  This study addresses the challenge of achieving real-time Universal
Self-Learning Control (USLC) in nonlinear dynamic systems with uncertain
models. The proposed control method incorporates a Universal Self-Learning
module, which introduces a model-free online executor-evaluator framework to
enable controller adaptation in the presence of unknown disturbances. By
leveraging a neural network model trained on historical system performance
data, the controller can autonomously learn to approximate optimal performance
during each learning cycle. Consequently, the controller's structural
parameters are incrementally adjusted to achieve a performance threshold
comparable to human-level performance. Utilizing nonlinear system stability
theory, specifically in the context of three-dimensional manifold space, we
demonstrate the stability of USLC in Lipschitz continuous systems. We
illustrate the USLC framework numerically with two case studies: a low-order
circuit system and a high-order morphing fixed-wing attitude control system.
The simulation results verify the effectiveness and universality of the
proposed method.
