A variance-constrained proportional—integral feedback controller that tunes itself

Proportional-Integral (PI) feedback controllers have been shown to provide a very robust process adjustment strategy. Recent work by Box and Luceno [1-3] provides PI controller settings that trade-off the variance of the quality characteristic versus the variance of the adjustments, an important concern in manufacturing. A Quality Engineer may desire a particular maximum adjustment variability, perhaps because of safety considerations or because the resolution of the manufacturing equipment. The graphs and tables provided by these authors were developed by minimizing a weighted sum of adjustment and output variance and will not always provide enough information to guarantee a specific maximum adjustment variance. Specifying a constraint on the adjustment variance is much more natural for a Quality Engineer than specifying the value of the relative weight that satisfies the adjustment constraint, and easier than choosing from a list of possible controller designs. In order to obtain a specific adjustment variance, parameter estimates of the process are needed. Moreover, to allow for rapid control after startup and to minimize scrap, it is desirable that on-line estimation occurs during closed-loop operation. This paper presents a PI controller that tunes its parameters on-line during closed-loop operation for a first order process under the assumption that no prior process information is known before starting the control session. Under these conditions, the proposed PI controller provides the minimum output variance for a specified upper bound in the adjustment variance; the value of the Lagrange multiplier associated with the constraint, similar to the relative weight in Box and Luceno's procedure, is computed internally by the controller and there is no need to specify it a-priori. An analysis of the performance of the controller is presented. It is shown that the proposed controller converges rapidly to the desired configuration without sacrificing the performance during the transient phase. g in Operations     

一款基于AGC-PI结构的微陀螺闭环驱动电路芯片

比例积分(PI)控制器是微陀螺自动增益控制(AGC)闭环驱动电路中的重要模块,常被用来改善闭环驱动电路的瞬态响应特性.但是,传统的PI控制器电路存在功耗、面积大的缺点.针对此问题,提出了一种Gm-C结构的PI控制器,其仅由一个片内的跨导放大器和片外电容电阻网络即同时实现了减法、比例和积分功能,显著减小了芯片功耗和面积.此外,提出了一种带温度补偿的跨阻放大器(TIA)结构的读出电路,将驱动轴振动速度幅值的温漂系数从补偿前的1 640×10-6/℃提高到了114×10-6/℃.设计的驱动电路芯片在0.35μm标准CMOS工艺下实现,工作电压为5 V.其中PI控制器的静态功耗小于1 mW,面积仅为0.18 mm×0.08 mm.芯片与微陀螺的联合测试结果表明,在PI控制器的比例系数和积分系数分别设置为10和200时,闭环驱动电路有最佳的瞬态响应.     

Speed control of SR motor by self-tuning fuzzy PI controller with artificial neural network

In this work, the dynamic model, flux-current-rotor position and torque-current-rotor position values of the switched reluctance motor (SRM) are obtained in MATLAB/Simulink. Motor control speed is achieved by self-tuning fuzzy PI (Proportional Integral) controller with artificial neural network tuning (NSTFPI). Performance of NSTFPI controller is compared with performance of fuzzy logic (FL) and fuzzy logic PI (FLPI) controllers in respect of rise time, settling time, overshoot and steady state error.     

A real-time filtering method for the drift data of a laser Doppler velocimeter

To effectively reduce the random drift of a laser Doppler velocimeter (LDV), a real-time filtering model is presented for filtering the drift data of an LDV, which is a combination of the metabolic grey model (1, 1) and the metabolic time series model AR (2). The basic principle of the metabolic grey-time series model is introduced in detail first. Then, the model is established for the static and dynamic drift data, and a Kalman filter is used to filter the drift data based on the model. The variance analysis method and the Allan variance method are employed to analyse the static drift data. The dynamic drift data are also compared before and after being modelled and filtered. The results demonstrate that the metabolic grey-time series method cannot only effectively reduce the static random drift of an LDV, but can also reduce the dynamic random drift in real time.     

Robust controllers design and efficiency analysis for a brushless servo system

Abstract

This paper investigates the design of robust controllers for a class of ac servo drives, the brushless servo motor drive, and presents the efficiency and power factor analysis of the drive system. Robust PI, H ², and H ⁸ control techniques are applied to the design of servo controllers. A fully digital control system is constructed to experimentally verify the controller performance. The steady‐state characteristics of the servo drive are derived and are measured on‐line by a microcomputer‐based efficiency measurement system. The theoretical development is validated by the experimental work.     

Optimal Adjustment in the Presence of Deterministic Process Drift and Random Adjustment Error

A state-space process-control model involving adjustment error and deterministic drift of the process mean is presented. The optimal adjustment policy is developed by dynamic programming. This policy calls for a particular adjustment when a Kalman-filter estimator is outside a deadband defined by upper and lower action limits. The effects of adjustment cost, adjustment variance, and drift rate on the optimal policy are discussed. The optimal adjustment policy is computed for a real machining process, and a simulation study is presented that compares the optimal policy to two sensible suboptimal policies.     

A multivariate double EWMA process adjustment scheme for drifting processes

The “predictor-corrector” feedback controller, a process adjustment scheme proposed for semiconductor manufacturing run-to-run processes that drift, is extended to the multiple-input-multiple-output case. The controller is based on two coupled multivariate Exponentially-Weighted-Moving-Average (EWMA) equations, thus its performance depends on the choices of EWMA weight matrices. Stability conditions are given for a pure gain process adjusted with a MIMO double EWMA (EWMA) controller. It is shown that the stability conditions are invariant with respect to various realistic drift disturbance models. Recommendations on how to choose the EWMA weight matrices are given. An analysis is conducted to assess the impact of errors in the estimates of the process gains. The proposed MIMO EWMA feedback controller is compared to the common practice of using multiple single-input-single-ouput dEWMA controllers running in parallel.     

An object-oriented simulation architecture with real-time capabilities

Simulation that approximates real-time system response has previously been used to train operators in the use of system controllers. These simulations typically use architectures that are highly specific and tightly coupled to the system hardware. A formalized generic real-time simulation architecture is presented to decouple the simulation and provide a mechanism for implementation and rapid reconfiguration. This architecture distributes the state variables in the system by coupling them with manufacturing process equipment, specifically for the continuous process industries such as chemical, food and pharmaceutical. This design provides a mechanism for rapidly interchanging manufacturing equipment and simulation model components for discrete and continuous simulation, that facilitates operator training.     

Long run and transient analysis of a double EWMA feedback controller

The “predictor-corrector” feedback controller is a popular adjustment scheme proposed for the quality control of certain semiconductor manufacturing process steps. This controller is based on a double Exponentially-Weighted Moving Average (EWMA) scheme; thus the performance of the closed-loop system depends on the two weight parameters of the EWM A equations. In this paper, the conditions the weights must satisfy to ensure closed-loop stability are discussed. The optimal determination of the controller weights depends on a trade-off between long-run process variance and transient bias performance. It is shown that small weights, although they can guarantee stability, may result in severe, long transients, an important concern if fabrication is in small batches. An optimization model for finding the controller weights is given and numerically solved. An extension of this type of controllers to the multiple controllable factor case is described. The performance of the controller is illustrated with an application to Chemical Mechanical Polishing, a critical semiconductor manufacturing step.     

A kalman filtering process control scheme with an application in semiconductor short run manufacturing

A quality control chart for monitoring a short run process during the start-up phase is presented in this article. The chart is based on the Kalman filter recursive equations being applied to a stable process where the process variance is unknown prior to the start of the production run. The run length properties of this control scheme are discussed. It is shown that for the proposed scheme the run length properties are independent of the unknown process variance and that these properties are appropriate for monitoring a stable process during start-up. An economic model for the optimal design of the control scheme is presented and illustrated with a wet etching process used in semiconductor manufacturing.     

A Multivariate EWMA Controller for Linear Dynamic Processes

Most research of run-to-run process control has been based on single-input and single-output processes with static input–output relationships. In practice, many complicated semiconductor manufacturing processes have multiple-input and multiple-output (MIMO) variables. In addition, the effects of previous process input recipes and output responses on the current outputs might be carried over for several process periods. Under these circumstances, using conventional controllers usually results in unsatisfactory performance. To overcome this, a complicated process could be viewed as dynamic MIMO systems with added general process disturbance and this article proposes a dynamic-process multivariate exponentially weighted moving average (MEWMA) controller to adjust those processes. The long-term stability conditions of the proposed controller are derived analytically. Furthermore, by minimizing the total mean square error (TMSE) of the process outputs, the optimal discount matrix of the proposed controller under vector IMA(1,?1) disturbance is derived. Finally, to highlight the contribution of the proposed controller, we also conduct a comprehensive simulation study to compare the control performance of the proposed controller with that of the single MEWMA and self-tuning controllers. On average, the results demonstrate that the proposed controller outperforms the other two controllers with a TMSE reduction about 32% and 43%, respectively.     

Automated Controller Placement for Software-Defined Networks to Resist DDoS Attacks

In software-defined networks (SDNs), controller placement is a critical factor in the design and planning for the future Internet of Things (IoT), telecommunication, and satellite communication systems. Existing research has concentrated largely on factors such as reliability, latency, controller capacity, propagation delay, and energy consumption. However, SDNs are vulnerable to distributed denial of service (DDoS) attacks that interfere with legitimate use of the network. The ever-increasing frequency of DDoS attacks has made it necessary to consider them in network design, especially in critical applications such as military, health care, and financial services networks requiring high availability. We propose a mathematical model for planning the deployment of SDN smart backup controllers (SBCs) to preserve service in the presence of DDoS attacks. Given a number of input parameters, our model has two distinct capabilities. First, it determines the optimal number of primary controllers to place at specific locations or nodes under normal operating conditions. Second, it recommends an optimal number of smart backup controllers for use with different levels of DDoS attacks. The goal of the model is to improve resistance to DDoS attacks while optimizing the overall cost based on the parameters. Our simulated results demonstrate that the model is useful in planning for SDN reliability in the presence of DDoS attacks while managing the overall cost.     

An analysis of failure-time distributions for product design optimization

Analysis of experimental data is often a problem facing design and manufacturing engineers. Many experiments are run for the express purpose of making a decision between manufacturing process or material alternatives. Statisticians recommend replication in experimental design; however, methods of analysing experimental data, as presented in a majority of engineering curricula, generally review only the most basic situations (checking for a statistically significant difference between the means or variances of two samples, for example). If means and variances change with time, group comparisons may require more sophisticated analyses. This paper presents one method that takes into account shifts in group means and variances over time. Resistance temperature sensor drift data generated from six different design configurations are used as an illustration. This procedure takes into account all drift path information from multiple sensors in multiple groups.     

Measurement System Analysis in a Production Environment with Multiple Test Parameters

The purpose of measurement system analysis (MSA) is to separate the variation among devices being measured from the error in the measurement system. The total measurement system error can be further decomposed into variance components associated with the measurement equipment and repeatability. An analysis of variance approach based on a variance component model is used to model the variables of interest. Once estimated, the variance components are used to compute various metrics, which quantify the adequacy of the measurement system for the application in which it is used. Confidence intervals computed on the variance components and metrics indicate the amount of precision in the estimates. The MSA is typically conducted on a single measurement variable with a single measurement instance. The aim of this paper is to extend the univariate single-instance case to a common manufacturing test scenario where multiple parameters are tested on each device with a sequence of tests, which may include retest and test and repair steps. The methods presented are illustrated with examples from an industrial application.     

Performance of real-time distributed arrival time control in heterarchical manufacturing systems

A highly distributed feedback control algorithm for autonomous part entities in heterarchical manufacturing systems is presented in this paper. A difference equation-based model is developed to analyze the discrete time dynamics of the resulting nonlinear control system. Control parameters are found analytically that guarantee that the system is bounded under disturbances. The dynamic response to: (i) changes in due dates; and (ii) the bulk arrival of parts is presented. The ability of the system to exponentially reduce the mean and variance of due-date deviation in the single machine case makes it an attractive option for real-time control of just-in-time production.     

On the Efficiency and Robustness of Discrete Proportional-Integral Control Schemes

Feedback control schemes have been widely used in process industries for many years. They are also increasingly being used in the discrete-parts manufacturing industry in recent years. Proportionalintegral (PI) schemes are especially popular, primarily because of their simple structure and ease of implementation. This article studies the efficiency and robustness properties of discrete PI schemes under some commonly encountered situations. For process disturbance, we consider the stationary ARMA (1, 1) model and the nonstationary ARIMA (1, 1, 1) model. Process dynamics is studied under a first-order dynamic model, including the special case of pure gain. The efficiency of PI schemes is compared with that of minimum mean squared error (MMSE) schemes under these models. The PI schemes are seen to be quite efficient over a broad range of the parameter space. Furthermore, the PI schemes are much more robust than MMSE schemes to model misspecihcations, especially the presence of first-order nonstationarity. The results here provide additional justification for the use of discrete PI schemes.     

基于Agent的机器人新型控制器模型研究

进行了基于Agent的机器人新型控制器模型研究,在指出传统机器人控制器的缺陷基础上首先介绍了机器人控制器适应先进制造要求的发展方向,在此基础上详细介绍了提出的基于Agent的机器人新型控制器模型,对于开发未来的开放化机器人控制器具有重要的指导意义。     

高频(5G)信号对新能源汽车控制器的辐射研究

随着5G等高频信号快速推广和应用,信号上升边沿极速变陡,对汽车控制器的信号完整性造成极大的威胁.针对5G等高频信号对汽车控制器传输线辐射问题,该文对传输线算法模型进行分析,提出一种边界有限元算法模型,该模型能够对汽车控制器的传输线进行边界界定以此进行理论算法曲线分析;针对汽车控制器传输线互连结构造成的辐射问题,提出一种...     

史密斯预估补偿控制与PID控制的比较研究

比较了两种工业过程控制中的控制器,PID控制器和史密斯预估补偿控制器.介绍了四种PID参数的整定方法,分别为试凑法确定PID调节参数、Ziegler-Nichols参数整定方法、最优PID整定算法、运用Matlab/simulink中的NCD Outport(非线性控制设计输出端口模块)模块对PID参数进行最优整定.史密斯预估补偿控制器主要用于工业控制中的大延时系统控制,以提高系统的阶跃响应性能.在理论上,史密斯预估补偿控制器提供了一个有效的方法来提高控制效果.运用Matlab/Simulink仿真方案对前述两种控制方法进行了仿真比较,结果表明,在有较大延时情况下,史密斯预估补偿控制能获得比PID控制更好的结果.