Deal with uncertainty systems using intelligent control methods

Abstract

This paper represents superior properties of advanced control methods such as Fuzzy Logic, Neural network to PID controller for uncertainties systems to achieve good tracking response in real time. All three control methods are based on the feedback error signal that is then calculated on the processor through algorithms and outputting the optimal control signals. In particular, the uncertainties system such as an AC motor control model, temperature control model. These are two characteristic models, which differ in response latency and inertia control. Firstly, These models are controlled by the PID controller. Afterward, the Fuzzy controller and Neural controller are utilized for these models to explore the adaptive capability to changes in parameters of the advanced control methods. The experimental results for the AC motor speed control model and temperature control model are showed to verify the effectiveness and applicability of the advanced control methods for the uncertainty systems.