Emotion is nature’s most exquisite survival mechanism to speedily and reasonably handle the uncertainties and complexities of life. It is hence reasonable to expect emotion-based artificial agencies to better handle their complex and uncertain environments and their interactions with other known or unknown agencies. Accordingly, an emotional neural network is proposed here for multi-agent systems (MAS). This computational paradigm has the universal approximation property of its well-established predecessor, the multilayer perceptron, but also promises the characteristics of the emotional mind such as fast response and learning. Specifically, we extend our previously established radial basis emotional neural network (RBENN) to approximate the uncertain dynamics in a cooperative adaptive radial basis emotional neuro-controller (CARENC) for a class of higher-ordered uncertain nonlinear MAS. Artificial potential functions are used to describe the interactions among heterogeneous agents. Also, the approximation error is compensated using an adaptive component. Suitable update laws are derived for the weights of RBENN that are consistent with the basic emotional models. In following Lyapunov stability theory, the overall system stability is also guaranteed. Application to two nonlinear multi-agent control problems shows a lower steady-state tracking and lesser control energy consumption when compared with a competing multi-agent neuro controller.

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