Potti Subbaraj and Pon S. Godwin Anand


MIMO, FLC, GAs, optimization, cement mill, plugging


Multiple input multiple output systems (MIMO) generally exhibit nonlinear characteristics due to dynamics coupling between the control variables. Hence the effective control of these systems makes more difficult to compensate the coupling effect between the variables. Model-based coupling controller cannot perform satisfactorily because of the uncertainty associated with these systems. The knowledge base of the fuzzy logic controller (FLC) consists of rule base and data base (membership function). Optimization of these knowledge base components traditionally been achieved through a process of trial and error. Such an approach is convenient for FLCs having low number of input variables. However for greater number of inputs and outputs, more formal methods of knowledge base optimization are required. In this work, an MIMO FLC with coupling FLC is optimized by genetic algorithm for the cement milling process is presented. The proposed control algorithm was studied on the cement mill simulation model within MATLAB and SIMULINK TM TM environment. The performance indices of the proposed control technique are compared with other control techniques. The results of the simulation control study indicate that the proposed controller provides better performance compared to other control techniques.

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