OPTIMAL DESIGN OF A FUZZY LOGIC CONTROLLER FOR CONTROL OF A CEMENT MILL PROCESS BY A GENETIC ALGORITHM
Potti Subbaraj, P.S. Godwin Anand · Instrumentation Science & Technology · 2011
The knowledge base of a fuzzy logic controller (FLC) encapsulates expert knowledge and consists of database (membership functions) and rule base of the controller. Optimization of both 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 numbers of inputs, more formal methods of knowledge base optimization are required. Genetic algorithms (GAs) provide such a method to optimize the FLC parameters to be globally optimum. The FLC is optimized by GA for varying nonlinearity and set point in the plant. The proposed control algorithm was studied on the cement mill simulation model and with a real cement mill model using MATLAB and Simulink environments. Parameters of the simulation model were set up based on the actual cement mill characteristics. The performance of the proposed control technique is compared with various other control techniques. The results of the control study indicate that the proposed algorithm can fully prevent the mill from plugging and can control the cement mill circuit more effectively.