Non-linear Control System Using Function-Link Fuzzy Brain Emotional Controller

This paper portrays the design, operation, and application of an artificial neural network-based intelligent controller, the structure of which is constructed by integrating a function-link neural network with a fuzzy brain emotional learning controller (FBELC). The resulting model is called the function-link fuzzy brain emotional learning controller (FLFBELC). The FBELC consists of two sets of neural networks inspired by the two sets of systems of the brain, the orbitofrontal cortex and amygdala, which are responsible for decision-making and emotional response, respectively. These two networks are the sensory network and the emotional network, which affect each other’s learning capabilities and are jointly responsible for the control output of the controller. The function-link neural network (FLNN) is a method shown to facilitate faster convergence and reduce computational load through function approximation. The FLNN structure is integrated with the FBELC, with the resulting controller’s intent to acquire these characteristics. The weights of the proposed FLFBELC network can be approximated using function expansions of the control system’s input. Finally, the FLFBELC is applied for the tracking control of nonlinear systems to illustrate the effectiveness of the proposed control method. The results are compared to other recent controllers to contrast the favorable performance of the FLFBELC.