Stochastic Discrete Event Dynamic Systems Research Articles

Performance Regulation via Integral Control in a Class of Stochastic Discrete Event Dynamic Systems

This paper presents a performance-regulation method for a class of Discrete Event Dynamic Systems (DEDS). The main idea is to use an integral controller with a variable gain, adaptively computed so as to guarantee effective output-tracking of a given reference value. The computation of the gain is based on the Infinitesimal Perturbation Analysis (IPA) gradient of the plant function with respect to the control variable, and the resultant tracking can be quite robust with respect to modeling inaccuracies and gradient-estimation errors. The proposed technique is tested on two examples concerning the regulation of the loss rate in a queue, and of inventory levels in a manufacturing-system represented by a Petri net. The results suggest its potential efficacy for a broader class of DEDS.

Design, simulation, and optimal control for the Fab 30 wafer fabrication facility

The paper introduces a methodology for the optimal control of stochastic discrete event dynamic systems (SDEDS). In this methodology, performance analysis (PA) techniques are used to design state observer of SDEDS. System identification, sensitivity analysis and predictive control techniques lead to an optimal control system design for SDEDS.

On-Line Control of Discrete Event Systems: Some Extensions of Perturbation Analysis

Standard optimization problems for stochastic Discrete Event Dynamic Systems (DEDS) are limited by the unavailability of closed-form expressions for sensitivity information (gradients or finite differences). We address the problem of estimating on line gradients to be used in conjunction with parameterized control policies and standard optimization schemes. By exploiting the flexibility provided by different sample path representations of the same underlying DEDS, we show that Perturbation Analysis techniques can be extended to more complicated queueing models than those analyzed thus far.