Parallel learning-based security robust tracking control for nonlinear systems with uncertainties: An event-triggered design.

Utilizing parallel learning technology and an event-triggering mechanism, we introduce an adaptive security tracking control approach for nonlinear systems with both matched and mismatched uncertainties. The methodology commences with an overview of the constraints imposed by the control barrier function (CBF) on the system state, followed by the selection of a suitable CBF. An augmented system is constructed by amalgamating the system state and tracking error, with the mismatched uncertainty being decomposed to introduce an auxiliary system. Incorporating the CBF into the performance metrics ensures that the system state remains within a secure region. Subsequently, the paper employs an event-triggering mechanism to derive an event-based secure Hamiltonian–Jacobi–Bellman (HJB) equation. An adaptive single-critic network, designed using parallel learning technology, approximates the solution to the event-based secure HJB equation. Lastly, the Lyapunov method is employed to demonstrate the uniform ultimate boundedness (UUB) of both the tracking error and weight error. The efficacy of the proposed method is validated through two nonlinear examples. [ABSTRACT FROM AUTHOR]