Dual mode control strategy for the energy efficiency of complex and flexible manufacturing systems

The manufacturing industry is shifting towards smart manufacturing, in which both energy efficiency and flexibility are some of the main objectives of this digital transformation. In this regard, the control strategies for manufacturing systems should be able to support the requirements of this transformation with a low computational burden towards their implementation in real time. To this end, in this paper, a dual mode control strategy based on two control approaches is proposed to minimise the energy consumption of manufacturing systems without affecting their productivity, even when scenarios of flexible manufacturing are considered. The first control mode is based on model predictive control to determine an optimisation-based strategy for the constrained behaviour of the system. Then, the second mode builds on the assumption that the system exhibits a periodic behaviour and, thus, it will be able to switch to an autonomous control mode that avoids the resolution of an optimisation problem online. The proposed control strategy is tested in a manufacturing process line in which changes in the production programs are considered with the aim to test the performance in flexible manufacturing scenarios. The obtained results show that the computational burden could be significantly reduced while reducing global energy consumption without affecting the system productivity.     

基于自控网系统的混杂控制

混杂控制系统通常是复杂的非线性控制系统,难以用统一的模型进行建模. David和Alla提出的混杂Petri网,虽然可以解决一般混杂系统的建模问题,并得到了广泛的应用,但对于传统的如PID这类控制器,缺乏统一建模的能力.探讨了基于广义自控网系统的混杂控制方法,实现了对混杂控制器中监控器和数字控制器进行统一的Petri网建模.仿真实例设计了基于广义同步自控网系统的电加热炉控制系统,给出了可根据不同温度状态实施多种控制策略的变结构数字控制器模型,并详细分析了控制器的性能,证明了广义自控网系统具有十分强大的建模能力和广泛的应用前景.     

Assessment of non-centralised model predictive control techniques for electrical power networks

Model predictive control (MPC) is one of the few advanced control methodologies that have proven to be very successful in real-life applications. An attractive feature of MPC is its capability of explicitly taking state and input constraints into account. Recently, there has been an increasing interest in the usage of MPC schemes to control electrical power networks. The major obstacle for implementation lies in the large scale of these systems, which is prohibitive for a centralised approach. In this article, we therefore assess and compare the suitability of several non-centralised predictive control schemes for power balancing, to provide valuable insights that can contribute to the successful implementation of non-centralised MPC in the real-life electrical power system.     

Genetic synthesis of production-control systems for unreliable manufacturing systems with variable demands

The control of manufacturing systems with variable demands has attracted much research attention over the years. However, only limited results have been obtained due to the difficulty of this production-control problem. In this paper, genetically optimized short-run hedging points are used to construct gain-scheduled adaptive controllers for unreliable manufacturing systems with variable demands. The performance of such adaptive controllers is illustrated for unreliable systems subjected to piecewise-constant demands. It is demonstrated that the performance of these adaptive controllers is superior, in general, to that of genetically optimized non-adaptive controllers. However, such gain-scheduled adaptive controllers are designed for variable demands that are piecewise-constant. Therefore, in order to deal with more general classes of variable demands, a genetic rule-induction design methodology is used to synthesize robust fuzzy-logic controllers to provide automatic closed-loop control for unreliable manufacturing systems. Such robust fuzzy-logic controllers are shown to provide effective control for unreliable manufacturing systems with various kinds of variable demands.     

Synthesis of product-driven coordination controllers for a class of discrete-event manufacturing systems

A synthesis approach is proposed for discrete-event coordination architectures applied to a class of automated manufacturing systems (AMS) in which a clear separation is established between equipment control activities and product manufacturing procedures. Manufacturing procedures are modeled by regular languages constructed with a class of control commands named imperative. Equipment controllers are synthesized as a standard discrete-event supervisors dealing only with operational and safety issues of equipment groupings. The control of equipment modules is carried out following the imperative control commands sequences. Conditions are established to guarantee that the manufacturing procedure of a given product can be achieved using the synthesized supervisors in a particular AMS. Therefore, equipment controllers are not needed to be modified to consider the manufacturing of different products, whilst the construction of achievable manufacturing procedures becomes an “ad hoc” simple process using a reduced set of procedural blocks. The approach is illustrated with an experimental AMS.     

Agent-based distributed manufacturing control: A state-of-the-art survey

Manufacturing has faced significant changes during the last years, namely the move from a local economy towards a global and competitive economy, with markets demanding for highly customized products of high quality at lower costs, and with short life cycles. In this environment, manufacturing enterprises, to remain competitive, must respond closely to customer demands by improving their flexibility and agility, while maintaining their productivity and quality. Dynamic response to emergence is becoming a key issue in manufacturing field because traditional manufacturing control systems are built upon rigid control architectures, which cannot respond efficiently and effectively to dynamic change. In these circumstances, the current challenge is to develop manufacturing control systems that exhibit intelligence, robustness and adaptation to the environment changes and disturbances. The introduction of multi-agent systems and holonic manufacturing systems paradigms addresses these requirements, bringing the advantages of modularity, decentralization, autonomy, scalability and re-usability. This paper surveys the literature in manufacturing control systems using distributed artificial intelligence techniques, namely multi-agent systems and holonic manufacturing systems principles. The paper also discusses the reasons for the weak adoption of these approaches by industry and points out the challenges and research opportunities for the future.     

Implementation of real-time distributed control for discrete event robotic systems using Petri nets

This article presents a systematic method of modeling and implementing real-time control for discrete-event robotic systems using Petri nets. Because, in complex robotic systems such as flexible manufacturing systems, the controllers are distributed according to their physical structure, it is desirable to realize real-time distributed control. In this article, the task specification of robotic processes is represented as a system control-level net. Then, based on the hierarchical approach, it is transformed into detailed subnets, which are decomposed and distributed into the local machine controllers. The implementation of real-time distributed control through communication between the system controller and the machine controllers on a microcomputer network is described for a sample robotic system. The proposed implementation method is sufficiently general, and can be used as an effective prototyping tool for consistent modeling, simulation, and real-time control of large and complex robotic systems.     

Shaping probability density functions using a switching linear controller

Linear controllers for dynamic systems have been used in a stochastic context to decrease the variance of certain key variables. An extension is to use nonlinear controllers for shaping the entire probability density function (PDF). Typically, polynomial controllers are preferred due to their flexibility and also because their continuity makes them mathematically attractive. However, due to restrictions on stability, these controllers tend to be quasi-linear around the desired operating point. So, in this paper, a switching linear controller is presented as an alternative for PDF shaping. It is shown that, despite the discontinuity, an analytical solution of the PDF can be obtained using the Fokker-Planck-Kolmogorov equation. Also, for a simple case, even the Gram-Charlier coefficients can be computed analytically. Thus, it is demonstrated that the PDF shaping problem with a switching controller does not cause any further complications than using polynomial controllers. It is then shown in simulation that switching controllers are more robust and could create more skewness due to their discontinuity.     

Comparison between linear and nonlinear control strategies for variable speed wind turbines

The purpose of this work is to compare some linear and nonlinear control strategies, with the aim of benefiting as well as possible of wind energy conversion systems. Below rated wind speed, the main control objective is to perform an optimal wind power capture while avoiding strong loads on the drive train shafts. To explicitly take into consideration the low speed shaft flexibility, a two-mass nonlinear model of the wind turbine is used for controllers synthesis. After adapting a LQG controller based on the linearized model, nonlinear controllers based on a wind speed estimator are developed. They take into account the nonlinear dynamic aspect of the wind turbine and the turbulent nature of the wind. The controllers are validated upon an aeroelastic wind turbine simulator for a realistic wind speed profile. The study shows that nonlinear control strategies bring more performance in the exploitation of wind energy conversion systems.     

Optimal Threshold Levels in Stochastic Fluid Models via Simulation-based Optimization

A number of important problems in production and inventory control involve optimization of multiple threshold levels or hedging points. We address the problem of finding such levels in a stochastic system whose dynamics can be modelled using generalized semi-Markov processes (GSMP). The GSMP framework enables us to compute several performance measures and their sensitivities from a single simulation run for a general system with several states and fairly general state transitions. We then use a simulation-based optimization method, sample-path optimization, for finding optimal hedging points. We report numerical results for systems with more than twenty hedging points and service-level type probabilistic constraints. In these numerical studies, our method performed quite well on problems which are considered very difficult by current standards. Some applications falling into this framework include designing manufacturing flow controllers, using capacity options and subcontracting strategies, and coordinating production and marketing activities under demand uncertainty.     

The development of a program generator for programmable logic controllers

Programmable logic controllers (PLC) are very widely used for sequential logic control in manufacturing environments. PLCs serve to integrate materials handling and processes in automated manufacturing systems. Due to changing demands on manufacturing systems, frequently new control software needs to written and changes need to be made to existing control software. The productivity of such software development efforts can benefit from a software generation tool. We present an approach for the development of such a tool.     

Event-based broadcasting containment control for multi-agent systems under directed topology

The event-based broadcasting containment control problem for both first-order and second-order multi-agent systems under directed topology is investigated. Based on certain event, each agent decides when to transmit its current states to its neighbours and the agents’ distributed control algorithms are based on these sampled state measurements, which can significantly decrease the number of the controllers’ updates. All the agents are divided into two groups, namely, the leaders and the followers. The formation control is introduced. The leaders exchange their information to converge to a formation. The followers utilise the information from both their leader neighbours and their follower neighbours and are driven to the convex hull of the leaders using the proposed control algorithms. Numerical simulations are provided to illustrate the effectiveness of the obtained theoretical results.     

批次过程控制——回顾与展望

批次过程是一类重要的化工过程.因其本身的灵活性及高效性,被广泛应用于半导体制造、塑料加工、生物制药等领域.针对批次过程控制算法的研究也得到了大批学者的关注.在近三十年中,批次过程控制理论得到了长足的发展.但由于过程本身复杂的动态特性,以及对控制精度要求的提高,现有的理论和方法仍面临着挑战.本文从批次过程的特性出发,分析了算法设计的难点,对几种重要的控制算法进行总结分析,同时讨论了未来可能的发展方向.     

信标基底的可选择性在制造系统死锁控制中的应用

本文基于Petri网模型,讨论柔性制造系统的死锁控制问题.为了建立结构简单的Petri网控制器,本文在以前的工作中提出了信标基底的概念.信标基底是一组满足特定条件的严格极小信标集合.本文证明基于不同的信标基底,建立的受控系统其容许性能也不同.而容许性是评价死锁控制策略优劣的重要标准之一.故如何选择信标基底,提高受控系统的容许性能是值得研究的问题.本文讨论了使受控系统容许性能大大提高的信标基底的选择条件.基于该条件,为柔性制造系统建立有效的死锁控制策略.最后,通过两个例子解释该条件和策略.     

Cloud manufacturing: Strategic vision and state-of-the-art

Cloud manufacturing, a service oriented, customer centric, demand driven manufacturing model is explored in both its possible future and current states. A unique strategic vision for the field is documented, and the current state of technology is presented from both industry and academic viewpoints. Key commercial implementations are presented, along with the state of research in fields critical to enablement of cloud manufacturing, including but not limited to automation, industrial control systems, service composition, flexibility, business models, and proposed implementation models and architectures. Comparison of the strategic vision and current state leads to suggestions for future work, including research in the areas of high speed, long distance industrial control systems, flexibility enablement, business models, cloud computing applications in manufacturing, and prominent implementation architectures.     

Genetic algorithm design of neural network and fuzzy logic controllers

This paper discusses the design of neural network and fuzzy logic controllers using genetic algorithms, for real-time control of flows in sewerage networks. The soft controllers operate in a critical control range, with a simple set-point strategy governing “easy” cases. The genetic algorithm designs controllers and set-points by repeated application of a simulator. A comparison between neural network, fuzzy logic and benchmark controller performance is presented. Global and local control strategies are compared. Methods to reduce execution time of the genetic algorithm, including the use of a Tabu algorithm for training data selection, are also discussed. The results indicate that local control is superior to global control, and that the genetic algorithm design of soft controllers is feasible even for complex flow systems of a realistic scale. Neural network and fuzzy logic controllers have comparable performance, although neural networks can be successfully optimised more consistently.     

On discrete dynamic output feedback min-max controllers

The paper considers output feedback min-max controllers for non-square discrete time uncertain linear systems. Based on previous work, it is demonstrated that static output feedback min-max controllers are only realizable for a specific class of systems. To broaden this class, a compensator based framework is proposed to introduce additional degrees of freedom. The conditions for the existence of such dynamic output feedback min-max controllers are given and are shown to be relatively mild. Furthermore, a simple parameterization of the available design freedom is proposed. An explicit procedure is described which shows how a Lyapunov matrix, which satisfies both a discrete Riccati inequality and a structural constraint, can be obtained using Linear matrix inequality optimization. This Lyapunov matrix is used to calculate the robustness bounds associated with the closed-loop system. A simple aircraft example is provided to demonstrate the efficacy of the design approach.     

Stabilizing low complexity feedback control of constrained piecewise affine systems

Piecewise affine (PWA) systems are powerful models for describing both non-linear and hybrid systems. One of the key problems in controlling these systems is the inherent computational complexity of controller synthesis and analysis, especially if constraints on states and inputs are present. In addition, few results are available which address the issue of computing stabilizing controllers for PWA systems without placing constraints on the location of the origin.This paper first introduces a method to obtain stability guarantees for receding horizon control of discrete-time PWA systems. Based on this result, two algorithms which provide low complexity state feedback controllers are introduced. Specifically, we demonstrate how multi-parametric programming can be used to obtain minimum-time controllers, i.e., controllers which drive the state into a pre-specified target set in minimum time. In a second segment, we show how controllers of even lower complexity can be obtained by separately dealing with constraint satisfaction and stability properties. To this end, we introduce a method to compute PWA Lyapunov functions for discrete-time PWA systems via linear programming. Finally, we report results of an extensive case study which justify our claims of complexity reduction.     

An object-oriented model for FMS control

The flexible manufacturing system (FMS) is a distributed network of heterogeneous programmable manufacturing machinery, such as assembly lines and numerically controlled machines. Despite these interconnected, programmable hardware elements, the success of building a truly flexible manufacturing system has been limited so far, owing to the lack of flexibility in its control software layer. In integrating heterogeneous machinery, many existing FMS control software systems depend structurally on specific machinery and job-scheduling strategies, and thus it is difficult to incorporate new developments in FMS organization and operational requirements. In searching for an open architecture for the FMS control software system, this paper presents an object-oriented FMS data model. Among others, it represents each physical cluster of related machinery (called a flexible manufacturing cell) as an object. To facilitate the integration of heterogeneous physical cells, such cell objects share a common protocol of interacting with the main control process through inheritance from the abstract cell class. Other related physical and abstract entities in FMS are also modelled as objects, with their similarity and difference captured in inheritance hierarchies. To verify the proposed approach experimentally, a prototype FMS control software system named FREE (FMS Runtime Executive Environment) has been implemented on top of a commercial object-oriented database system.     

Model‐free adaptive formation control for unknown multiinput‐multioutput nonlinear heterogeneous discrete‐time multiagent systems with bounded disturbance

Based on the model‐free adaptive control, the distributed formation control problem is investigated for a class of unknown heterogeneous nonlinear discrete‐time multiagent systems with bounded disturbance. Two equivalent data models to the unknown multiagent systems are established through the dynamic linearization technique considering the circumstances with measurable and unmeasurable disturbances. Based on the obtained data models, two distributed controllers are designed with only using the input/output and disturbance data of the neighbor agents system. The tracking error of the closed‐loop system driven by the proposed controllers is shown to be bounded by the contraction mapping principle and inductive methods. An example illustrates the effectiveness of the proposed two distributed controllers.