GA Optimized Knowledge Base of FLC for Complex Industrial Process

The Knowledge Base of a Fuzzy Logic Controller (FLC) encapsulates expert knowledge and consists of the Data Base (membership functions) and Rule-Base of the controller. Optimization of these Knowledge Base components is critical to the performance of the controller and has traditionally been achieved through a process of trial and error. Such an approach is convenient for FLCs having low numbers of input variables. However for greater number of inputs, more formal methods of Knowledge Base optimization are required. Genetic Algorithms (GAs) provide such a method to optimize the FLC parameters. An intelligent multi input multi output (MIMO) control for the cement milling circuit is presented. The FLC is optimized by GA for varying nonlinearity in the plant. The proposed control algorithm was tested with the cement mill simulation model within MATLAB SIMULINK environment. Parameters of the simulation model were set up based on the actual cement mill characteristics. The performances of the proposed control technique are compared with various other control techniques.

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