| Today's manufacturing processes present many challenging control problems; among these are nonlinear dynamic behavior, uncertain and time varying parameters, and unmeasured disturbances. In the past decade, the control of these systems has received considerable attention in both academia and industry. Surveys and studies indicate that MPC and fuzzy control are the most widely used of the modern control techniques in industries. These tendencies indicate that there is a huge demand in the industry for new fuzzy and MPC solutions. However, most of the available algorithms to control nonlinear systems lead to the use of computationally intensive nonlinear techniques that make industrial application almost impossible. To avoid this problem, this work presents the use of Takagi-Sugeno Fuzzy (TSF) Models and Least Mean Square (LMS) for the design and implementation of new control techniques that incorporate Internal Model Control (IMC) structure and Adaptive Inverse Control (AIC) for the control of nonlinear systems. The proposed control techniques are applied to control nonlinear temperature process module and a robotic arm manipulator in a laboratory scale. |
