1、A fuzzy PLC with gain-scheduling control resolutionfor a thermal process - a case studyH.-X. Li*, S.K. TsoCenter for Intelligent Design, Automation and Manufacturing, Faculty of Science and Technology, City University of Hong Kong,Tat Chee A venue, Kowloon, Hong KongReceived 2 July 1998; accepted 6
2、November 1998AbstractThis paper presents a case study on the practical implementation of a fuzzy-PLC system for a thermal process. The theoretical study indicates that the inferior performance of fuzzy-controlled processes around a reference point is often caused by insufficient resolution of the fu
3、zzy inference. The limitations of ladder logic cannot support complex algorithms for resolution improvement. A simple gain adaptation method is presented here, to achieve smooth fuzzy control, that can be easily implemented in a PLC system. Real-time experiments on an unidentified thermal process sh
4、ow the effectiveness of the approach, as well as the robustness of the fuzzy controller with respect to the time-varying features of the process. (1999Elsevier Science td . All rights reserved. Keywords: Fuzzy control; Fuzzy-plc systems; Gain scheduling; Process control; Fuzzy sets1. IntroductionIn
5、industrial automation applications, ladder logic, a programming language running on the so-called programmable logic controllers (PLCs) (Erickson, 1996), is usually used for discrete event control. For continuous control, either bang bang-type control or PID-type controllers are more often employed.
6、 In 1974, the first fuzzy control application appeared (Mamdani, 1974). Since then, fuzzy-logic control (FLC) has been taken as the preferred method of designing controllers for dynamic systems, even where traditional methods can be used (Mamdani, 1993). In the early 1990s, when more and more succes
7、sful industrial automation applications were proving the potential of fuzzy logic, the fuzzy-PLC systems came on to the market. These systems tightly integrate fuzzy logicwith conventional industrial automation technologies. Many applications of fuzzy-PLC systems have been reported (Von Altrock and
8、Gebhardt, 1996).Thermal plants are very sensitive to environmental variations, and require highly robust performance for temperature control. Since the linear controller may not be robust enough with respect to the time-varying properties of the process, fuzzy-logic control (FLC) becomes a good cand
9、idate when a fuzzy-PLC system is available. On the other hand, FLC may have other problems that the linear controllers do not have. Practical experiments show inferior performance of FLC around the reference point, partially due to the complex resolution required for complex processes. A second set
10、of fine membership functions (MFs)/look-up tables, which can provide finer control, was used in some fuzzy systems to replace the coarse MFs/tables when the error falls within preset limits (Li and Lau, 1989; Liaw and Wang, 1991). However, this method is not applicable to fuzzy-PLC systems due to th
11、e complexity of the systems and the difficulties of tuning. A simple but effective method is required to improve the performance in practice.In this paper, a practical method is introduced, using gain scheduling. This approach can adapt to different resolution requirements by adjusting only the scal
12、ing gains. The method is effective, and can be easily implemented using ladder logic in the PLC. A properly designed fuzzy-PLC system is then very successful for controlling a thermal plant with time-varying features.2. The architecture of fuzzy-PLC systems and a problem descriptionThe architecture
13、of an OMRON fuzzy-PLC system can be seen in Fig. 1. The basic function modules are I/O, Fig. 1. Architecture of a basic fuzzy-PLC system.processor and fuzzy-logic inference. Fuzzy inference consists of several operations, as shown in Fig. 2: fuzzification, inference, and defuzzification. Though the
14、fuzzy-logic inference module on the PLC carries out the fuzzy-inference operation, a separate software tool on a PC programs the knowledge base required for the inference. This software tool is linked to the fuzzy-PLC system by a standard serial cable (RS232), through which the developer downloads t
15、he designed knowledge base to the fuzzy-PLC system. The fuzzy inference becomes a function to be called by the ladder logic when needed.A fuzzy variable is defined by a set of membership functions (MFs). The support for a given MF is the set of points in the region for which the grade k is positive.
16、 The resolution of each MF depends on the grade () distribution over its support. Since there is a crisp-fuzzy or fuzzy-crisp conversion, the resolution of the fuzzy inference depends heavily on the resolution of both the fuzzy input and output variables, while the resolution of a fuzzy variable depends on the MF design. Inappropriate MFs of a fuzzy variable may lose some input information, resulting in a poor resolutio
