Adaptive recurrent fuzzy neural network control for linearized multivariable systems

This paper develops a design method of recurrent fuzzy neural network (RFNN) control system for multi-input multi-output (MIMO) dynamic systems. This control system consist a feedback controller and a RFNN controller. The feedback controller reveals basic stabilizing controller to stabilize the system and the RFNN controller presents a robust controller to deal with unknown part of system dynamics. The adaptive laws of RFNN are derived based on the Lyapunov stability function so that the stability of the system can be guaranteed. Finally, the proposed control system is applied to an F-16 flight control system. Simulation results demonstrate that the developed control system can achieve favorable robust control performances even with some failures of the flight control system.