Control Model for Large‐Sized Gantry Type Linear Motor Slider

At present, many industrial carrier devices utilize linear motor sliders. However, a heavier load requires a linear motor (which is an example of a direct drive device) to get higher power, compared with a rotary motor with a ball screw slider (which is an example of an indirect drive device). In order to obtain higher power at a lower cost, a linear slider with multiple motors can be utilized, for example, a gantry type linear motor slider. Moving the gantry type slider requires two linear motors that are set up in parallel to enable synchronization control. Some conventional synchronization control methods have been proposed for the parallel twin linear slider; however, a large‐scaled gantry type linear motor slider has two unique problems: mechanical distortion caused by the limitations of installation environment and coupling caused by joints with low stiffness. This paper proposes a control model to solve these problems, and an identification method of each parameter. Furthermore, the effectiveness of the control model is verified by comparison with simulation results and experimental results.     

Artificial neural networks as alternative tool for minimizing error predictions in manufacturing ultradeformable nanoliposome formulations

This work was aimed at determining the feasibility of artificial neural networks (ANN) by implementing backpropagation algorithms with default settings to generate better predictive models than multiple linear regression (MLR) analysis. The study was hypothesized on timolol-loaded liposomes. As tutorial data for ANN, causal factors were used, which were fed into the computer program. The number of training cycles has been identified in order to optimize the performance of the ANN. The optimization was performed by minimizing the error between the predicted and real response values in the training step. The results showed that training was stopped at 10?000 training cycles with 80% of the pattern values, because at this point the ANN generalizes better. Minimum validation error was achieved at 12 hidden neurons in a single layer. MLR has great prediction ability, with errors between predicted and real values lower than 1% in some of the parameters evaluated. Thus, the performance of this model was compared to that of the MLR using a factorial design. Optimal formulations were identified by minimizing the distance among measured and theoretical parameters, by estimating the prediction errors. Results indicate that the ANN shows much better predictive ability than the MLR model. These findings demonstrate the increased efficiency of the combination of ANN and design of experiments, compared to the conventional MLR modeling techniques.     

Temperature Compensation Algorithm for Hydraulic System Pressure Control

In this paper the control mechanism of solenoid valve is analyzed, which shows the solenoid valve control is actually the control of coil current. The response characteristic of coil current is related to coil inductance and resistance. The coil resistance is influenced greatly by the ambient temperature and the self-heating of coil, which affects the control precision of coil current. First, considering the heat dissipation mode of coil, the coil temperature model is established from the perspective of heat conduction, and a temperature compensation algorithm for hydraulic system pressure control is put forward. Then the hardware-in-the-loop testbed is set up by using the dSPACE platform, carrying out wheel cylinder pressurization tests with inlet valve fully opened at -40℃ and 20℃, and testing the actual pressure of wheel cylinder with the target pressures at -40℃ and 6.000 kPa/s (pressurization rate). The results show that the pressure control temperature compensation algorithm proposed in this paper accurately corrects the influence of resistance temperature drift on the response accuracy of wheel cylinder pressure. After the correction, the pressure difference is less than 500 kPa, which can meet the control accuracy requirements of solenoid valve, enriching the linear control characteristic of solenoid valve.     

Stability and stabilization for singularly perturbed systems with Markovian jumps

This article focuses on the stability and stabilization problems of singularly perturbed jump systems. Here, the singularly perturbed parameter (SPP) is also with Markov switching and satisfies any with positive bound predefined. First, stability conditions expressed ?_i‐free but involving its bound are developed by constructing an ?_i‐dependent Lyapunov function. Then, a method for state feedback stabilization controller depending on SPP is proposed, whose conditions are given in terms of linear matrix inequalities. Moreover, some special cases about deterministic SPP are considered too. Finally, two practical examples are used to demonstrate the effectiveness and superiorities of the proposed methods.     

硅树脂添加量对氧化铝基陶瓷型芯性能的影响

以氧化铝粉末为基体,添加具有粘结性和一定陶瓷产率的硅树脂粉末,通过干压成型和无压烧结制备出氧化铝基陶瓷型芯,重点研究了硅树脂添加量对氧化铝基陶瓷型芯性能的影响。结果表明:硅树脂在裂解过程中会形成二氧化硅,二氧化硅与氧化铝基体发生反应形成新相莫来石。由于硅树脂在交联和裂解过程中会释放大量气体,导致烧结体失重,且气体的逸出会抑制由烧结引起的收缩,因此,随着硅树脂添加量增加,产生的气体量增加,烧结体的失重率增加,收缩率降低。硅树脂含量的增加使得烧结体的气孔率变大和体积密度减小,烧结体的室温抗弯强度逐渐减小。硅树脂的添加虽然降低了其室温强度,但是保证了陶瓷型芯的尺寸精度。     

A numerical filtering method for linear state-space models with Markov switching

A class of discrete-time random processes arising in engineering and econometrics applications consists of a linear state-space model whose parameters are modulated by the state of a finite-state Markov chain. Typical filtering approaches are collapsing methods, which approximate filtered distributions by mixtures of Gaussians, each Gaussian corresponding to one possibility of the recent history of the Markov chain, and particle methods. This article presents an alternative approach to filtering these processes based on keeping track of the values of the underlying filtered density and its characteristic function on grids. We prove that it has favorable convergence properties under certain assumptions. On the other hand, as a grid method, it suffers from the curse of dimensionality, and so is only suitable for low-dimensional systems. We compare our method to collapsing filters and a particle filter with examples, and find that it can outperform them on 1- and 2-dimensional problems, but loses its speed advantage on 3-dimensional systems. Meanwhile, our method has a proven theoretical convergence rate that is probably not achieved by collapsing and particle methods.     

Full and reduced-order H∞ filtering of Takagi–Sugeno fuzzy time-varying delay systems: Input–output approach

This article addresses the issue of delay-dependent H_∞ filtering design for TakagiŮSugeno fuzzy time-varying delay systems using the input–output approach. A three-term approximation model has been used to transform the original system into two interconnected subsystems. Since the nonquadratic Lyapunov–Krasovskii functional requires to deal with the membership function's (MF) time derivative, upper-bound inequalities have been added to the obtained conditions. Based on the scaled small gain theorem, nonquadratic Lyapunov–Krasovskii functional approach and considering the bounds of the MF time-derivative, the H_∞ full- and reduced-order filters are designed and then formulated in terms of linear matrix inequalities. Finally, illustrative examples are presented to demonstrate the validity of the proposed methods.     

Prediction of the colorimetric parameters and mass loss of heat‐treated bamboo: Comparison of multiple linear regression and artificial neural network method

In this study, the colorimetric parameters (L*, a*, b*) and mass loss of heat‐treated bamboo were investigated, and the obtained results were modeled by using two methods: multiple linear regression (MLR) and artificial neural network (ANN). First, bamboo samples were exposed to heat treatment at different temperatures (110°C, 140°C, 170°C, and 200°C) and durations (15, 30, 45, 60, 75, 90, and 115 minutes) in a laboratory oven. Then, the colorimetric parameters (L*, a*, b*) and mass loss of each sample were measured after each period of heat treatment. All data were modeled by using two methods separately for each parameter and the performances of these proposed methods were compared. It was found that color change and mass loss increased with increasing temperature and duration of heat treatment. Mean absolute percentage error (MAPE) values of all obtained MLR ranged from 0.64% to 10.63%, while the all MAPE values of ANN were found to be lower than 1.5%. Based on these results, it can be said that MLR and ANN could be used to evaluate the changes on the selected properties of heat‐treated bamboo samples. On the other hand, it should be emphasized that the ANN gave more accurate results than the MLR method because of its learning capability.     

里底水电站混凝土强度与波速线性关系研究及应用

混凝土工程是电站枢纽工程最重要的组成部分之一,是工程安全的基础和保障,确保混凝土实体质量、杜绝混凝土质量缺陷是电站安全稳定运行的基本保证。随着声波检测技术水平的提高,并已在大坝混凝土工程中得到广泛应用。通过大量混凝土实测抗压强度和声波波速值对比试验的数理统计分析,建立工程专用测强曲线,能有效提高混凝土抗压强度推算值的准确性,较真实地反映混凝土实体质量水平。     

液层Pr比值对环形双液层热毛细对流的影响

为了解双层流体系统上液层流体与下液层流体普朗特数(Pr)对热毛细对流的影响,通过线性稳定性分析,确定了上液层流体与下液层流体Pr比值从0.164～5.417时环形双层流体热毛细对流失稳的临界条件,预测了它们的4种流动失稳型式,即轮辐状的几乎占据了整个液层的"轮辐波"、轮辐状的热流体波与同波数共同旋向的靠热壁处流胞、径向流动的三维稳态流动、靠热壁处的短小的"边沿波"。结果表明:随着上、下液层Pr比值的增大,环形双液层流体的流动稳定性以上、下液层Pr比值0.750为分界线呈分段上升趋势。上、下液层Pr比值约为0.750时,双液层流体的稳定性最差,此时易出现流动分岔现象。为了获得更好的系统流动稳定性、提高生长晶体的质量,在选择双液层工质对时,应选择上、下液层Pr比值较大的工质对。