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A new inference method for multivariable fuzzy systems with application in industrial control

University of British Columbia

Fuzzy logic has been proven to be a useful technique for information fusion in intelligent (or expert) systems as well as both direct control and supervisory control of industrial processes. In this technique, knowledge or expertise of humans are formulated in the form of if-then rules. Usually a human can correlate two to three input variables (in the if part) to an output variable (in the then part). However, complex practical systems are typically multi-input-multi-output (MIMO) systems where there are many input variables that relate to one or more output variables. In such a system, it would be rather unrealistic for a human to keep track of a large number of interacting variables and to express the operational expertise as a set of coupled rules consisting of all input variables and output variables. This thesis develops a new inference method to address the decision making of multivariable fuzzy systems. The developed inference method is based on the concepts of fuzzy measure and the fuzzy integral. The method first separates a coupled rulebase into several single-input-singleoutput (SISO) modules or, dual-input-single-output (DISO) modules. Then, it assigns a degree of importance to these SISO modules. Finally the inferences of these modules are combined (fused) to obtain an overall inference, through the use of a fuzzy integral. The technique was originally motivated by the requirements of an industrial fish cutting machine that has been developed in the Industrial Automation Laboratory, University of British Columbia. This inference method can be embedded in any commercial expert system, any direct fuzzy logic controller or a supervisory fuzzy control system that deals with multiple fuzzy variables in a coupled rulebase. The validity of this technique is illustrated by implementing it on hydraulic manipulator of the prototype fish cutting machine and through thorough experimentation. Two inference methods—individual — rulebased method and composition-based method, are implemented on a direct proportional (P)-type fuzzy controller in real time on the prototype hydraulic manipulator. A proportional-plus-derivative (PD)-type fuzzy controller is developed and implemented in real time on the prototype hydraulic manipulator. These various implementations are used to carry out an extensive experimental investigation and study the significance of selecting and tuning of such components as the membership functions, rulebase, inference method, and the defuzzification technique in practical fuzzy logic systems. A supervisory fuzzy control system is developed and implemented integral with an intelligent fuzzy tuner for the hydraulic manipulators of the prototype fish cutting machine. This control system monitors, the performance of the machine and automatically tunes the parameters of the PD controllers of the hydraulic actuators. The control system has a three-layer hierarchical structure. The middle layer monitors the performance parameters of the PD-controllers, based on a step-input response. The top layer infers the tuning actions for the PD servo units. The knowledge base for tuning the low level controller is developed using expert knowledge and represented as fuzzy rulebase modules. The investigation presented in this thesis makes important contributions for design, development and implementation of fuzzy knowledge-based systems, particularly control systems, in practical applications. In particular, the present work concentrates on inference techniques, fuzzy rulebase, membership functions, and defuzzification in these systems.

Thesis/Dissertation

application/pdf

2009-09-16

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http://hdl.handle.net/2429/12845

Mechanical Engineering

University of British Columbia

UBCV

DSpace