Fast state estimation subject to random data loss in discrete-time nonlinear stochastic systems

This paper focuses on the design of the standard observer in discrete-time nonlinear stochastic systems subject to random data loss. By the assumption that the system response is incrementally bounded, two sufficient conditions are subsequently derived that guarantee exponential mean-square stability and fast convergence of the estimation error for the problem at hand. An efficient algorithm is also presented to obtain the observer gain. Finally, the proposed methodology is employed for monitoring the Continuous Stirred Tank Reactor (CSTR) via a wireless communication network. The effectiveness of the designed observer is extensively assessed by using an experimental tested-bed that has been fabricated for performance evaluation of the over wireless-network estimation techniques under realistic radio channel conditions.     

A stochastic method for discrete and continuous optimization in manufacturing systems

Algorithms for discrete and continuous optimization are a very important part of decision making systems in manufacturing. Most planning, scheduling and layout problems require these algorithms. In practice, research into efficient algorithms meets two principal obstacles. The first one is linked to the fact that quite often the criteria cannot be expressed in an analytic form, so it is not possible to use existing theoretical resolution methods. The second is due to the fact that most of the problems for which the criteria can be represented in analytic form are NP-hard problems. This situation can be simplified using simulation. But the use of simulation and optimization methods together often gives a local optimum. The proposed method in this paper is based on the use of a discrete modification of -transforms jointly with some heuristics for local optimization. The originality of this approach is in the possibility to avoid a local optimum, while using models of simulation for the computation of values of the criteria. An example of the utilization of the method is given: it concerns the optimization of the launching of the parts in production in systems of the job-shop type. The proposed method is compared with a heuristic known to be very good in the same number of simulations. The results of five tests with different model sizes show the efficiency of the proposed method.     

Industrial Additive Manufacturing: A manufacturing systems perspective

As Additive Manufacturing becomes increasingly prevalent in commercial manufacturing environments, the need to effectively consider optimal strategies for management is increased. At present most research has focused on individual machines, yet there is a wealth of evidence to suggest competitive manufacturing is best managed from a systems perspective. Through 14 case studies developed with four long-established Additive Manufacturing companies this paper explores the conduct of Industrial AM in contemporary manufacturing environments. A multitude of activities, mechanisms, and controls are identified through this detailed investigation of Additive Manufacturing operations. Based on these empirical results a general four component Industrial Additive Manufacturing System is developed, together with the identification of potential strategic opportunities to enhance future manufacturing.     

Manufacturing systems: A knowledge- and optimization-based approach

Optimization approaches have traditionally been viewed as tools for solving manufacturing problems. The optimization approach is not suitable for many problems arising in modern manufacturing systems due to their complexity and involvement of qualitative factors. Expert systems appear to remedy the latter weakness of optimization approaches. The biggest disadvantage of expert systems in manufacturing environment is the slow response time. In this paper an integrand approach involving knowledge-based and optimization approaches is explored. The knowledge-and optimization-based approach is applied to solve two manufacturing problems: group technology (static problem) and scheduling (dynamic problem). The approach presented is illustrated with numerical example and computational results.The original version of this paper was presented at the 2nd International Symposium on Robotics and Manufacturing (ISRAM), Albuquerque, New Mexico 16–18 November 1988. The published proceedings of this meeting may be ordered from: CAD Laboratory for Systems/Robotics, EECE Dept, UNM, Albuquerque, NM 87131, U.S.A.     

随机状态空间系统的梯度优化辨识

提出了随机状态空间系统参数的梯度优化辨识方法。通过极小化输出预报误差而获得系统的参数估计。提出了动态选择雅可比矩阵奇异值比率确定参数搜索方向的方法,用以解决因雅可比矩阵的线性相关性引起的算法失效问题。给出了融合参数局部逼近性能信息的辨识算法,并得到了算法收敛速度的解析表达式。数值仿真实验的结果说明了所提出方法的有效性。     

Descriptor discrete-time systems with random abrupt changes: stability and stabilisation

This paper deals with the class of linear discrete-time systems with random abrupt changes also known as class of Markovian jump singular systems. The problems of stochastic stability and the stochastic stabilisation (using state-feedback control and static output feedback control) are tackled. Conditions in the LMI setting to design the appropriate gains of the controllers are developed. It is shown that all the addressed problems can be solved if the corresponding developed linear matrix inequalities (LMIs) are feasible. Numerical examples are employed to show the usefulness of the proposed results.     

Loading flexible manufacturing systems: a heuristic approach

In this study, heuristic algorithms are developed for loading Flexible Manufacturing Systems. The heuristic approach is an efficient way of planning the FMS with multiple nonlinear loading objectives.     

Computing the productivity of multistation serial manufacturing systems: A heuristics approach

Analytical models to evaluate the performance of multistation serial production systems are difficult to build and solve. Based on the insights obtained from a two-station continuous materials flow production line, a heuristic is developed to approximate the production rate of longer lines.     

A two phase optimization method for Petri net models of manufacturing systems

Optimization is a key issue in the design of large manufacturing systems. An adequate modeling formalism to express the intricate interleaving of competition and cooperation relationships is needed first. Moreover, robust and efficient optimization techniques are necessary. This paper presents an integrated tool for the automated optimization of DEDS, with application to manufacturing systems. After a very quick overview of optimization problems in manufacturing systems, it presents the integration of two existing tools for the modeling and evaluation with Petri nets and a general-purpose optimization package based on simulated annealing. The consideration of a cache and a two phase technique for optimization allows to speed-up the optimization by a factor of about 35. During the first preoptimization phase, a rough approximation of the optimal parameter set is computed based on performance bounds. Two application examples show the benefits of the proposed technique.     

Applications of queueing structures in manufacturing systems

This survey paper discusses and, to some extent, identifies manufacturing sub-systems that can be represented using accepted queueing models. Aggregate performance measures can be obtained through application of these models. Depending on the complexity of the congestion characteristics of the system, the standard results from queueing theory texts may yield guidelines for selection of optimal operational parameters.     

Constrained state estimation for stochastic jump systems: moving horizon approach

We discuss the state estimation advantages for a class of linear discrete-time stochastic jump systems, in which a Markov process governs the operation mode, and the state variables and disturbances are subject to inequality constraints. The horizon estimation approach addressed the constrained state estimation problem, and the Bayesian network technique solved the stochastic jump problem. The moving horizon state estimator designed in this paper can produce the constrained state estimates with a lower error covariance than under the unconstrained counterpart. This new estimation method is used in the design of the restricted state estimator for two practical applications.     

Distributed real-time embedded systems: Recent advances, future trends and their impact on manufacturing plant control

Real-time and embedded systems have historically been small scale. However, advances in microelectronics and software now allow embedded systems to be composed of a large set of processing elements, and the trend is towards significant enhanced functionality, complexity, and scalability, since those systems are increasingly being connected by wired and wireless networks to create large-scale distributed real-time embedded systems (DRES). Such embedded computing and information technologies have become at the same time an enabler for future manufacturing enterprises as well as a transformer of organizations and markets. This paper discusses opportunities for using recent advances in the DRES area in the deployment of intelligent, adaptive, and reconfigurable manufacturing plant control architectures.     

On stabilization for a class of nonlinear stochastic time-delay systems:a matrix inequality approach

This paper treats the feedback stabilization of nonlinear stochastic time-delay systems with state and control-dependent noise. Some locally (globally) robustly stabilizable conditions are given in terms of matrix inequalities that are independent of the delay size. When it is applied to linear stochastic time-delay systems, sufficient conditions for the state-feedback stabilization are presented via linear matrix inequalities. Several previous results are extended to more general systems with both state and control-dependent noise, and easy computation algorithms are also eiven.     

On stabilization for a class of nonlinear stochastic time-delay systems： a matrix inequality approach

This paper treats the feedback stabilization of nonlinear stochastic time-delay systems with state and control-dependent noise. Some locally （globally） robustly stabilizable conditions are given in terms of matrix inequalities that are independent of the delay size. When it is applied to linear stochastic time-delay systems, sufficient conditions for the state-feedback stabilization are presented via linear matrix inequalities. Several previous results are extended to more general systems with both state and control-dependent noise, and easy computation algorithms are also given.     

Networked iterative learning control approach for nonlinear systems with random communication delay

This paper constructs a proportional-type networked iterative learning control (NILC) scheme for a class of discrete-time nonlinear systems with the stochastic data communication delay within one operation duration and being subject to Bernoulli-type distribution. In the scheme, the communication delayed data is replaced by successfully captured one at the concurrent sampling moment of the latest iteration. The tracking performance of the addressed NILC algorithm is analysed by statistic technique in virtue of mathematical expectation. The analysis shows that, under certain conditions, the expectation of the tracking error measured in the form of 1-norm is asymptotically convergent to zero. Numerical experiments are carried out to illustrate the validity and effectiveness.     

Perturbation analysis for production control and optimization of manufacturing systems

We use stochastic fluid models (SFM) to capture the operation of threshold-based production control policies in manufacturing systems without resorting to detailed discrete event models. By applying infinitesimal perturbation analysis (IPA) to a SFM of a workcenter, we derive gradient estimators of throughput and buffer overflow metrics with respect to production control parameters. It is shown that these gradient estimators are unbiased and independent of distributional information of supply and service processes involved. In addition, based on the fact that they can be evaluated using data from the observed actual (discrete event) system, we use them as approximate gradient estimators in simple iterative schemes for adjusting thresholds (hedging points) on line seeking to optimize an objective function that trades off throughput and buffer overflow costs.     

Stability radii of infinite dimensional systems with stochastic uncertainty and their optimization

In this paper we consider infinite dimensional systems which are subjected to stochastic structured multiperturbations. We first characterize the stability radii of these systems in terms of a Lyapunov equation and the corresponding Lyapunov inequalities. Then we investigate the problem of maximizing the stability radius by linear state feedback. We show that the supremal achievable stability radius can be determined via the resolution of a parametrized Riccati equation. Illustrative examples are included. Copyright © 2006 John Wiley & Sons, Ltd.     

Machine planning for manufacturing: dynamic resource allocation and on-line supervisory control

In this paper*, we provide tools for integrating machine planning and manufacturing. Specifically, we show how assembly trees can be coded into operators for machine planners and how machine planners can represent flow-lines, assembly and job-shop choices. We provide a polynomial-time algorithm for succinctly combining multiple plans; the resulting plan can be expressed as four matrices that are equivalent to a Petri net. We also provide a dynamic supervisory controller that can execute a single plan or switch between multiple plans as real-time conditions change.     

Group decision making in manufacturing systems: An approach using spatial preference information and indifference zone

Decision making is an essential activity in manufacturing systems when designing production lines, scheduling, etc. Many decision making problems are characterized by multiple conflicting criteria and a large number of alternatives. For these complex decision making problems, it is rational to involve a group of decision makers (DM) for considering different aspects of the problem. This paper proposes an approach for supporting the decision making group to reduce disagreement in the group and obtain a common solution. The proposed approach allows the DMs to specify a region of acceptance, known as indifference zone, in the objective space as preference inputs. This makes the proposed approach applicable to problems with a large number of alternatives. The use of indifference zone concept captures the uncertain nature of preference articulation. Moreover, the indifference zone is shown beneficial in reducing the difficulty of reaching a group common solution. The properties of the proposed method are investigated analytically and with numerical experiments. Finally, the usefulness of the proposed method is shown by tackling a real-world packaging line configuration problem with a large alternative set.