Robust fractional-order control with quantitative feedback theory–constrained learning for uncertain pressure regulation systems
Achu Govind K. R.
Robust pressure regulation in uncertain dynamical systems remains a challenging control problem, particularly in the presence of plant uncertainties, actuator nonlinearities, and external disturbances. Conventional proportional–integral–derivative–based controllers often exhibit limited robustness and adaptability when operating under such conditions. This motivates the design of advanced, robust, and learning-assisted control strategies. Hence, this article proposes a hybrid data-driven robust control framework that combines a fractional-order proportional–integral–derivative controller with quantitative feedback theory and a gated recurrent unit network. The quantitative feedback theory–based design ensures robust frequency-domain performance over structured plant uncertainties, while the gated recurrent unit adaptively tunes the fractional-order proportional–integral–derivative parameters by minimizing the integral of squared error, thereby enhancing transient and steady-state performance. The proposed approach is evaluated on a blower-driven ventilator pressure-control model under reference tracking, measurement noise, external disturbances, and actuator degradation scenarios. Simulation results demonstrate accurate pressure tracking with low overshoot (