Modified state prediction algorithm based on UKF

The state prediction based on the unscented Kalman filter （UKF） for nonlinear stochastic discrete-time systems with linear measurement equation is investigated. Predicting future states by using the information of available measurements is an effective method to solve time delay problems. It not only helps the system operator to perform security analysis, but also allows more time for operator to take better decision in case of emergency. In addition, predictive state can make the system implement real-time monitoring and achieve good robustness. UKF has been popular in state prediction because of its advantages in handling nonlinear systems. However, the accuracy of prediction degrades notably once a filter uses a much longer future prediction. A confidence interval （Ci） is proposed to overcome the problem. The advantages of CI are that it provides the information about states coverage, which is useful for treatment-plan evaluation, and it can be directly used to specify the margin to accommodate prediction errors. Meanwhile, the CI of prediction errors can be used to correct the predictive state, and thereby it improves the prediction accuracy. Simulations are provided to demonstrate the effectiveness of the theoretical results.

PREDICTION OF THE PHASE BEHAVIOR OF GAS CONDENSATES USING PATEL-TEJA EQUATION OF STATE AND GAMMA DISTRIBUTION FUNCTION

The three-parameter Petal-Teja equation of state coupled with a characterization proceduref0r C7+-fraction based on gamma distribution function was employed to predict the phase behaviorof eight gas condensates.The lumping of the subdivided single carbon number（SCN）hydrocarbons inthe plus-fraction and the choice of empirical correlations for calculating the critical properties andacentric factor of SCN hydrocarbons were discussed.

Bottleneck Identification and Prediction of Wafer Fabrication Systems in Transient States

According to theory of constraints( TOCs), the performance of a complex manufacturing system,such as a wafer fabrication system,is mainly determined by its bottleneck machine.A method of the identification and prediction of the bottleneck machine was proposed in transient states of a system. Firstly,the bottleneck index was formulated based on the workloads and the variability in wafer fabrication systems. Secondly, main factors causing the variability and their influences on the bottleneck machine in transient states of the system were analyzed and discussed. An effective bottleneck identification and prediction model was presented,which incorporated the variability and queuing theory,and took machine breakdowns and setups into considerations.Finally,the proposed bottleneck prediction method was verified by simulation experiments. Results indicate that the proposed bottleneck prediction method is feasible and effective.

Research on information technology of state monitoring and fault prediction for mechatronics system

The safety and reliability of mechatronics systems,particularly the high-end,large and key mechatronics equipment in service,can strongly influence on production efficiency,personnel safety,resources and environment.Based on the demands of development of modern industries and technologies such as international industry 4.0,Made-in-China 2025 and Internet + and so on,this paper started from revealing the regularity of evolution of running state of equipment and the methods of signal processing of low signal noise ratio,proposed the key information technology of state monitoring and earlyfault-warning for equipment,put forward the typical technical line and major technical content,introduced the application of the technology to realize modern predictive maintenance of equipment and introduced the development of relevant safety monitoring instruments.The technology will play an important role in ensuring the safety of equipment in service,preventing accidents and realizing scientific maintenance.

A Modification of α in SRK Equation of State and Vapor-Liquid Equilibria Prediction

Based on results of saturated vapor pressures of pure substances calculated by SRK equation of state, the factor α in attractive pressure term was modified. Vapor-liquid equilibria of mixtures were calculated by original and modified SRK equation of state combined with MHV1 mixing rule and UNIFAC model, respectively. For 1447 saturated pressure points of 37 substance including alkanes; organics containing chlorine, fluorine, and oxygen; in-organic gases and water, the original SRK equation of state predicted pressure with an average deviation of 2.521% and modified one 1.673%. Binary vapor-liquid equilibria of alcohols containing mixtures and water containing mixtures also indicated that the SRK equation of state with the modified α had a better precision than that with the original one.

Machine learning of materials design and state prediction for lithium ion batteries

With the widespread use of lithium ion batteries in portable electronics and electric vehicles,further improvements in the performance of lithium ion battery materials and accurate prediction of battery state are of increasing interest to battery researchers.Machine learning,one of the core technologies of artificial intelligence,is rapidly changing many fields with its ability to learn from historical data and solve complex tasks,and it has emerged as a new technique for solving current research problems in the field of lithium ion batteries.This review begins with the introduction of the conceptual framework of machine learning and the general process of its application,then reviews some of the progress made by machine learning in both improving battery materials design and accurate prediction of battery state,and finally points out the current application problems of machine learning and future research directions.It is believed that the use of machine learning will further promote the large-scale application and improvement of lithium-ion batteries.

A Prediction Method Based on Improved Echo State Network for COVID-19 Nonlinear Time Series

This paper proposes a prediction method based on improved Echo State Network for COVID-19 nonlinear time series, which improves the Echo State Network from the reservoir topology and the output weight matrix, and adopt the ABC (Artificial Bee Colony) algorithm based on crossover and crowding strategy to optimize the parameters. Finally, the proposed method is simulated and the results show that it has stronger prediction ability for COVID-19 nonlinear time series.

Interval finite difference method for steady-state temperature field prediction with interval parameters

A new numerical technique named interval finite difference method is proposed for the steady-state temperature field prediction with uncertainties in both physical parameters and boundary conditions. Interval variables are used to quantitatively describe the uncertain parameters with limited information. Based on different Taylor and Neumann series, two kinds of parameter perturbation methods are presented to approximately yield the ranges of the uncertain temperature field. By comparing the results with traditional Monte Carlo simulation, a numerical example is given to demonstrate the feasibility and effectiveness of the proposed method for solving steady-state heat conduction problem with uncertain-but-bounded parameters.

Study on fuzzy control of dynamic equalization based on state prediction

A new method of using dynamic equalization technology to realize the maximum energy storage uti-lization was presented to overcome the influence of the disaccord among units of series super capacitor(SC) bank and ensure that the units could work safely.By considering in combination with the high spe-cific power,low working voltage,wide voltage working range and nonlinear external characteristics,wepresent constant duty ratio pulse frequency modulation mode and fuzzy control method based on state pre-diction in the active equalization circuit and accomplish the software and hardware design for the equaliza-tion system.The simulation analysis and experiment results of constant current multi-cycle and variablecurrent multi-cycle charge-discharge process verify the validity of the design.

Analogue correction method of errors and its application to numerical weather prediction

In this paper, an analogue correction method of errors （ACE） based on a complicated atmospheric model is further developed and applied to numerical weather prediction （NWP）. The analysis shows that the ACE can effectively reduce model errors by combining the statistical analogue method with the dynamical model together in order that the information of plenty of historical data is utilized in the current complicated NWP model, Furthermore, in the ACE, the differences of the similarities between different historical analogues and the current initial state are considered as the weights for estimating model errors. The results of daily, decad and monthly prediction experiments on a complicated T63 atmospheric model show that the performance of the ACE by correcting model errors based on the estimation of the errors of 4 historical analogue predictions is not only better than that of the scheme of only introducing the correction of the errors of every single analogue prediction, but is also better than that of the T63 model.

Comparative Study of Trajectory Tracking Control for Automated Vehicles via Model Predictive Control and Robust H-infinity State Feedback Control

A comparative study of model predictive control(MPC)schemes and robust Hstate feedback control(RSC)method for trajectory tracking is proposed in this paper.The main objective of this paper is to compare MPC and RSC controllers’performance in tracking predefined trajectory under different scenarios.MPC controller is designed based on the simple longitudinal-yaw-lateral motions of a single-track vehicle with a linear tire,which is an approximation of the more realistic model of a vehicle with double-track motion with a non-linear tire mode.RSC is designed on the basis of the same method as adopted for the MPC controller to achieve a fair comparison.Then,three test cases are built in CarSim-Simulink joint platform.Specifically,the verification test is used to test the tracking accuracy of MPC and RSC controller under well road conditions.Besides,the double lane change test with low road adhesion is designed to find the maximum velocity that both controllers can carry out while guaranteeing stability.Furthermore,an extreme curve test is built where the road adhesion changes suddenly,in order to test the performance of both controllers under extreme conditions.Finally,the advantages and disadvantages of MPC and RSC under different scenarios are also discussed.

DTHMM based delay modeling and prediction for networked control systems

In the forward channel of a networked control system （NCS）, by defining the network states as a hidden Markov chain and quantizing the network-induced delays to a discrete sequence distributing over a finite time interval, the relation between the network states and the network-induced delays is modelled as a discrete-time hidden Markov model （DTHMM）. The expectation maximization （EM） algorithm is introduced to derive the maximumlikelihood estimation （MLE） of the parameters of the DTHMM. Based on the derived DTHMM, the Viterbi algorithm is introduced to predict the controller-to-actuator （C-A） delay during the current sampling period. The simulation experiments demonstrate the effectiveness of the modelling and predicting methods proposed.

The improved local linear prediction of chaotic time series

Based on the Bayesian information criterion, this paper proposes the improved local linear prediction method to predict chaotic time series. This method uses spatial correlation and temporal correlation simultaneously. Simulation results show that the improved local linear prediction method can effectively make multi-step and one-step prediction of chaotic time series and the multi-step prediction performance and one-step prediction accuracy of the improved local linear prediction method are superior to those of the traditional local linear prediction method.

Remaining Useful Life Prediction of Rolling Element Bearings Based on Different Degradation Stages and Particle Filter

A method is proposed to improve the accuracy of remaining useful life prediction for rolling element bearings,based on a state space model(SSM)with different degradation stages and a particle filter.The model is improved by a method based on the Paris formula and the Foreman formula allowing the establishment of different degradation stages.The remaining useful life of rolling element bearings can be predicted by the adjusted model with inputs of physical data and operating status information.The late operating trend is predicted by the use of the particle filter algorithm.The rolling bearing full life experimental data validate the proposed method.Further,the prediction result is compared with the single SSM and the Gamma model,and the results indicate that the predicted accuracy of the proposed method is higher with better practicability.

Roles of initial ocean states on predicting the 2002/03 central Pacific El Nino

The 2002/03 El Ni？o event, a new type of El Ni？o with maximum warm anomaly occurring in the central equatorial Pacific, is known as central-Pacific（CP） El Ni？o. In this study, on the basis of an El Ni？o prediction system, roles of the initial ocean surface and subsurface states on predicting the 2002/03 CP El Ni？o event are investigated to determine conditions favorable for predicting El Ni？o growth and are isolated in three sets of hindcast experiments. The hindcast is initialized through assimilation of only the sea surface temperature（SST）observations to optimize the initial surface condition（Assim_SST）, only the sea level（SL） data to update the initial subsurface state（Assim_SL）, or both the SST and SL data（Assim_SST＋SL）. Results highlight that the hindcasts with three different initial states all can successfully predict the 2002/03 El Ni？o event one year in advance and that the Assim_SST＋SL hindcast performs best. A comparison between the various sets of hindcast results further demonstrates that successful prediction is significantly affected by both of the initial surface and subsurface conditions, but in different developing phases of the 2002/03 El Ni？o event. The accurate initial surface state can easier trigger the prediction of the 2002/03 El Ni？o, whereas a more reasonable initial subsurface state can contribute to improving the prediction in the growth of the warm event.

Temperature Prediction of Aluminum Alloy Work-Pieces in Aging Furnaces Based on Improved Case-Based Reasoning

The temperature of aluminum alloy work-pieces in the aging furnace directly affects the quality of aluminum alloy products. Since the temperature of aluminum alloy work-pieces cannot be measured directly, a temperature prediction model based on improved case-based reasoning (CBR) method is established to realize the online measurement of the work-pieces temperature. More specifically, the model is constructed by an advanced case-based reasoning method in which a state transition algorithm (STA) is firstly used to optimize the weights of feature attributes. In other words, STA is utilized to find the suitable attribute weights of the CBR model that can improve the accuracy of the case retrieval process. Finally, the CBR model based on STA (STCBR) was applied to predict the temperature of aluminum alloy work-pieces in the aging furnace. The results of the experiments indicated that the developed model can realize high-accuracy prediction of work-pieces temperature and it has good application prospects in the industrial field.

Adaptive State Feedback Predictive Control and Expert Control for a Delayed Coking Furnace

预兆的控制(SFPC ) 计划和专家控制策划的适应州的反馈被介绍并且适用于 1200 kt 路 a 的温度控制 ? 1 推迟了 coking 炉子，它是为推迟的 coking 进程的关键设备。适应 SFPC 被用来在正常操作改进温度控制的表演。一个简化非线性的模型根据炉子的第一个原则被开发由 linearization 获得一个州的空间模型。利用非线性的模型，因为试管 coking 和负担变化，改编方法的一个联机模型被介绍提供过程特征的动态变化。为了补偿源于特别速度的注射蒸气的突然的增加到试管，一个监视方法和一位专家的插头温度的大反的反应，基于热控制计划平衡计算被建议。工业实现显示出建议控制策略的有效性和可行性。

On the stability of two-step predictive controller based on state observer

For input saturated Hammerstein systems, the two-step predictive control strategy is adopted. The first step calculates the desired intermediate variable applying unconstrained linear modal and predictive control. The second step obtains the real-time control action by solving algebraic equation and desaturation. The case of immeasurable state is considered where the observer gain matrix is solved by Sylvester equation. The sufficient closed-loop stability condition is given and the designing and tuning algorithm for the domain of attraction is proposed. The theoretical results are validated by an example.

Aero-thermal heating constrained midcourse guidance using state-constrained model predictive static programming method

This paper proposes a multiple-constraints-guaranteed midcourse guidance law for the interception of the hypersonic targets. In traditional midcourse law design, the constraints of the aero-thermal heating are rarely taken into consideration. The performance of the infrared detection system may be degraded and the instability of the flight control system may be induced.To address this problem, a state-constrained model predictive static programming method is introduced such that both terminal constraints(position and angle) and optimal energy consumption can be ensured. As a result, a sub-optimal midcourse guidance,guaranteeing the aforementioned multiple-constraints to be never violated, is synthesized. Simulation results demonstrate the effectiveness of the proposed method.

State Predictive Model Following Control System for Linear Time Delays

In this paper, we propose a new state predictive model following control system （MFCS）. The considered system has linear time delays. With the MFCS method, we obtain a simple input control law. The bounded property of the internal states for the control is given and the utility of this control design is guaranteed. Finally, an example is given to illustrate the effectiveness of the proposed method.