Emotion enables biological organisms to respond quickly and reasonably to uncertain and unpredictable events. This basic survival instinct is remarkably in line with the theory of model predictive control (MPC) that predicts the future occurrences of events to determine a present appropriate action. Specifically, here we propose to resolve the basic challenge of nonlinear MPC in keeping low computational cost by using a modified computational model of the limbic brain. For this purpose, we modify the thalamus–amygdala expansion link by a harmonic function to reach a differentiable and smooth mathematical model. The proposed harmonic emotion-based neuro-cognitive network (HENN) avoids the typical hidden layers in neural networks, leading to a lower computational cost. We then extend this architecture to a hierarchical HENN (H2ENN) to reach higher modeling accuracy. Theoretical analysis proves the training algorithm’s convergence for the general case of brain emotional learning (BEL). Specifically, the learning rules are shown to converge in a finite number of iterations if a solution exists. These theoretical results are general and equally applicable to HENN and hierarchical harmonic emotional neuro-cognitive network (H2ENN). Predictive control based on H2ENN is then applied to control a 3-Prismatic-Spherical-Prismatic (3-PSP) nonlinear parallel robot manipulator and compared with the same general structure that uses a conventional neural network instead. Results indicate the proposed H2ENN-based nonlinear MPC approach offers better accuracy. At the same time, its computational cost is twenty times less than the competing method.

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