This paper discusses an optimal scheduling problem for a three-machine robotic cell with finite buffer for WIP's (Work-In-Process) such as FMC's (Flexible Manufacturing Cells), where jobs are processed on three machines in the same order, and sent between machines by a transportation robot. The objective is to find an optimal schedule that minimizes the maximum completion time (i.e., the makespan). In this paper we propose heuristic algorithms employing a fuzzy inference and tabu search technique. Numerical experiments show that the proposed heuristics give good approximate schedules for any size of buffer capacity.
In batch manufacturing information management systems, software productivity is a big problem because it is difficult to provide commonly-utilized software layer due to differences of plant facilities and production processes among target plants. In this paper, we present a model of a batch tracing mechanism and a view generation mechanism which can be utilized as a software layer organizing a batch manufacturing information management system. In addition, we confirmed that an application of this software layer for a model plant can be performed in short time and run-time performance is good enough for a practical use.
This paper considers an identification and design of temperature control system for a coke oven plant. We first develop a linear state-space model by identifying two ARX models based on the input-output data of the plant. Then we derive a digital tracking control system with feedforward and preview actions by applying the techniques of Refs. 2) and 3). The tracking performance of the designed control algorithm is evaluated by extensive simulation studies. It is shown that the control algorithm with the preview actions on the coal charge and reference temperature has a potential applicability for automating the operation of coke oven plant.
A major, and yet unresolved, problem has been the choice of the step-size in some parameter tracking algorithms. This paper presents an adaptive setting method of step-size parameter for tracking time-varying parameters when normalized least mean square (NLMS) algorithm is used. The usual method suggested by Benveniste et al is to adjust the step-size so as to minimize the performance measure defined by the mean squares of prediction error. The weak point of this method is that the performance measure converges only on a local minimum. The main object of this paper is to give a solution for this problem. The solution obtained is that the performance measure converges on the global minimum through the minimization of another performance measure. As a result, the proposed algorithm becomes asymptotically optimal. Numerical examples indicate acceptable performance of the proposed method.
In this paper, we apply H∞ control theory to the level-control of a continuous casting machine. The level-control has two major problems of parameter variations and disturbance-pattern variations. In order to cope with these problems, we make up a table which consists of multiple H∞ controllers. By switching one H∞ controller to another properly, adaptability as well as robustness is achieved. This method has been applied to an actual machine and the results show a 40% decrease in level error.