Stability and performance of a simple distributed tracking policyfor production control of manufacturing systems

The objective of distributed tracking is to operate a production system as closely as possible to an idealized regime obtained by a continuous-flow relaxation of the actual (discrete) production control problem. The stability and performance of a large class of distributed tracking policies called “nonidling-nonexceeding (NINE)” were investigated in an earlier paper (1994). In this work we focus on the most natural tracking policy in this class and find a tight bound on its performance for a single machine and a sufficient condition for its stability for multiple-machine systems. This condition is considerably less stringent than the one available for general NINE policies     

Stability and performance analysis of an irrigation channel with distributed control

For a string of pools with distant-downstream control, the internal time-delay for water transport from upstream to downstream does not only limit the local control performance of regulating water-levels at setpoints and rejecting offtake disturbances in each pool, but also impacts the global performance of managing the water-level error propagation and attenuating the amplification of control actions in the upstream direction. A distributed control scheme that inherits the interconnection structure of the plant is investigated. It is shown that the decoupling terms in the controller help to improve global closed-loop performance by decreasing the low-frequency gain of the closed-loop coupling. Moreover, the decoupling terms compensate for the influence of the time-delay by imposing extra phase lead-lag compensation in the mid-frequency range on the closed-loop coupling function.     

Intelligent distributed production control

This editorial introduces the special issue of the Springer journal, Journal of Intelligent Manufacturing, on intelligent distributed production control. This special issue contains selected papers presented at the 13th IFAC Symposium on Information Control Problems in Manufacturing??INCOM??2009 (Bakhtadze and Dolgui 2009). The papers in this special issue were selected because of their high quality and their specific way of addressing the variety of issues dealing with intelligent distributed production control. Previous global discussions about the state of the art in intelligent distributed production control are provided, as well as exploratory guidelines for future research in this area.     

Frequency and time-domain analysis on performance of a production line operated by observer-based distributed control

This article deals with the problem of controlling a tandem line of manufacturing machines such that an unknown production demand is tracked with a desired accuracy. To study this problem, a manufacturing machine is approximated by an integrator which is subject to input saturation as a result of the finite capacity of the machine. To solve the problem in the case of unknown demand rate, a combination of feedforward–feedback controller with a reduced-order observer is proposed. The decentralised feedback control strategy for a line of machines is proposed and studied both in continuous time-domain and in frequency-domain representation. The presented illustrating example for a line of four machines underlines the efficiency of the proposed frequency-domain-based performance analysis tool.     

Information characteristics and their impact on system performance: framework and case study in distributed production control

The judicious use of information is critical to the effective design and operation of intelligent systems such as flexible factories. Three key attributes of information are sufficiency, efficiency, and timeliness. We examine the impact of these parameters on system effectiveness through analytic models and simulation studies.     

分布式网络拥塞控制算法稳定性研究

为解决由于网络阻塞而造成的分组丢弃和时延,基于优化理论框架,建立了原始—对偶模型,可用于描述一类拥塞控制算法.对该模型平衡点存在的条件和系统的稳定性进行了分析,与传统的网络控制算法相比,该算法考虑到边界条件的限制,得出了系统存在唯一平衡点的条件.通过TCP/E-RED网络对该理论进行了验证,结果表明,对于一般网络结构,该模型不具有往返时延,系统具有极高的稳定性,提高了网络性能.该方法用于避免由于网络阻塞而造成的分组丢弃和时延是可行的、有效的.     

Stability analysis of model-based networked distributed control systems

Networked distributed control systems (NDCSs) face serious challenges such as delays and packet dropouts induced by the communication network employed to connect local controllers of interacting subsystems. These two network-induced shortcomings may degrade the performance or even destabilize NDCSs. This paper is concerned with the problem of stability analysis and stabilization of the NDCSs, featuring both random delay and random packet loss in their communication networks. A model-based networked distributed control framework is proposed to stabilize the NDCS consisting of discrete-time subsystems interconnected through their states. In this control framework, to compensate for the adverse effects of these two network-induced shortcomings, an interaction estimator is provided in each local controller; in addition to a main control unit. This estimator uses the explicit model of the subsystems to estimate the evolution of the states of interacting subsystems, when information about their actual values is not available. A model for the NDCS subject to both random packet loss and random delay is developed. By providing a 3-step interaction estimating algorithm, the closed-loop model-based networked distributed control system (MB-NDCS) is formulated as a time-dependent impulsive system. Then, a quadratic Lyapunov function is constructed to derive a linear matrix inequality (LMI) based sufficient condition for stability analysis of the overall impulsive system. Finally, an illustrative example of a network of interconnected chemical reactors with recycle is presented to show the effectiveness of the proposed approach.     

Stability of distributed congestion control with heterogeneousfeedback delays

We investigate how congestion control can achieve efficient usage of network resources in the presence of heterogeneous communication delays between network users and resources. To this end, we consider a fluid flow model of network behavior. We study the stability of the system's behavior under small perturbations around the target equilibrium point (local stability). We establish several criteria for stability of certain linear delay-differential equations, via a technique which essentially reduces the question to studying stability of ordinary differential equations. These results are then used to derive sufficient conditions for local stability of the network congestion control problem. The same issue has been studied by Johari et al. (2001), where the authors propose a conjecture according to which local stability can be ensured in a distributed way. The correctness of the conjecture was established by Johari et al., only in degenerate cases where feedback delays coincide. Our results show that a modified form of the conjecture holds true for arbitrary feedback delays     

Information retrieval with distributed databases: analytic models of performance

The major emphasis is on analytical techniques for predicting the performance of various collection fusion scenarios. Knowledge of analytical models of information retrieval system performance, both with single processors and with multiple processors, increases our understanding of the parameters (e.g., number of documents, ranking algorithms, stemming algorithms, stop word lists, etc.) affecting system behavior. While there is a growing literature on the implementation of distributed information retrieval systems and digital libraries, little research has focused on analytic models of performance. We analytically describe the performance for single and multiple processors, both when different processors have the same parameter values and when they have different values. The use of different ranking algorithms and parameter values at different sites is examined.     

Intelligent distributed and supervised flow control methodology for production systems

This paper deals with the development of an intelligent distributed and supervised control approach for high-volume production systems, in which the flow of parts can be approximated by a continuous (fluid) model. The proposed approach is based on the decomposition of the production system into elementary modules in order to reduce the control design computational complexity. In this context, a two levels control structure is proposed. At the local level, a surplus-based principle is adopted to regulate the production flow for each module according to the distributed structure. The proposed control methodology decides how to adjust the production rate in order to avoid system overloading and eliminate machine starvation or blocking. In this context, the local control law is synthesized by using the Takagi–Sugeno fuzzy systems. At the high level, a supervisory controller is designed to improve the overall system performances. A supervisor provides an additive component for each local controller when the overall system performances deviate from their acceptable domains (degraded mode). This is done by combining both local and global information into a unified formalism by using aggregation operators and according to fuzzy interval representation of the desired objectives. Finally, the feasibility of the proposed methodology is validated with simulation examples.     

Stability and performance analysis of mixed product run-to-run control

Run-to-run control has been widely used in batch manufacturing processes to reduce variations. However, in batch processes, many different products are fabricated on the same set of process tool with different recipes. Two intuitive ways of defining a control scheme for such a mixed production mode are (i) each run of different products is used to estimate a common tool disturbance parameter, i.e., a “tool-based” approach, (ii) only a single disturbance parameter that describe the combined effect of both tool and product is estimated by results of runs of a particular product on a specific tool, i.e., a “product-based” approach. In this study, a model two-product plant was developed to investigate the “tool-based” and “product-based” approaches. The closed-loop responses are derived analytically and control performances are evaluated. We found that a “tool-based” approach is unstable when the plant is non-stationary and the plant-model mismatches are different for different products. A “product-based” control is stable but its performance will be inferior to single product control when the drift is significant. While the controller for frequent products can be tuned in a similar manner as in single product control, a more active controller should be used for the infrequent products which experience a larger drift between runs. The results were substantiated for a larger system with multiple products, multiple plants and random production schedule.     

Stability and performance recovery within discretized non-linear control systems

In this paper, a closed-loop fast sampling analysis of a discretized (emulated) continuous-time controller in a sampled-data environment is presented. The analysis involves a general weighted version of the L_p-norm. This allows a qualitative and quantitative stability and performance analysis for an emulated controller. Several examples are used to show the relevance of these results for the analysis of sampled-data implementations and the computation of quantitative upper limits on the sampling period to achieve recovery of stability and performance.     

Robust control performance of time-varying human controller models

Previous models for human operator control performance have exhibited stochastic characteristics. Even with a repetitive task and a highly motivated and trained subject, there is clear variability of output (control actions). This variability, termed remnant, has been previously modeled as observation and motor noise associated with the operator. Recently, a new approach was taken: remnant was modeled as a time-varying operator characteristic associated with changes in the relative weighting of various performance measures, together with lack of understanding of the dynamics of the plant being controlled. The present work extends this concept and shows that the performance of time-varying human controller models is very robust with respect to changes in the time variability of the operator model. The results are particularly heartening in view of the inherent difficulties associated with modeling human control performance.     

提高分布式多预测控制器协调及稳定性的鲁棒区域控制

大型复杂化工程过程控制中,常规的集中式控制方式不利于实时性、灵活性和容错控制。而采用多预测控制器协调的分布式控制是解决这一问题的有效方法:。针对子系统间的动态耦合行为严重影响多预测控制器协调以及稳定性的问题,提出一种鲁棒区域控制策略。即在每个子系统的目标函数中加入松弛因子增加控制器间协调时的余量来达到分布式预测控制的稳定性。通过以反应器-存储器分馏器组成的过程为事例,仿真结果:表明该方法:的可行性和有效性。     

A multi-agent and cloud-edge orchestration framework of digital twin for distributed production control

The demands for mass individualization and networked collaborative manufacturing are increasing, bringing significant challenges to effectively organizing idle distributed manufacturing resources. To improve production efficiency and applicability in the distributed manufacturing environment, this paper proposes a multi-agent and cloud-edge orchestration framework for production control. A multi-agent system is established both at the cloud and the edge to achieve the operation mechanism of cloud-edge orchestration. By leveraging Digital Twin (DT) technology and Industrial Internet of Things (IIoT), real-time status data of the distributed manufacturing resources are collected and processed to perform the decision-making and manufacturing execution by the corresponding agent with permission. Based on the generated data of distributed shop floors and factories, the cloud production line model is established to support the optimal configuration of the distributed idle manufacturing resources by applying a systematic evaluation method and digital twin technology, which reflects the actual manufacturing scenario of the whole production process. In addition, a rescheduling decision prediction model for distributed control adjustment on the cloud is developed, which is driven by Convolutional Neural Network (CNN) combined with Bi-directional Long Short-Term Memory (BiLSTM) and attention mechanism. A self-adaptive strategy that makes the real-time exceptions results available on the cloud production line for holistic rescheduling decisions is brought to make the distributed manufacturing resources intelligent enough to address the influences of different degrees of exceptions at the edge. The applicability and efficiency of the proposed framework are verified through a design case.     

Stability robustness analysis of digital control systems in state-space models

The stability robustness of discrete linear time-invariant systems in state-space models is analysed. Based on a root-locus approach and from the stable-eigenvalue viewpoint, fundamental criteria for testing the stability robustness of autonomous systems are derived and applied to the robustness analysis of multivariable feedback systems. Both the norm bound and the element bounds for the allowed perturbations are obtained. A stability robustness index is denned which is useful both for the analysis and synthesis of control systems.     

Stability and performance improvement of extremum seeking control with sliding mode

Extremum seeking control is an adaptive control strategy for a system to iteratively extremize a function in real-time. In this paper we investigate stability and performance improvement by using sliding mode concepts in extremum seeking controllers.     

On the predictive performance of queueing network models for large-scale distributed transaction processing systems

Automated business processes running on distributed transaction processing (DTP) systems characterize the IT backbone of services industries. New web services standards such as BPEL have increased the importance of DTP systems in business practice. IT departments are forced to meet pre-defined quality-of-service metrics, therefore performance prediction is essential. Unfortunately, the complexity of multiple interacting services running on multiple hardware resources as well as the volatility in the demand for these services can make performance analysis extremely difficult. While business process automation has been a dominant topic in the recent years, surprisingly little has been published on performance modelling of large-scale DTP systems. In this paper, we will describe these systems with respect to the workloads and technical features, and compare the predictive accuracy of different types of queueing models and discrete event simulations experimentally. The experiments are based on two real-world DTP systems and respective data sets of a telecom company. Overall, we found that while the results for average utilization scenarios are quite similar, the effort to implement and run analytic solutions is much lower. As long as standard distributional assumptions of analytical models hold, they provide a reliable and fast methodology to explore different demand mix scenarios even for large-scale systems. The difficulty to estimate service and arrival time parameters and demand mix for the respective queueing network models can largely be reduced with appropriate tooling. Often, this information is missing in IT departments. Also, complex event conditions and error handling in DTP systems can make the analysis difficult. For many DTP applications, however, performance modelling could provide valuable decision support for service level management.