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An adaptive fuzzy controller improving a control system for process control

A nonlinear controller based on a fuzzy model of MISO dynamical systems is described and analysed. Fuzzy sets and fuzzy inference to combine mathematical models in order to construct a nonlinear model of the system are used. The fuzzy rule base consists of a set of linguistic rules in the form of "IF a set of conditions are satisfied, THEN a set of consequences are inferred." We consider the case where the fuzzy rule base consists of N rules in the working form. Adaptive fuzzy logic control is used to deal with plant uncertainty. The basic idea is to have a controller which tunes itself to the plant being controlled: typically such controllers can be described by a nonlinear time-varying (NTLV) differential (or difference) equation. One of the important problems in the area has been the model reference adaptive control problem (MRACP), where the goal is to have the output of the plant asymptotically track the output of a stable reference model in response to a piecewise continuous bounded input. The adaptive control structure is applied to a simulated control of the pH level of a neutralization process where a first order chemical reaction I/spl rarr/II takes places. A perfectly effective pH level controller is used for keeping the reactor volume constant. The objective of the controller is to drive the pH to the desired set point in the shortest time possible and to maintain the system pH at the desired setpoint. The performance of this adaptive pH FLC is demonstrated for three different situations.