Direct Adaptive Emotional Control of Uncertain Nonlinear Systems with Input Saturation

Emotional neural networks have gained attention in different fields. Despite their growing applications, their employment in more complicated control problems is not much investigated. Accordingly, in this study, we employ the previously published Radial basis Emotional Neural Network with an Averaging Thalamic Connection (RBENN-ATC) in the direct adaptive control of affine nonlinear systems with partially unknown dynamics and control input saturation. The update rules of the amygdala are increasing as the basic models of emotion. The closed-loop stability is ensured using the Lyapunov stability theory. Simulation results depict better performance of the proposed system in higher robustness and lower tracking error compared to a neuro-approach.