A new neuroadaptive control architecture for nonlinear uncertain dynamical systems: Beyond σ- and e-modifications

Neural networks are a viable paradigm for adaptive system identification and control. This paper develops a new neuroadaptive control architecture for nonlinear uncertain dynamical systems. The proposed framework involves a novel controller architecture involving additional terms in the update laws that are constructed using a moving window of the integrated system uncertainty. These terms can be used to identify the ideal system parameters as well as effectively suppress system uncertainty. A linear parameterization of the system uncertainty is considered and state feedback neuro-adaptive controllers are developed.