Interval type-2 fuzzy gmc for nonlinear stochastic process of methane production in the anaerobic digester system

The paper focused on the implementation of hybrid Interval Type-2 (IT2) Fuzzy with Generic Model Control (GMC) for the nonlinear stochastic waste treatment process in the anaerobic digester. Development of the deterministic methane process model has been extended to a set of stochastic nonlinear differential equations. The stochastic effect is introduced by adding white noise with unit covariance to give an interesting profile like physical plant dynamic. The IT2 Fuzzy based on Takagi-Sugeno-Mendel and GMC by Lee and Sullivan have been developed to control the holdup pH inside reactor. The pH value is being manipulated by the flowrate of Sodium hydroxide at optimal methane gas production condition. The process variables that need to be controlled and included into the controller are pH, error and change of error while the consequence fuzzy set output is from the GMC backpropagation law equations. As a result from several studies; servo and regulatory, the controller show significant improvement on the set point tracking and disturbance rejection over typical Fuzzy, Fuzzy-GMC and conventional Proportional-Integral-Derivative (PID) controllers. It shows the controller is suitable for stochastic process and nonlinear control system application