An adaptive fault tolerant neuro-fuzzy based proportional-integral controller for a nonlinear system

Abstract

In this paper, an adaptive proportional-integral controller based on a fuzzy relational model is developed for the purpose of fault tolerant control in a nonlinear, information-poor system. First, the methods of fault tolerant control are briefly introduced. An air-cooling subsystem in a heating, ventilating and air-conditioning system is used as an example to describe the fuzzy relational modelling procedure. Then a proportional-integral (PI) observer is established for fault identification and a PI-based adaptive controller is designed for fault tolerance with varied parameter adjusting. By introducing the fault estimation, an adaptive mechanism is adopted to update the parameter selection in the control scheme. Sensor noise is also considered in the method and simulation experiments are performed to verify the effectiveness of the proposed scheme.

Stability analysis of brain emotional intelligent controller with application to electrically driven robot manipulators

“…Parametric uncertainty, external disturbances and un-modelled dynamics are other challenges which should be taken into consideration. To solve these problems, intelligent control approaches such as neurofuzzy controllers have been presented [3,4].…”