Robust performance optimization of UAV dynamic systems using MPC-PID hybrid control.

This paper intends to address the challenges of insufficient robustness and model uncertainty compensation in unmanned aerial vehicle dynamic systems under complex disturbances. The paper proposes a hybrid control architecture that combines deep fusion model predictive control with adaptive Proportional-Integral-Derivative (PID) based on Transformer attention mechanism. The core innovation of this architecture lies in introducing attention neural networks to dynamically tune PID gains online, and forming a deep collaborative control framework of "prediction-learning-compensation" with Model Predictive Control (MPC) and sliding mode disturbance observer with H∞ (H-infinity) robust optimization. This thereby improvs the adaptability and control accuracy of the system under unstructured disturbances and model mismatches. The control architecture employs a robustly optimized upper-layer MPC controller, which, based on the receding horizon principle, utilizes real-time system state updates to predict future state evolution. An H∞ performance criterion is incorporated into the control sequence optimization to strengthen robustness against model parameter perturbations and external disturbances. The lower-layer controller adopts an adaptive PID structure that responds quickly to the reference signals generated by the MPC. To address the degradation of PID tuning performance under dynamic mismatches and unmodeled disturbances, an attention mechanism neural network based on the Transformer architecture is introduced to adjust the PID gains online and capture nonlinear dynamic variations. Additionally, in order to further enhance system stability under severe disturbances, this control framework integrates sliding mode control technology into the disturbance observer design, and constructs a sliding mode disturbance observer module for explicit estimation of external disturbances and model uncertainties. The estimated values are injected into the lower-level adaptive PID controller through a feedforward compensation mechanism to achieve active disturbance rejection. Simulation experiments conducted in a nonlinear disturbance environment built on the AirSim platform, as well as tests using the EuRoc dataset, demonstrate that the proposed method maintains a steady-state tracking error within 5% during path-following tasks. Compared with the traditional MPC combined with fixed gain PID control, this method improves the steady-state robustness by about 17%, and shortens the system adjustment time from 3.15 s to 2.47 s, significantly improving by 21.6%, demonstrating excellent convergence and anti-interference ability. The results indicate that the MPC-PID hybrid control approach offers significant advantages in enhancing the robustness, adaptability, and control accuracy of UAV systems, making it well-suited for intelligent control demands in complex flight missions.