After implementing the fuzzy PLC control system for the servomechanism, the results in figure 16 were obtained。 The speed reference was set at 300 rpm and different loads were applied (at 20, 30 and 40 seconds)。 

Fig。 16: System response for a set speed reference with different loads 

5。 CONCLUSIONS 

In fuzzy control systems, the existing knowledge gained from experience with the behaviour of the process is replaced by rules that qualitatively describe the process behaviour。   

The parameters of the controller were designed so that the servomechanism had a fast response and minimum steady state error。 The disturbances (different loads applied to the motor) were rejected by this type of control。 

The designed system, fuzzy-PLC connects classic control with modern technology, supporting a wider spectrum of PLC (fuzzy logic control) applications。

REFERENCES 

Amira, DR300 – Laboratory Setup Speed Control with Variable Load, User Manual。 

Cemal Yilmaz, Osman Gürdal, Ilhan Kosalay, Network induced delay of asynchronous motor connected to Profibus-DP networks using fuzzy logic control algorithm, Expert Systems with Applications 37 (2010) 3248–3255。 

Dumitru I。, Arghira N。, Fagarasan I。, Iliescu S。, Process Control Simulator using  Programmable Controller Technology, Proceedings of the 2010 IEEE 

International Conference on Automation, Quality and Testing, Robotics17th edition of , vol。 1, pp 89-93 , ISBN 978-1-4244-6722-8/10, 28-30 May 2010, Cluj, Romania 

H。-X。 Li, S。K。 Tso, A fuzzy PLC with gain-scheduling control resolution for a thermal process - a case study, Control Engineering Practice 7 (1999) 523-529。 

Ivanescu, N。, T。 Borangiu, S。 Brotac, “Automate programabile。 Teorie si probleme rezolvate”, Editura Printech, 2002 

Jirka Roubal, Petr Augusta, Vladimr Havlena, Jindrich Fuka, Control Design for servo amira DR300, Department of Control Engineering, Faculty of Electrical Engineering, 

Czech Technical University in Prague, 2005 

Luis J。 de Miguel, L。 Felipe Blazquez, Fuzzy logic-based    decision-making for fault diagnosis in a DC motor, Engineering Applications of Artificial Intelligence 18 (2005) 423–450。 

Mamdani, E。H。 (1974)。 Applications of fuzzy algorithms for simple dynamic plant, Proc。 IEE, 121(12), 1585-1588。 

Mamdani, E。H。 (1993)。 Twenty years of fuzzy control: Experiences gained and lessons learnt。 Proc。 2nd IEEE Int。 Conf。 on Fuzzy Systems, San Francisco (pp。 339344)。 

Petr Chalupa, Vladimír Bobál , Jakub Novák, Petr Dostál, Using of Self-Tuning Controllers Simulink Library for real-time control of nonliniar servo systems, Proceedings 23rd European Conference on Modelling and Simulation, ISBN: 978-0-9553018-8-9 / ISBN: 9780-9553018-9-6。 

Siemens, Fuzzy Control++, User’’s Manual。 

Siemens – Programmable Logic Controllers S7-300 Module Data, 2004 

Yasar Birbir, H。 Selcuk Nogay, Design and Implementation of PLC-Based Monitoring Control System for ThreePhase Induction Motors Fed by PWM Inverter, International Journal of Systems Applications, Engineering & Developement, Issue 3, Volume 2, 2008。 

伺服机构模糊 PLC 控制系统

摘要 

本文案例研究模糊 PLC 系统的伺服机构的实际执行，提出控制方案以达到平滑的模糊控制，可以在 PLC 系统中很容易实现对伺服系统的速度控制。

关键词 ︰ 模糊控制； 可编程逻辑控制器 (PLC)； 模糊 PLC 系统； 过程控制；计算机控制系统

1。介绍

近年来，我们推动了工业与信息技术的迅速变化。如今，通过使用计算机能执行对大多数设备的控制。大多数设备使用可编程逻辑控制器 (PLC) 与计算机连接并监视每个负载和用电设备。因为他们易于安装的特性和非常灵活的应用程序，PLC被 广泛应用于工业控制中。PLC通过其输入和输出接口与外部世界进行交互。