Author/Authors :
çetin, meriç pamukkale üniversitesi - mühendislik fakültesi, turkey , beyhan, selami pamukkale üniversitesi - mühendislik fakültesi - elektrik elektronik mühendisliği bölümü, turkey , bahtiyar, bedri pamukkale üniversitesi - denizli teknik bilimler meslek yüksekokulu - elektrik ve enerji bölümü, turkey
Title Of Article :
Artificial neural network based adaptive linear model predictive control
Abstract :
The effect of the unmodeled dynamics and unknown disturbances prevent the accurate control of the real-time systems. The designed controllers must undertake the effect of these undesired uncertainties. In this paper, adaptive uncertainty modeling based model predictive controller is proposed for the control of uncertain linear systems. The uncertainty modeling structure uses an artificial neural network with adaptive learning rate for fast approximation. The stability of the proposed adaptive uncertainty modeling based model predictive control (UMPC) is shown using Lyapunov candidate function. Conventional MPC and proposed UMPC are applied to the control of a real-time DC/DC buck power converter. The conventional MPC cannot accurately control the DC/DC converter due to the unknown parameters and unmodeled dynamics. However, the proposed UMPC controller can accurately control the system with modeling the uncertainties in controller dynamics. The proposed controller is promising to control uncertain systems in future applications.
NaturalLanguageKeyword :
Model predictive control , Adaptive linear model predictive control , Adaptive neural networks , Stability , Real , time DC , DC converter
JournalTitle :
Pamukkale University Journal Of Engineering Sciences
