A Neuroadaptive Architecture for Model Reference Control of Uncertain Dynamical Systems with Performance Guarantees

Neuroadaptive control systems have the capability to approximate unstructured system uncertainties on a given compact set using neural networks. Yet, a challenge in their design is to guarantee the closed-loop system trajectories stay in this set such that the universal function approximation property is satisfied and the overall system stability is achieved. To address this challenge, we present and analyze a new neuroadaptive architecture in this paper for model reference control of uncertain dynamical systems with strict performance guarantees. Specifically, the proposed architecture is predicated on a novel set-theoretic framework and has the capability to keep the closed-loop system trajectories within an a-priori, user-defined compact set without violating the universal function approximation property. A transport aircraft example is also given to complement the presented theoretical results