Stable adaptive fuzzy data-driven controller for unknown dynamic within a class of nonlinear discretetime systems

World Congress on Industrial Automation

July 20-22, 2015 San Francisco, USA

Chidentree Treesatayapun and Naret Suyarot

Posters-Accepted Abstracts: Adv Robot Autom

Abstract :

For several control engineering applications especially automation systems, the system dynamic and models are required to design the controllers under the expectation performance. Unfortunately, the mathematical model and system dynamic are difficult to determine regarding the nonlinearity and uncertainty of practical plants. In this work, the controller for a class of nonlinear discrete-time systems is designed under the assumption that system dynamic and mathematical model are assumed to be unknown. This controller is constructed with a Fuzzy rule adaptive network (FREN) which can operate under the human knowledge of controlled plant within the format of IF-THEN rules. Only input-output data set is required to design this controller. Furthermore, the off-line learning phase can be neglected here with the closed-loop performance analysis. The experimental system is constructed to demonstrate the validation of the proposed controller.