Disturbance Observer‐Enhanced Fractional Data‐Driven Control for Uncertain Discrete‐Time Nonlinear Systems

Abstract

ABSTRACT This study proposes a novel adaptive approach for fractional data‐driven control of nonlinear systems subject to disturbances. The robust stability of the control system is demonstrated, ensuring bounded tracking error under disturbances. To enhance disturbance mitigation, a data‐driven control strategy is developed, incorporating a radial basis function neural network (RBFNN) based disturbance observer. This design improves system resilience to uncertainties and external perturbations. By integrating fractional‐order dynamics with adaptive neural techniques, the proposed methodology offers enhanced performance over traditional integer‐order controllers. Simulation results demonstrate the approach's effectiveness in tracking accuracy, disturbance rejection, and system stability.