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.     

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.     

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.     

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.     

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.     

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.     

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.     

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.     

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.     

Multi-objective optimization of product variety and manufacturing complexity in mixed-model assembly systems

Product variety has increased dramatically as manufacturers compete for market shares. While higher variety of products may satisfy a broader range of customers, it also introduces complexity in manufacturing. A multi-objective optimization approach is proposed to balance product variety and manufacturing complexity when designing a product family and the mixed-model assembly system. Relative complexity is introduced to measure the complexity and to find the best set of product variants to be offered while balancing market share and complexity. Numerical examples are provided to demonstrate the approach.     

A homogeneous domination approach to state feedback of stochastic high-order feedforward nonlinear systems

This paper investigates stochastic high-order feedforward nonlinear systems for the first time. By introducing the homogeneous domination approach and solving several troublesome obstacles in the design and analysis procedure, a state feedback controller is constructed to render the closed-loop system globally asymptotically stable in probability.     

Mean-square filter design for stochastic polynomial systems with Gaussian and Poisson noises

This paper addresses the mean-square finite-dimensional filtering problem for polynomial system states with both, Gaussian and Poisson, white noises over linear observations. A constructive procedure is established to design the mean-square filtering equations for system states described by polynomial equations of an arbitrary finite degree. An explicit closed form of the designed filter is obtained in case of a third-order polynomial system. The theoretical result is complemented with an illustrative example verifying performance of the designed filter.     

Stability of impulsive stochastic functional differential systems in terms of two measures via comparison approach

Comparison principles for general impulsive stochastic functional differential systems are established.Employing the comparison principles and the theory of differential inequalities,stability and instability,involving two measures,of impulsive stochastic functional differential systems are investigated.Several stability and instability criteria are obtained,and two examples are also given to illustrate our results.     

Modelling manufacturing systems: A birth-death approach

An approximation method for modelling a manufacturing system is introduced. The system is considered as a queueing network, where each queue is limited in size, and interarrival and processing times are exponentially distributed. The birth-death approach is considered and an approximation method to reduce the dimension of the model is developed. The results are the marginal probability distribution of the number of units in each queue; other performance indices, such as mean queue lengths, utilizations of the working stations, and throughput can be easily obtained. The general procedure is applied to model, for example, queues in tandem, a split node, and a more complex network of queues. Simulation and, when possible, comparison with the exact solution show an acceptable error level of the proposed method.     

Parameter optimization of stochastic automata operating in random environments

Two methods of determining the lower bounds of the rate of convergence of finite stochastic automata are presented. The rate of convergence, defined as the percentage decrease in the distance between the transient probability distribution and the equilibrium probability distribution in each step, is determined as a function of the probability transition matrix. Formulas for parameter optimization for a class of stochastic automata for fast convergence and maximum expediency are derived and illustrative examples of fourth-order systems are given.     

Simulated annealing with auxiliary knowledge for process planning optimization in reconfigurable manufacturing

In this paper, three simulated annealing based algorithms that exploit auxiliary knowledge in different ways are devised and employed to handle a manufacturing process planning problem for reconfigurable manufacturing. These algorithms are configured based on a generic combination of the simulated annealing technique with; (a) heuristic knowledge, and (b) metaknowledge. Capabilities of the implemented algorithms are tested and their performances compared against a basic simulated annealing algorithm. Computational and optimization performances of the implemented algorithms are investigated and analyzed for two problem sizes. Each problem size consists of five different forms of a manufacturing process planning problem. The five forms are differentiated by five alternative objective functions. Experimental results show that the implemented simulated annealing algorithms are able to converge to good solutions in reasonable time. A computational analysis indicates that significant improvements towards a better optimal solution can be gained by implementing simulated annealing based algorithms that are supported by auxiliary knowledge.