Robust PID controller design for nonlinear processes using JITL technique

A robust PID controller design methodology for nonlinear processes is proposed based on the just-in-time learning (JITL) technique. To do so, a composite model consisting of a nominal ARX model and the JITL, where the former is used to capture linear process dynamics and the latter to approximate the inevitable modeling error caused by the process nonlinearity, is employed to model the process dynamics in the operating space of interest. The state space realizations of this composite model and PID controller are then reformulated as an uncertain closed-loop system, by which the corresponding robust stability condition is developed. Literature examples are employed to illustrate the proposed methodology and a comparison with the previous result is made.     

A new data-based methodology for nonlinear process modeling

A new data-based method for nonlinear process modeling is developed in this paper. In the proposed method, both distance measure and angle measure are used to evaluate the similarity between data, which is not exploited in the previous work. In addition, parametric stability constraints are incorporated into the proposed method to address the stability of local models. Furthermore, a new procedure of selecting the relevant data set is proposed. Literature examples are presented to illustrate the modeling capability of the proposed method. The adaptive capability of the proposed method is also evaluated.     

基于细粒度代码表示和特征融合的即时软件缺陷预测方法

即时软件缺陷预测指在软件更改初次提交之际预测该更改引入缺陷的倾向。此类预测针对单一程序变更,而非在粗粒度上进行。由于其即时性和可追溯性,该技术已在持续测试等领域得到广泛应用。目前的研究中,提取变更代码表示的方法粒度较粗,仅标出了变更行,而没有进行细粒度的标记。此外,现有的使用提交内容进行缺陷预测的方法,仅仅是把提交消息与变更代码的特征进行简单拼接,缺失了在特征空间上的深度对齐,这使得在提交消息质量参差不齐的情况下,会出现预测结果易受噪声干扰的情形,并且现有方法也未将领域专家设计的人工特征以及变更内容中的语义语法信息综合起来进行预测。为了解决上述问题,提出了一种基于细粒度代码表征和特征融合的即时软件缺陷预测方法。通过引入新的变更嵌入计算方法来在细粒度上表示变更代码。同时,引入特征对齐模块,降低提交消息中噪声对方法性能的影响。此外,使用神经网络从人工设计的特征中学习专业知识,充分利用现有特征进行预测。实验结果表明,相较于现有方法,该方法在3个性能指标上均有显著提升。     

JITL based MWGPR soft sensor for multi-mode process with dual-updating strategy

Process nonlinearity, multiple operating modes and time-varying characteristics often deteriorate the prediction performance of process models. In this article, a multi-mode moving-window Gaussian process regression (MWGPR) based approach for ARX modeling is proposed to effectively capture process nonlinearity or switching dynamics. The Gaussian mixture model (GMM) is first introduced to separate the data into different operating modes. Then the MWGPR strategy is applied to identify the local ARX model. Just-in-time learning (JITL) and dual updating are applied for more effective tracking of process dynamics. A simulation of a continuous fermentation process and a pilot scale experiment are presented to demonstrate the effectiveness of the proposed method.     

“Slimming” a Java virtual machine by way of cold code removal and optimistic partial program loading

Embedded systems provide limited storage capacity. This limitation conflicts with the demands of modern virtual machine platforms, which require large amounts of library code to be present on each client device. These conflicting requirements are often resolved by providing specialized embedded versions of the standard libraries, but even these stripped down libraries consume significant resources.We present a solution for “always connected” mobile devices based on a zero footprint client paradigm. In our approach, all code resides on a remote server. Only those parts of applications and libraries that are likely to be needed are transferred to the mobile client device. Since it is difficult to predict statically which library parts will be needed at run time, we combine static analysis, opportunistic off-target linking and lazy code loading to transfer code with a high likelihood of execution ahead of time while the other code, such as exception code, remains on the server and is transferred only on demand. This allows us to perform not only dead code elimination, but also aggressive elimination of unused code.The granularity of our approach is flexible from class files all the way down to individual basic blocks. Our method achieves total code size reductions of up to 95%.     

The reliable design of one-piece flow production system using fuzzy ant colony optimization

In this work, one-piece flow production system is designed with the purpose of ensuring just-in-time production. Three approaches are applied to achieve the goal: adopting straightforward schedule policies, relaxing the Takt time and decreasing the risk of machine failures and operator mistakes. Consequently, a multi-objective design model is proposed, whose aim is to minimize cycle time, changeover count, cell load variation and the number of cells and maximize the extent to which items are completed in a cell. The fuzzy ant colony optimization (FACO) is also presented to solve the formulated problem. In FACO, the fuzzy logic controller (FLC) is used to adapt the evaporated and deposited value of pheromone trail based on the ant's fitness and pheromone trail age. Furthermore, domain knowledge of facility layout, generated based on the travel chart method, is also adaptively injected to improve the performance of FACO. The proposed method is evaluated with the real-world data and experimental results demonstrate that our method outperforms many other methods in efficiency, solution quality and facilitation measures.     

Adaptive speed control based on just-in-time learning technique for permanent magnet synchronous linear motor

In this paper, an adaptive two degrees of freedom (2Dof) PI controller based on a just-in-time learning (JITL) method is proposed for predictive speed control of permanent magnet synchronous linear motor (PMSLM). Firstly, to guarantee the high identification accuracy and high real-time performance simultaneously, an improved JITL method is proposed to estimate the controlled model parameters of speed control system. Then, based on the dynamic controlled model, a simplified generalized predictive control (GPC) supplies a 2Dof proportional integral (PI) controller with suitable control parameters to follow a sinusoid-type speed command in operating conditions. The main motivation of this paper is the extension of the predictive controller to replace traditional PI controller in industrial applications. Finally, the efficacy and usefulness of the proposed controller are verified through the experimental results.     

Scheduling a single vehicle in the just-in-time part supply for a mixed-model assembly line

This paper focuses on the scheduling of a single vehicle, which delivers parts from a storage centre to workstations in a mixed-model assembly line. In order to avoid part shortage and to cut down total inventory holding and travelling costs, the destination workstation, the part quantity and the departure time of each delivery have to be specified properly according to predetermined assembly sequences. In this paper, an optimisation model is established for the configuration that only one destination workstation is involved within each delivery. Four specific properties of the problem are deduced, then a backward-backtracking approach and a hybrid GASA (genetic algorithm and simulated annealing) approach are developed based on these properties. Both two approaches are applied to several groups of instances with real-world data, and results show that the GASA approach is efficient even in large instances. Furthermore, the existence of feasible solutions (EOFS) is analysed via instances with different problem settings, which are obtained by an orthodox experimental design (ODE). An analysis of variance (ANOVA) shows that the buffer capacity is the most significant factor influencing the EOFS. Besides this, both the assembly sequence length and distances to workstations also have noticeable impacts.     

An overview of JIT

The term JIT (Just in Time), when used as a manufacturing control system, has become very confusing over the past few years. Everyone wants to claim they are users of JIT systems and because of this a variety of systems creations, all claiming themselves to be JIT systems, have sprouted up, none of which look anything like what Japan uses. This article attempts to clear up some of this confusion by showing four different variations of JIT and comparing them to the traditional MRP/MRP II environment.