Abstract
Model Reference Adaptive Systems (MRASs) use mostly the traditional MIT rule based controllers to drive the difference (error) between the model reference signal and actual output one to zero value. MIT rule based controllers are slow and cause large error values in case of highly non-linear process. In this paper, we propose the Brain Emotional Learning Based Intelligent Controller (BELBIC) to replace the MIT rule based one. BELBIC benefits Brain Emotional Learning modeled algorithm in mammalians brain to seek the proper control signal that eliminates the error. In spite of some overshoots in MRAS with BELBIC, simulation of the proposed BELBIC for MRAS with its large number of adjustable gains achieves remarkable fast response.