New Conditions for Lyapunov Stability Of A Hybrid Learning Based Indirect Adaptive Neural Control for Nonlinear Systems

In this study, novel criteria for both asymptotic and exponential stability within a hybrid learning-based adaptive neural control framework are presented. These necessary and sufficient conditions are employed to adaptively tune the learning rates of the Neural Model (NM) and the Neural Controller (NC), thereby guaranteeing the stability of the indirect adaptive neural control system. Theoretical analysis together with simulation outcomes confirm the efficiency and robustness of the proposed adaptive control.