A new adaptive fuzzy control policy against conventional methods. Statistical analysis of real time control performance

Author(s):  
Rafik Lasri ◽  
Ignacio Rojas ◽  
Hector Pomares ◽  
Olga Valenzuela
2009 ◽  
Vol 407-408 ◽  
pp. 117-121
Author(s):  
Ji Ming Chen ◽  
Yong Hong Liu ◽  
Rong Shen ◽  
Shi Peng Wu

EDM is a complex time-varying system, and its online parameters have a great influence on processing stability. In order to overcome the difficulty of EDM online parameters control, this paper designed a kind of intelligent controller, based on fuzzy control and ANN technology, to implement real-time control of EDM online parameters. The simulation results show that this controller can improve machining efficiency and enhance processing stability.


2019 ◽  
pp. 850-886
Author(s):  
Yan Qiao ◽  
NaiQi Wu ◽  
MengChu Zhou

In semiconductor manufacturing, when a wafer is processed, it requires unloading from its process module in a given time interval, otherwise it is scraped. This requirement is called wafer residency time constraints. Thus, it is crucial to schedule a cluster tool such that the wafer sojourn time in a process module is within a given time window to satisfy the wafer residency time constraints. Besides wafer residency time constraints, in a cluster tool, the activity time is subject to variation. The activity time variation can make a feasible schedule obtained under the assumption of deterministic activity times become infeasible. To solve this problem, it is important to reveal the wafer sojourn time fluctuations with bounded activity time variation. Such an issue is addressed in this chapter for single-arm cluster tools. A single-arm cluster tool is modeled by a resource-oriented Petri net to describe the wafer fabrication processes. Based on it, a real-time control policy is proposed such that it offsets the effect of the activity time variation as much as possible. Then, the wafer sojourn time delay in a process module is analyzed and analytical expressions are derived to calculate the upper bound. With the help of the real-time control policy and wafer sojourn time delay analysis results, schedulability conditions and scheduling algorithms for an off-line schedule are presented in this chapter. The schedulability conditions can be analytically checked. If schedulable, an off-line schedule can be analytically found. The off-line schedule together with the real-time control policy forms the real-time schedule for the system. It is optimal in terms of cycle time minimization. Examples are given to show the application of the proposed approach.


Author(s):  
Wei Zheng ◽  
Yong Lei ◽  
Qing Chang

It is attractive to reduce the total cost of a manufacture system with real-time control of the production. The total cost mainly consists of the production cost, the penalty of the permanent production loss, and the Work-In-Process (WIP) inventory level cost. However, it is difficult to derive an analytical model of manufacture system due to the complexity of starved and blocked phenomena, the random failure and maintenance processes. Therefore, finding a real-time control policy for the manufacture system without exact analytical model is dearly needed. In this paper, a novel reinforcement learning based control decision policy is proposed based on the action of switching the machines on or off at the start of each time slot. Firstly, a simulation model is developed with MTBF and MTTR evaluated from the history data to collect samples. Then, a reinforcement learning method, specifically, Least-Square-Policy-Iteration method, is applied to obtain a sub-optimal policy. The simulation results show that the proposed method performs well in reducing the total cost.


2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
R. Lasri ◽  
I. Rojas ◽  
H. Pomares ◽  
O. Valenzuela

The main objective of this paper is to prove the great advantage that brings our novel approach to the intelligent control area. A set of various types of intelligent controllers have been designed to control the temperature of a room in a real-time control process in order to compare the obtained results with each other. Through a training board that allows us to control the temperature, all the used algorithms should present their best performances in this control process; therefore, our self-organized and online adaptive fuzzy logic controller (FLC) will be required to present great improvements in the control task and a real high control performance. Simulation results can show clearly that the new approach presented and tested in this work is very efficient. Thus, our adaptive and self-organizing FLC presents the best accuracy compared with the remaining used controllers, and, besides that, it can guarantee an important reduction of the power consumption during the control process.


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