Location of Repository

In the chemical industry, the control of pH is a well-known problem that presents\ud difficulties due to the large variations in its process dynamics and the static nonlinearity\ud between pH and concentration. pH control requires the application of advanced control\ud techniques such as linear or nonlinear adaptive control methods. Unfortunately, adaptive\ud controllers rely on a mathematical model of the process being controlled, the parameters\ud being determined or modified in real time. Because of its characteristics, the pH control\ud process is extremely difficult to model accurately.\ud Fuzzy logic, which is derived from Zadeh's theory of fuzzy sets and algorithms,\ud provides an effective means of capturing the approximate, inexact nature of the physical\ud world. It can be used to convert a linguistic control strategy based on expert knowledge,\ud into an automatic control strategy to control a system in the absence of an exact\ud mathematical model. The work described in this thesis sets out to investigate the\ud suitability of fuzzy techniques for the control of pH within a continuous flow titration\ud process.\ud Initially, a simple fuzzy development system was designed and used to produce an\ud experimental fuzzy control program. A detailed study was then performed on the\ud relationship between fuzzy decision table scaling factors and the control constants of a\ud digital PI controller. Equation derived from this study were then confirmed\ud experimentally using an analogue simulation of a first order plant. As a result of this\ud work a novel method of tuning a fuzzy controller by adjusting its scaling factors, was\ud derived. This technique was then used for the remainder of the work described in this\ud thesis.\ud The findings of the simulation studies were confirmed by an extensive series of\ud experiments using a pH process pilot plant. The performance of the tunable fuzzy\ud controller was compared with that of a conventional PI controller in response to step\ud change in the set-point, at a number of pH levels. The results showed not only that the\ud fuzzy controller could be easily adjusted to provided a wide range of operating characteristics, but also that the fuzzy controller was much better at controlling\ud the highly non-linear pH process, than a conventional digital PI controller. The fuzzy\ud controller achieved a shorter settling time, produced less over-shoot, and was less\ud affected by contamination than the digital PI controller.\ud One of the most important characteristics of the tunable fuzzy controller is its ability\ud to implement a wide variety of control mechanisms simply by modifying one or two\ud control variables. Thus the controller can be made to behave in a manner similar to that\ud of a conventional PI controller, or with different parameter values, can imitate other\ud forms of controller. One such mode of operation uses sliding mode control, with the\ud fuzzy decision table main diagonal being used as the variable structure system (VSS)\ud switching line. A theoretical explanation of this behavior, and its boundary conditions,\ud are given within the text.\ud While the work described within this thesis has concentrated on the use of fuzzy\ud techniques in the control of continuous flow pH plants, the flexibility of the fuzzy\ud control strategy described here, make it of interest in other areas. It is likely to be\ud particularly useful in situations where high degrees of non-linearity make more\ud conventional control methods ineffective

Topics:
QA, QD

OAI identifier:
oai:wrap.warwick.ac.uk:3657

Provided by:
Warwick Research Archives Portal Repository

Downloaded from
http://wrap.warwick.ac.uk/3657/1/WRAP_THESIS_Huang_1997.pdf

- (1978). A self-adjusting system for effluent pH control,"Springjoint conf.
- (1984). Adaptive Filtering Prediction and Control,
- (1976). Advances in the linguistic synthesis of fuzzy controllers,"
- (1979). Alinguistic self-organizingprocess controller,"
- (1985). An experimental study on fuzzy parking control using a model car,"
- (1985). An introductory survey of fuzzy control,"
- (1997). and Assessment ofa Self-organising Fuzzy-logic controller of a Highly Non-linear Fluid Supply System", read at a meeting of The Institute of Marine Engineerings,
- (1989). and Masaki Togaai."The Fuzzy-C Compiler: A Software Tool for Producing Portable Fuzzy Expert
- (1975). Anexperiment in linguistic synthesis with a fuzzy logic controller,"Int.
- (1981). AnExperiment in Linguistic Synthesis with a Fuzzy Logic Controller." Fuzzy Reasoning and its Application.
- (1985). Anexperimental study of a class of algorithms for adaptive pH control,"
- (1985). Application offuzzy reasoning to the water purification process,"
- (1985). Automatic train operation by predictive fuzzycontrol," in Industrial applications offuzzy control,M. Sugeno Ed.
- Bernard,"Use of rule-based system for process control,"
- (1976). Control Systems ofVariable Structure,
- (1983). Derivation of fuzzy control rules from human , tr I actions"
- (1986). DesignCriteria for pH control systems,"
- (1986). Expert System on a Chip: An Engine for approximate reasoning."
- (1988). Fujitec,"FLEX-8800series elevator group control system," Fujitec Co.,
- (1968). Fuzzy algorithm,"Informat.
- Fuzzy control for automatic train operation system,", in
- (1985). Fuzzy Identification ofSystems and Its Applications to Modeling and Control,"IEEETrans.
- (1991). Fuzzy logic for control of roll and moment for a flexible wing aircraft,"IEEE
- (1978). Fuzzy relations in a control setting,"
- (1990). Fuzzylogic in control systems:Fuzzy logic controller-PartI & II,"
- (1992). Hyperplane design techniques for discrete-time variablestructure control systems,"
- (1980). Industrial applications of fuzzy logic control."Int.
- Introduction toTheory ofFuzzy Subsets,
- (1991). Kravaris,"Nonlinearcontrol ofpH process using the strong id .
- (1979). Kummel,"Self-tuning control of a pH-neutralization process,"
- (1980). Lachmann,"Digital parameter adaptive control of a pH process,"Joint Auto. Control Conf'.
- (1980). Larsen," Industrial applications of fuzzy logic
- Lee,"A Self-learning Rule-Based Controller Emplying Approximation Reasoning and Neural Net Concepts,"Int.
- (1972). Lowenthal,"Dynamics ofpH in controlled stirred tank reactor,"Ind.
- (1976). MacVicar-Whelan,"Fussysets for man-manchine interaction,"
- Mamdani,"Afuzzy logic controller for a traffic junction,"
- (1987). Mulholland,"Comparingfuzzy logic with classical controller designs,"IEEE.
- (1987). Mulholland,"Designingexpertcontrollers,"presented attheIEEE Region Five Conf.,
- (1984). Murakami,"Fuzzyparking control of model car,"
- (1991). NeuraLogix Inc."FMC NLX23x Fuzzy MicroController Family. "
- (1985). Nishida,"Fuzzycontrol of model car,"
- (1990). Nonlinear controller for a pH process,"
- (1986). Nonlinear state feedback synthesisfor pH control,"
- (1981). Note on the arithmetic rule by Zadeh for fuzzy conditional inference,"
- Outline of a new approach to the analysis complex systems and -181-[13]
- (1988). Robust hyperplane design in multivariable structure control systems'"
- (1989). Sakawa."Virtual paging Fuzzy Chip & Fuzzy Workstation as its Design Evironment." 3rd IFSA World Congress,
- (1988). Structure identification of fuzzy model,"
- (1991). Successive identification of a fuzzy model and its applications to predictionofacomplex system,"Fuzzy Sets andSystems,
- (1983). Takagi,"Multi-dimensionalfuzzy reasoning,"
- Tanaka,"Practicalfuzzy book-by C language,"RaSe Ru co., Tokyo,1989.
- (1975). Theapplication of fuzzy control systems to industrial process," in IFAC World Congress,
- (1977). Tong,"Acontrol engineering review of fuzzy systems."
- (1979). Tsukamoto,"Anapproachto fuzzy reasoning method."
- (1988). Variable structure control of nonlinear and multivariable system: a tutorial,"Proc.
- (1977). Variable structure systems with sliding modes,"
- (1983). Waller,"Dynamic modeling and reaction invariant control ofpH,"
- (1989). Wittenmark,,Adaptive control.
- (1994). Yang,"AReal-Time Interpreted Fuzzy InferenceEngine."2nd Symposium on Fuzzy System,
- (1973). Zadeh,"Outlineof a new approach to the analysis of complex systems and decision process."IEEE Trans.

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.