The time history of the free rotation rate from an initial speed is measured by the gyro, which is further used to calculate the rotation angle and acceleration. The static vortex torque is measured via the removable micro-torque measurement system. Based on these data, the model parameters are identified and modeling errors are presented. Results show that the second model is more *** root mean squire error is less than 0.5×10^（-4） N·m and the relative error of the static vortex torque is 0.16%.

关键词： aerostatic bearing disturbance torque vortex torque spacecraft simulator modeling and identification

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Optimal Dosing and Sizing Optimization for a Ground-Vehicle Diesel-Engine Two-Cell Selective Catalytic Reduction System

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2016年第6期65卷 4740-4751页

作者： Zhang, Hui Wang, Junmin Wang, Yue-Yun Beihang Univ Sch Transportat Sci & Engn Beijing 100191 Peoples R China Ohio State Univ Dept Mech & Aerosp Engn Columbus OH 43210 USA Gen Motors Global Res & Dev Ctr Prop Syst Res Lab Warren MI 48090 USA

In this paper, we investigate the cycle-based sizing optimization for diesel-engine two-cell selective catalytic reduction ( SCR) systems. In modeling the SCR system, it is generally assumed that the states inside the catalyst can are homogeneous to simplify the modeling work. However, the actual states are not homogeneous, and the modeling error becomes large if the catalyst length is long. To reduce the modeling error, the states are assumed piecewise homogeneous, i. e., the catalyst is sliced into multicells and the states for each cell are assumed homogeneous. In this paper, we focus on the two-cell SCR systems where the two cells are connected in series and where the chemical reaction rates are assumed identical for the two cells. Considering the main chemical reactions and assuming that each cell is a continuous stirred tank reactor, a nonlinear system model with six states is obtained. To simultaneously reduce the tailpipe NOx and ammonia slip, it is quite interesting to study the optimal control of the urea dosing such that the NOx emissions can be maximally converted and where the ammonia slip is constrained. A dynamic programming ( DP) algorithm that has a tolerable computational load is proposed. Since the whole catalyst is sliced into two cells, the next motivation is how to divide the catalyst such that the NOx conversion efficiency can be maximized while meeting the tailpipe ammonia constraint. The best sizing ratio of the SCR system in our application for the US06 test cycle is found to be 60%-40%.

关键词： Dynamic programming (DP) emission control modeling and identification selective catalytic reduction (SCR) sizing optimization

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