This paper focuses on the output-constrained tracking control problem of magnetic drive transmission systems subject to modeling uncertainties. Specifically, a tracking error-based time-varying transformation function is introduced to convert the constrained system into an unconstrained framework. And radial basis function-based neural networks (RBFNN) will be employed to approximate the unknown nonlinear dynamics. Meanwhile, the extended state observer will be incorporated to estimate and compensate for external disturbances. The simulation results demonstrate the effectiveness of the proposed neuroadaptive learning algorithm in the presence of uncertainties.
Xu et al. (2026) studied this question.