A recurrent wavelet-based brain emotional learning network controller for nonlinear systems

Conventional control systems often suffer from the coexistence of nonlinearity and uncertainty. This paper proposes a novel brain emotional neural network to support addressing such challenges. The proposed integrates wavelet into conventional learning network. is further enhanced by introduction recurrent structure employ two networks as channels therefore combines advantages function, mechanism, system, for optimal performance on nonlinear problems under uncertain environments. works with bounding compensator mimic an ideal controller, parameters are updated based laws derived Lyapunov stability analysis theory. system was applied systems, including Duffing-Homes chaotic simulated 3-DOF spherical joint robot. experiments demonstrated that outperformed other popular neural-network-based indicating superiority system.