Neuro-fuzzy iterative learning control for 4-poster test rig

Abstract

In this paper, a new control method is presented for the 4-poster test systems. The primary aim of the paper is to improve the convergence speed and decrease the error rate for model-based iterative learning control (ILC), a widely used method as a tracking control. First, the dynamic equations of the system are generated, and the control problem is formulated. Then, an inverse model of the system is established directly through the adaptive neuro-fuzzy inference system (ANFIS) with auxiliary parameter (piston position) as a serial combination of two sub-models. In order to construct a neuro-fuzzy ILC (NFILC) structure, these sub-models are integrated into the neuro-fuzzy inverse controller (NFIC). Because of this new structure, the modified ILC rule has two layers. In the first layer, the controlled parameter, namely, the acceleration is iterated, whereas, in the second layer, the auxiliary parameter is iterated. The outcomes of the proposed control method are scrutinized by testing through a numerical simulation. Finally, it is demonstrated that the modified ILC rule dramatically increase the convergence speed and reduce the final error rate.