Web-based distributed manufacturing control systems

With the evolution of the application of web technology, agent-based manufacturing systems can be both easily implemented and become a potential approach for modern manufacturing enterprises. In this paper, we present problems and models required for the development of web-based distributed manufacturing control systems. The specific objectives are: (1) to propose an appropriate architecture for a web-based distributed manufacturing control system, (2) to define the specification of a web-based cell controller, (3) to design the co-operative mechanisms, contract net protocol, for better system performance, (4) to upgrade the communication capability of existing CNC machines from RS232C to TCP/IP (Transmission Control Protocol/Internet Protocol) based on Ethernet .To demonstrate the proposed development, prototype software based on ASP and VB programming was designed and implemented. As a result, not only is a great amount of time saved in the development of web-based manufacturing control software, but this development can also enhance the competitive capability of companies trying to operate on a flexible basis.     

开放式集散控制—GDUTAA系统的研究

开放式集散控制－－GDUTAA系统的研究,是以μXL中小型集散控制为基础,通过μXL的操作站与上位PC机相连接,使得我们既能充分利用μXL原有的各种,又能利用上位PC机进一步开发各种新的控制算法,如：神经网络、智能控制、专家系统等,并在μXL上加以实施,为我们研究新的控制理论和控制算法提供了一个开放式的实验平台,同时为拥有μXL系统的各工矿企业提供了一个技术更新的方向。     

Robust control of saturating systems with Markovian packet dropouts under distributed MPC

In this work, a robust control methodology is presented for saturating systems with packet dropouts under distributed model predictive control framework. The sequence of time instants when data dropout happens is modeled by a Markov chain. A packet dropout compensation strategy and an augmented Markov jump linear model are considered simultaneously. To design distributed model predictive controllers, the entire system is decomposed into coupled subsystems. Considering the influences of neighbor subsystems, a distributed predictive control synthesis involving packet dropouts and Markovian probabilities is developed by minimizing the worst-case performance index at each time instant. The input saturation constraints are also incorporated into the robust controller design under distributed model predictive control framework. Furthermore, both the recursive feasibility of the proposed robust control under distributed model predictive control and the closed-loop mean-square stability are proved. To show the effectiveness, the proposed methodology is validated by simulations on a continuous stirred tank reactor process and a DC control system.     

Sampled-data control for linear time-delay distributed parameter systems

Some real systems have spatiotemporal dynamics and are time-delay distributed parameter systems (DPSs). The existence of time-delay may lead to system instability. The analysis and design of DPSs with time-delay is essentially more complicated. To take into account the factor of time-delay and fully enjoy the benefits of the digital technology in control engineering, it is a theoretical and practical value to consider the sampled-data control (SDC) problem of DPSs with time-delay. However, there are few attempts to solve the SDC problem of time-delay DPSs. In this paper, we introduce a SDC for linear time-delay DPSs described by parabolic partial differential equations (PDEs). A SDC design is developed in the formulation of spatial linear matrix inequalities (LMIs) by constructing an appropriate Lyapunov functional, which can stabilize exponentially the time-delay DPSs. This stabilization condition can be applied to either slowing-varying time delay or fast-varying one. Finally, simulation results of a numerical example are provided to illustrate the effectiveness of the proposed method.     

On the control of distributed parameter systems using a multidimensional systems setting

The unique characteristic of a repetitive process is a series of sweeps, termed passes, through a set of dynamics defined over a finite duration with resetting before the start of the each new one. On each pass an output, termed the pass profile is produced which acts as a forcing function on, and hence contributes to, the dynamics of the next pass profile. This leads to the possibility that the output, i.e. the sequence of pass profiles, will contain oscillations which increase in amplitude in the pass-to-pass direction. Such behavior cannot be controlled by standard linear systems approach and instead they must be treated as a multidimensional system, i.e. information propagation in more than one independent direction. Physical examples of such processes include long-wall coal cutting and metal rolling. In this paper, stability analysis and control systems design algorithms are developed for a model where a plane, or rectangle, of information is propagated in the pass-to-pass direction. The possible use of these in the control of distributed parameter systems is then described using a fourth-order wavefront equation.     

An efficient numerical scheme for solving fractional infinite-horizon optimal control problems

This paper presents an efficient numerical method to solve fractional infinite-horizon optimal control problems, where the dynamic control system depends on Caputo fractional derivatives. First, by a suitable change of variable, we transform the fractional infinite-horizon optimal control problem to a finite-horizon one. Then, with the help of an approximation, we replace the Caputo derivative to integer order derivative. According to the Pontryagin minimum principle (PMP) for optimal control problems and by constructing an error function, we define an unconstrained minimization problem. In the optimization problem, we use trial solutions for state, costate and control functions where these trial solutions are constructed by using two-layered perceptron neural network. Some numerical results are introduced to explain our main results.     

Modified particle swarm optimization for solving machine-loading problems in flexible manufacturing systems

In flexible manufacturing systems (FMSs), the loading problem is considered as a vital pre-release decision because its operational effectiveness largely depends on a good quality solution to the loading problem. Difficulties arise in obtaining optimal solutions to such problems because of its combinatorial and NP-hard nature. In the past, numerous techniques have been suggested and found to be efficient, but they take long computational times when the problem size increases. In order to address the above issues, a meta-heuristic approach based on particle swarm optimization (PSO) has been proposed in this paper to improve the solution quality and reduce the computational effort. However, PSO has the tendency to suffer from premature convergence. Therefore, the PSO algorithm has been modified through the introduction of a mutation operator to improve efficiency of the algorithm. The proposed algorithm attempts to minimize the system unbalance while satisfying the technological constraints, such as the availability of machining time and tool slots. The proposed algorithm produces promising results in comparison to existing methods for ten benchmark instances available in the FMS literature.     

Stabilizing model predictive control for constrained nonlinear distributed delay systems

In this paper, a model predictive control scheme with guaranteed closed-loop asymptotic stability is proposed for a class of constrained nonlinear time-delay systems with discrete and distributed delays. A suitable terminal cost functional and also an appropriate terminal region are utilized to achieve asymptotic stability. To determine the terminal cost, a locally asymptotically stabilizing controller is designed and an appropriate Lyapunov-Krasoskii functional of the locally stabilized system is employed as the terminal cost. Furthermore, an invariant set for locally stabilized system which is established by using the Razumikhin Theorem is used as the terminal region. Simple conditions are derived to obtain terminal cost and terminal region in terms of Bilinear Matrix Inequalities. The method is illustrated by a numerical example.     

分布式系统负载均衡中基于模糊控制的参数自适应调整算法

目前在分布式系统中已有多种负载均衡系统,它们各自采用不同的策略,并具有特定的适用环境,但是都缺乏通用性,自适应性不足.针对本地代理收集负载信息的时间间隔这个参数,本文提出并实现了基于模糊控制的参数自适应调整算法TPAFC.该算法利用模糊控制技术,对测量时间间隔参数按照负载状态的变化率进行自适应调整,在节约额外开销的前提下,提高了负载测试的准确性.实验证明,与同类算法(固定时间间隔)相比,特别是在重负载情况下,TPAFC算法的效率更高.     

A new method of solving complex variational problems: Examples of the determination of broaching parameters

Examples of the determination of the basic broaching parameters are presented. Comparison of the results with experimental data confirms the precision of the proposed theory.     

Delay-dependent fuzzy static output feedback control for discrete-time fuzzy stochastic systems with distributed time-varying delays

This paper is concerned with the delay-dependent H(∞) fuzzy static output feedback control scheme for discrete-time Takagi-Sugeno (T-S) fuzzy stochastic systems with distributed time-varying delays. To begin with, the T-S fuzzy stochastic system is transformed to an equivalent switching fuzzy stochastic system. Then, based on novel matrix decoupling technique, improved free-weighting matrix technique and piecewise Lyapunov-Krasovskii function (PLKF), a new delay-dependent H(∞) fuzzy static output feedback controller design approach is first derived for the switching fuzzy stochastic system. Some drawbacks existing in the previous papers such as matrix equalities constraint, coordinate transformation, the same output matrices, diagonal structure constraint on Lyapunov matrices and BMI problem have been eliminated. Since only a set of LMIs is involved, the controller parameters can be solved directly by the Matlab LMI toolbox. Finally, two examples are provided to illustrate the validity of the proposed method.     

新一代中药自动配药控制系统中关键技术的研究

在提出新一代中药自动配药系统组成框架的基础上，着重研究了在控制系统研制中遇到的关键问题－制约配药工作效率的集中料斗的取药规划和基于计算机网络的分层分布式计算机－PLC控制系统设计，为成功研制新一代中药系统奠定了坚实的理论基础。     

Event-triggered distributed receding horizon control for tracking and formation of homogeneous multi-agent systems

This paper proposes an event-triggered distributed receding horizon control (DRHC) approach for the formation and tracking problems of homogeneous multi-agent systems. For each agent, an event-triggering condition, based on assumed predictive information of the neighbours, is derived from stability analysis. Considering the uncertain deviation between the assumed and true predictive information, we design a time-varying compatibility constraint for the individual optimization problem. In the event-triggered DRHC algorithm, each agent solves the optimization problem and communicates with its neighbours only when the event-triggering condition is satisfied, so the communication and computation burden are reduced. Moreover, guarantees for the recursive feasibility and asymptotic stability of the overall system are proved. A simulation example is provided to illustrate effectiveness of the proposed approach.     

A multi-objective integrated cellular manufacturing systems design with dynamic system reconfiguration

Cellular manufacturing system (CMS)—an important application of group technology—has been recognized as an effective way to enhance the productivity in a factory. As a result of dynamic deterministic demands within the planning horizon, a CMS configuration for a period might not be optimal or even feasible for other planning periods. Consequently, a multi-objective dynamic cell formation problem is presented, where the total cells load variation and sum of the miscellaneous costs are to be minimized simultaneously. The second objective function calculates machine costs, internal part production, intercellular and intracellular material handling, back order, inventory holding and subcontracting. Since in this type of problem, objectives are in conflict with each other, finding an ideal solution (a solution which satisfies all objectives simultaneously) is not possible. Therefore a non-dominated sorting genetic algorithm (NSGAII) is designed for finding Pareto-optimal frontier that decision maker can select her/his slightly solution. Numerical examples have been solved for demonstrate the efficiency of the proposed algorithm.     

多目标动态车间设施布局研究综述

车间设施布局问题是生产系统的重要问题。大规模定制生产模式对车间设施布局问题提出了动态性、多目标等方面的需求。在梳理相关文献的基础上,总结了多目标动态车间设施布局的特征,指出了图论、二次分配、混合整数三种基本数学模型在多目标动态车间设施布局方面的不足,分析了遗传算法、模拟退火算法、禁忌搜索算法和蚁群优化算法四种元启发式算法的适用性,最后指出了多目标动态车间设施布局的研究趋势。     

Robust output feedback distributed model predictive control of networked systems with communication delays in the presence of disturbance

In this work, an output feedback cooperative distributed model predictive control is developed for a class of networked systems composed of interacting subsystems interconnected through their states, in which it handles bounded disturbances and time varying communication delays. A distributed buffer based prediction strategy is used to compensate bounded delays and predict those states, which are coupled between subsystems that their actual values may not available due to delays. In the design of robust distributed model predictive control, distributed moving horizon estimation is employed so that convergence and boundedness of the estimation error are ensured. Furthermore, robust exponential stability of the closed loop system is established. The effectiveness of the proposed method is illustrated using two interconnected continuous stirred tank reactors.