Emotional neural networks with universal approximation property for stable direct adaptive nonlinear control systems

Abstract

Universal approximation, continuity, and differentiability are desirable properties of any computational framework, including those that rise from human cognition and/or are inspired by nature. Emotional machines constitute one such framework, but few studies have addressed their mathematical properties. Here, we propose a Continuous Radial Basis Emotional Neural Network (CRBENN) that benefits from the universal approximation property, continuous output, and simple structure of RBF; while keeping the fast response properties of emotion-based approaches. As such, CRBENN is amenable to a wide array of challenging problems in systems engineering and artificial intelligence. Here, we propose a CRBENN-based direct adaptive robust emotional neuro-control approach (DARENC) for a class of uncertain nonlinear systems. Stability is theoretically established using Lyapunov analysis of the closed-loop system. DARENC is then applied to control two nonlinear systems, and the performance of the controller is numerically compared with several competing fuzzy, neural, and emotional controllers. The simulation results indicate improved tracking performance, better disturbance rejection, and less control effort. Finally, DARENC is implemented on a real-world 3-PSP (spherical–prismatic–spherical) parallel robot in our laboratory. The experimental results show the satisfactory performance of the robot in tracking the desired trajectory with low control effort.