Stable robust controller inspired by the mammalian limbic system for a class of nonlinear systems

In recent years diverse computational models of emotional learning observed in the mammalian brain have inspired a number of self-learning control approaches. These architectures are promising in terms of their learning ability and low computational cost. However, the lack of rigorous stability analysis and mathematical proofs of stability and performance has limited the proliferation of these controllers. To address this drawback, this paper proposes a modified brain emotional neural network structure using a radial basis function inside the Thalamus and an emotional signal based on an integral action structure to increase performance. Mathematical stability proofs are provided, together with numerical simulations, demonstrating the superior performance obtained with the new modifications proposed to the emoional learning-inspired control.