Model-Free Active Disturbance Rejection Control Algorithm Implementation

This paper presents a model-free active disturbance rejection control (ADRC) methodology. The algorithm strategically considers both network components and controlled plants as integrated control objects. The implementation employs an extended state observer (ESO) that continuously estimates system states and total disturbances through real-time observation. A key implementation aspect involves the ESO's recursive estimation algorithm, which typically uses difference equations to track combined effects of internal dynamics and external disturbances. The primary advantage of this approach lies in its robust disturbance compensation capability, significantly enhancing system robustness and stability against various uncertainties. From an implementation perspective, the control law often incorporates a nonlinear feedback combination of estimated states, while the disturbance estimation component enables real-time cancellation of perturbations. This method demonstrates practical applicability across diverse domains including robotic control systems (e.g., trajectory tracking with dynamic compensation), autonomous driving platforms (handling road disturbances and model variations), and industrial automation scenarios (addressing parameter uncertainties and load changes). The code structure typically involves separate modules for state observation, disturbance estimation, and control law calculation, allowing for modular implementation and parameter tuning.