Nonlinear Time-varying Characteristics Research Articles

Thermal Characteristics of Spindle System Based on the Comprehensive Effect of Multiple Nonlinear Time-Varying Factors

The thermal characteristics of the spindle system for CNC machine tools are influenced by multiple factors which are nonlinear and time-varying. In this paper, a nonlinear time-varying thermal characteristics solving model for the spindle system was established based on the numerical solution method. Through theoretical deduction and data fitting, mathematical models of nonlinear time-varying factors including the friction torque generated by lubricants, convective heat transfer coefficient, and coolant and ambient temperature are constructed. The temperature and displacement of the spindle system at each time step are solved by considering the comprehensive effect of multiple nonlinear time-varying factors. And the actual temperature and axial deformation data of the spindle system are obtained through thermal characteristics detection experiments. By comparing solution results affected by multiple nonlinear time-varying factors and by non time-varying factors with experimental data, it can be concluded that the nonlinear time-varying thermal characteristics model has advantages in reflecting the trend of numerical changes and the accuracy of result solving over a method considering non time-varying factors and the solution values of temperature affected by multiple nonlinear time-varying factors are almost consistent with detection values and the relative errors are all within ±3%. The relative error of axial deformation between the value solved by the model and the detection value is close to −1%. This conclusion demonstrates the rationality and accuracy of the thermal characteristics solving model and the construction of nonlinear time-varying factors. This study is of great significance for exploring the thermal characteristics of the spindle system and improving CNC machine tool performance in depth.

Dynamic modeling and nonlinear analysis for lateral–torsional coupling vibration in an unbalanced rotor system

Imbalance is a common problem in rotating machinery. In addition to the unbalanced force, lateral–torsional coupling vibration can also be induced by the mass imbalance. However, the related nonlinear behaviors and coupling mechanism are still not well understood, especially when lateral-angular motion is incorporated in a model. In this paper, a model for coupling lateral and torsional vibrations of an unbalanced rotor is developed by Lagrangian dynamics, in which the coupling of lateral-angular and torsional motions is first considered. Based on the model, the lateral responses, torsional responses, coupling excitation characteristics and coupling mechanisms are investigated in detail. The results show that mass imbalance can lead to nonlinear time-varying characteristics in the dynamic equations. When the imbalance is small, the lateral vibration can be degraded to a traditional imbalance problem. Meantime, very small transient torsional vibration with torsional natural frequency ft in frequency domain can be observed at the beginning. The coupling excitation applying on the torsional DOF caused by the lateral synchronous whirling is demonstrated to behave the feature of static torque, consequently, there actually exists no steady-state torsional vibration in small unbalanced rotor. Under largely unbalanced conditions, both resonant and unstable behavior can be observed in the lateral and torsional responses. The combined resonance is induced by the lateral translational–torsional coupling, while the instability is caused by lateral angular–torsional coupling. By increasing the lateral damping, the instability phenomenon can be suppressed, and the lateral amplitude in the resonance region can be effectively reduced.

Time-Domain Identification Method Based on Data-Driven Intelligent Correction of Aerodynamic Parameters of Fixed-Wing UAV

In order to overcome the influence of complex environmental disturbance factors such as nonlinear time-varying characteristics on the dynamic control performance of small fixed-wing UAVs, the nonlinear expression relationship of neural networks (NNs) is combined with the recursive least squares (RLSs) identification algorithm. This paper proposes a hybrid aerodynamic parameter identification method based on NN-RLS offline network training and online learning correction. The simulation results show that compared with the real value of the identification value obtained by this algorithm, the residual error of the moment coefficient is reduced by 69%, and the residual error of the force coefficient is reduced by 89%. Under the same identification accuracy, the identification time is shortened from the original 0.1 s to 0.01 s. Compared with traditional identification algorithms, better estimation results can be obtained. By using this algorithm to continuously update the NN model and iterate repeatedly, iterative learning for complex dynamic models can be realized, providing support for the optimization of UAV control schemes.

Active RCD adaptive voltage balancing control strategy for high-voltage uncontrolled rectifier valve

The voltage balance of the series diodes is the premise to ensure the stable and reliable operation of the high voltage uncontrolled rectifier valve. When traditional RC and RCD voltage balancing methods are applied to high voltage uncontrolled rectifier valves in far-reaching offshore wind farms, the complex and changeable operating environment poses a challenge to the anti-interference of the balance effect of the snubber circuit. In this paper, an active RCD adaptive voltage balancing control strategy with strong anti-interference ability is proposed based on the establishment of the active RCD voltage balancing model of the high voltage uncontrolled rectifier valve. The parameter tuning method of the active RCD snubber circuit is given, and the auto-PID control strategy of the active RCD snubber circuit is proposed according to its nonlinear time-varying characteristics. The simulation example verifies that the active RCD adaptive voltage balancing strategy of the high voltage uncontrolled rectifier valve has strong anti-interference performance, which provides a reference for the design of the high voltage uncontrolled rectifier valve voltage sharing system in the far-reaching offshore wind farm.

Multivariate linear-regression variable parameter spatio-temporal zoning model for temperature prediction in steel rolling reheating furnace

In the steel industry, temperature control and prediction of billets during heating in steel rolling reheating furnace (SRRF) can guarantee product performance and save energy consumption. However, the nonlinear time-varying (NTV) and distributed of SRRF are a great challenge for the accurate temperature control of billets. It is important to predict and regulate the temperature of the SRRF online and to identify the causal variables that may lead to temperature variations. In this paper, a new multivariate linear regression variable parameter spatio-temporal (MLR-VPST) zoning model approach is proposed to predict the temperature. Firstly, a MLR-VPST zoning model method is proposed to predict the temperature of the SRRF, which can handle the distribution characteristics of the temperature field and the NTV characteristics. Secondly, a new least squares matrix block (LSMB) algorithm is proposed to obtain batch parameter solutions, which can improve the dynamic and temperature prediction accuracy. Thirdly, a zoning model approach is proposed to describe the degree of fitting of the SRRF model in each zone, and the linear correlation is gradually increasing from the reheating zone to the soaking zone. It makes the temperature of the soaking zone more accurate. Simulation results of real-time data from steel company show that it meets the requirements of the rolling process.

Graph convolutional network soft sensor for process quality prediction

The nonlinear time-varying characteristics of the process industry can be modeled using numerous data-driven soft sensor methods. However, the intrinsic relationships among the variables, especially the localized spatial–temporal correlations that shed light on model behavior, have received little attention. In this study, a soft sensor based on a graph convolutional network is constructed by introducing the concept of graph to process modeling. The focus is on obtaining localized spatial–temporal correlations that aid in comprehending the intricate interactions among the variables included in the soft sensor. The model is trained by considering the regularization terms and it learns distinctive localized spatial–temporal correlations in an end-to-end manner. Furthermore, long-term dependence is established via temporal convolution. Thus, both the localized spatial–temporal correlations and time-series properties are captured. The feasibility of the proposed soft sensor is illustrated using two fermentation processes. The localized spatial–temporal correlations of this case study are visualized, and they demonstrate that the soft sensor is not a black-box model; instead, it is consistent with process knowledge.

Dynamic-Memory Event-Based Asynchronous Attack Detection Filtering for a Class Of Nonlinear Cyber-Physical Systems.

In this article, we endeavor to address the problem of asynchronous attack detection for a class of nonlinear cyber-physical system (CPS) under the dynamic-memory event-triggered transmission protocol (DMETP). Malicious attacks under consideration are composed of the parameter-dependent false data-injection (FDI) attacks and the multichannel deception attacks with the time delay. Meanwhile, we attempt to employ the T-S fuzzy singular Semi-Markov jump linear parameter-varying (SS-MJLPV) systems to describe the stochastic jump characteristics and nonlinear time-varying characteristics of CPSs. Then, the DMETP is proposed for the first time, which can not only relieve the bandwidth pressure but also obtain the key released packets at the crest or trough of the curve. By augmenting the states of the original system and the asynchronous attacks detection filter, the issue of attacks detection is converted into an auxiliary dissipative filtering for the CPS. Moreover, sufficient conditions for the existence of the attacks detection filter are obtained. Finally, the effectiveness of the proposed scheme is verified via the networked truck-trailer reversing system.

Multi-fault diagnosis for series-connected lithium-ion battery pack with reconstruction-based contribution based on parallel PCA-KPCA

Various faults of the lithium-ion battery threaten the safety and performance of the battery system. The early faults are difficult to detect and isolate owing to unobvious abnormality and the nonlinear time-varying characteristics of the battery. Herein, a multi-fault diagnosis strategy is proposed that focuses on detecting and isolating different types of faults, and estimating fault waveforms of the battery, including inconsistency evaluation, virtual connection fault, and external short circuit. First, the principal component analysis (PCA) model of the battery is established and the contribution is employed to detect the abnormity in the battery pack. Once the fault is detected, the parallel kernel principal component analysis (KPCA) technology is adopted to reconstruct the fault waveform of the battery parameters, including ohmic resistance, terminal voltage, and open-circuit voltage. These parameters are jointly taken as fault indexes improving the reliability of fault diagnosis. Finally, the proposed method is verified using amounts of tested data of eight cells in series. The results indicate that the contribution-based PCA method can accurately detect the fault. Furthermore, the reconstruction-based parallel PCA-KPCA can accurately estimate the fault waveform of the faulty battery, which helps investigate the fault degree and causes.

Study on a Discharge Circuit Prediction Model of High-Voltage Electro-Pulse Boring Based on Bayesian Fusion

It is necessary to develop new drilling and breaking technology for hard rock construction. However, the process of high-voltage electro-pulse (HVEP) rock-breaking is complex, and the selection of electro-pulse boring (EPB) process parameters lacks a theoretical basis. Firstly, the RLC model, TV-RLC model, and TV-CRLC model are established based on the characteristics of the HVEP circuit to predict the EPB dynamic discharge curve. Secondly, the parameters are identified by the Particle Swarm Optimization Genetic Algorithm (PSO-GA). Finally, due to the nonlinear and complex time-varying characteristics of the discharge circuit, the discharge circuit prediction models based on Bayesian fusion and current residual normalization fusion method are proposed, and the optimal weight of these three models is determined. Compared with the single models for EPB current prediction, the average relative error reduction rates based on Bayesian fusion and current residual normalization fusion methods are 25.5% and 9.5%, respectively. In this paper, the discharge circuit prediction model based on Bayesian fusion is established, which improves the prediction accuracy and reliability of the model, and it guides the selection of process parameters and the design of pulse power supply and electrode bits.

State of Charge Dual Estimation of a Li-ion Battery Based on Variable Forgetting Factor Recursive Least Square and Multi-Innovation Unscented Kalman Filter Algorithm

Battery management is the key technical link for electric vehicles. A good battery management system can realize the balanced charge and discharge of batteries, reducing the capacity degradation and the loss of health caused by battery overcharge and discharge, which all depend on the real-time and accurate estimation of the battery’s state of charge (SOC). However, the battery’s SOC has highly complex nonlinear time-varying characteristics related to the complex chemical and physical state and dynamic environmental conditions, which are difficult to measure directly, and this has become a difficulty in design and research. According to the characteristics of ternary lithium-ion batteries of electric vehicles, a battery SOC dual estimation algorithm based on the Variable Forgetting Factor Recursive Least Square (VFFRLS) and Multi-Innovation Unscented Kalman Filter (MIUKF) is proposed in this paper. The VFFRLS algorithm is used to estimate battery model parameters, and the MIUKF algorithm is used to estimate the battery’s SOC in real time. The two algorithms are coupled to update battery model parameters and estimate the SOC. The experiment results show that the algorithm has high accuracy and stability.

Reference Frame Unification of IMU-Based Joint Angle Estimation: The Experimental Investigation and a Novel Method.

Inertial measurement unit (IMU)-based joint angle estimation is an increasingly mature technique that has a broad range of applications in clinics, biomechanics and robotics. However, the deviations of different IMUs’ reference frames, referring to IMUs’ individual orientations estimating errors, is still a challenge for improving the angle estimation accuracy due to conceptual confusion, relatively simple metrics and the lack of systematical investigation. In this paper, we clarify the determination of reference frame unification, experimentally study the time-varying characteristics of reference frames’ deviations and accordingly propose a novel method with a comprehensive metric to unify reference frames. To be specific, we firstly define the reference frame unification (RFU) and distinguish it with drift correction that has always been confused with the term RFU. Secondly, we design a mechanical gimbal-based experiment to study the deviations, where sensor-to-body alignment and rotation-caused differences of orientations are excluded. Thirdly, based on the findings of the experiment, we propose a novel method to utilize the consistency of the joint axis under the hinge-joint constraint, gravity acceleration and local magnetic field to comprehensively unify reference frames, which meets the nonlinear time-varying characteristics of the deviations. The results on ten human subjects reveal the feasibility of our proposed method and the improvement from previous methods. This work contributes to a relatively new perspective of considering and improving the accuracy of IMU-based joint angle estimation.

Non-linear Impact of China's Economic Growth on the Health of Residents-An Empirical Study Based on TVP-FAVAR Model.

This paper uses the 74-dimensional macroeconomic data set from 2005 to 2017 as a sample to construct a TVP-FAVAR model to empirically study the impact of China's economic growth on the health of residents. The study found that China's economic growth has an impact on the health of residents and is transformed into changes in the macroeconomic environment that exhibit non-linear time-varying characteristics. Specifically: (1) During the period of steady economic growth, China's economic growth has caused a significant increase in population mortality rate, infectious disease mortality rate, medical expenses of residents, traffic accident rate, neonatal mortality rate, and tumor mortality rate; (2) During the financial crisis, the positive impact of economic growth on population mortality rate, infectious disease mortality rate, traffic accident rate, and neonatal mortality rate was significantly reduced, while the medical expenses of residents, tumor mortality rate, and cardiovascular morbidity rate and the incidence of mental illness rate has a more obvious inhibitory effect; (3) In the period of sustained economic downturn, the positive impact of economic growth on overall population mortality rate, infectious disease incidence rate, traffic accident rate, and neonatal mortality rate continues to decrease, still negatively affecting the incidence of mental illness rate and cardiovascular morbidity rate. In this paper, we suggested that the Chinese government further promote the transformation of the economic growth model in the new normal economic stage, increase public health fiscal expenditure, and realize an economic development evaluation system that is oriented toward improving the health of residents.

Adaptive filtering-based recursive identification for time-varying Wiener output-error systems with unknown noise statistics

In area of control, model-based robust identification is rare, and studies in presence of unknown noise statistics are especially seldom. The robust estimation problem for time-varying Wiener output-error systems is considered in this paper. An adaptive filtering-based recursive identification scheme is proposed to distinguish nonlinear time-varying characteristics in complex noise environments. Firstly, a virtual equivalent state space model is constructed to achieve adaptive Kalman filtering. In filter design, a weighted noise estimator based on Sage-Husa principle is introduced, and is sensitive to noise changes. Secondly, the state estimates obtained by filters are used to form the unknown intermediate variables in information vectors. Then, a recursive estimation method based on multiple iterations is developed, and the convergence of identification is confirmed by martingale hyperconvergence theorem. Finally, the numerical simulation results verify the theoretical findings.

Monitoring models for base flow effect and daily variation of dam seepage elements considering time lag effect

Affected by external environmental factors and evolution of dam performance, dam seepage behavior shows nonlinear time-varying characteristics. In this study, to predict and evaluate the long-term development trend and short-term fluctuation of the dam seepage behavior, two monitoring models were developed, one for the base flow effect and one for daily variation of dam seepage elements. In the first model, to avoid the influence of the time lag effect on the evaluation of seepage variation with the time effect component of seepage elements, the base values of the seepage element and the reservoir water level were extracted using the wavelet multi-resolution analysis method, and the time effect component was separated by the established base flow effect monitoring model. For the development of the daily variation monitoring model for dam seepage elements, all the previous factors, of which the measured time series prior to the dam seepage element monitoring time may have certain influence on the monitored results, were considered. Those factors that were positively correlated with the analyzed seepage element were initially considered to be the support vector machine (SVM) model input factors, and then the SVM kernel function-based sensitivity analysis was performed to optimize the input factor set and establish the optimized daily variation SVM model. The efficiency and rationality of the two models were verified by case studies of the water level of two piezometric tubes buried under the slope of a concrete gravity dam. Sensitivity analysis of the optimized SVM model shows that the influences of the daily variation of the upstream reservoir water level and rainfall on the daily variation of piezometric tube water level are processes subject to normal distribution.

Design of Adaptive Subspace Predictive Controller for Stable Operation

For a class of process which had characteristics of nonlinear time-varying, dynamic characteristic varing significantly and can not be accurately modeled mechanism, this paper proposed a adaptive subspace predictive controller for stable operation. The method compared the prediction error to update prediction model online, and adjuste the length of the scroll window automatically according to the feedback error, which enhanced the adaptability for nonlinear time-varying characteristics, and strengthened its ability to inhibit unpredictable interference. Finally, simulated to verify the effectiveness of the method.

Nonlinear time-varying vibration system identification using parametric time–frequency transform with spline kernel

In real-life applications, mechanical vibration systems are not linear time invariant. Time-varying pattern arise, for instance, during deployment, aging, deformation, load variation, etc. In the absence of a complete physics-based description of a system, system identification (SI) is able to obtain the missed information of nonstationary pattern for the unknown system. The SI will face difficulty when the time-varying system involves the nonlinearity, which makes the system solution have both the nonlinear and nonstationary time–frequency patterns. This paper aimed to identify the system with linear and nonlinear time-varying characteristics using parametric time–frequency transform with spline kernel, which is known for offering great energy concentration in time–frequency domain and allows the accurate extraction of model feature. The efficacy of the proposed method is demonstrated on SDOF systems comprised of three types of nonlinear time-varying stiffness, including time-varying periodic modulation of stiffness, time-varying piecewise modulation of nonlinear stiffness and time-varying periodic modulation of nonlinear stiffness. Comparisons with the conventional time–frequency methods and the Hilbert transform-based SI validated that the proposed method is more robust in characterizing the nonlinear time-varying stiffness of the system with the presence of noise.

항공기 종 제어를 위한 Interval Type-2 퍼지논리 제어시스템

The flight control of aircraft, which has nonlinear time-varying dynamic characteristics depending on the various and unexpected external conditions, can be performed on two motions: longitudinal motion and lateral motion. In the longitudinal motion control of aircraft, pitch and trust are major control parameters and roll and yaw are control ones in the lateral motion control. Until now, a number of efficient and reliable control schemes that can guarantee the stability and maneuverability of the aircraft have been developed. Recently, the intelligent flight control scheme, which differs from the conventional control strategy requiring the various and complicate procedures such as the wind tunnel and environmental experiments, has attracted attention. In this paper, an intelligent longitudinal control scheme has been proposed utilizing Interval Type-2 fuzzy logic which can be recognized as a representative intelligent control methodology. The results will be verified through computer simulation with a F-4 jet fighter.

Study on the Control Strategy of Polydimethylsiloxanols Continuous Production System in One-Step Synthesis

According to the reaction principle of polydimethylsiloxanols produced by one-Step synthesis, the mathematical model is fitted by analyzing the result of test. This is a complex system with nonlinear time-Varying coupling characteristics, considering the requirements of dynamic and static performance indicators ,easily controlled features and simple structure, the combine control strategy of conventional PID and Fuzzy PID is used. The results show that the control precision of conversion rate is high and the robustness and adaptive ability of the system are both improved. This lays theoretical foundation to produce the polydimethylsiloxanols continuously in large scale in the future.

The PVC Stripping Process Predictive Control Based on the Implicit Algorithm

According to the nonlinear and parameters time-varying characteristics of stripper temperature control system, the PVC stripping process Generalized Predictive Control based on implicit algorithm is proposed. Firstly, supporting vector machine is adopted to dynamically modelize for the stripper temperature; Secondly, combining with real-time model linearized of nonlinear model, a predictive model is linearized for real-time online correction. Then, the implicit algorithm is used for optimal control law. Finally, the simulation results show that the algorithm has excellent validity and robustness of temperature control of the stripper.

Hybrid adaptive fuzzy and neural network controller for the molten steel level control in strip casting processes

The molten steel level control of strip casting process has the properties of nonlinear uncertainty and time-varying characteristics, hence, it is difficult to establish an accurate process model for designing a model-based controller to monitor the strip quality. This study develops a hybrid model-free adaptive fuzzy and neural network controller (HAFNC) which combines an adaptive rule with fuzzy and neural network control to overcome the difficulty. The proposed control strategy has online learning ability for responding to the system's nonlinear and time-varying behaviors during the molten steel level control. Since this model-free controller has simple control structure and small number of control parameters, it is easy to implement. Numerical results based on semi-experimental system dynamic model and parameters are executed to show the control performance of the proposed intelligent controller.