A Hierarchical Control Strategy for Full-Car Active Suspensions Integrating Virtual Road Reference Generation and Adaptive NLESO-Based Error Compensation

Active suspension systems with road preview capability rely on accurate LiDAR elevation measurements; however, offline calibration cannot compensate for dynamic measurement errors caused by vibration, temperature drift, and road impacts. This paper proposes a hierarchical control strategy to address this issue. A virtual road reference is generated via least-squares plane fitting, enabling the vehicle to conform to the macroscopic terrain while filtering out local irregularities. An adaptive nonlinear extended state observer is then designed to automatically adjust its gain based on road conditions, achieving simultaneous noise suppression and fast tracking of time-varying errors. An explicit dynamic mapping between actuator acceleration and vehicle attitude is also established, providing a theoretical bound on the closed-loop tracking error. Simulation results on various road profiles demonstrate the effectiveness of the proposed strategy in numerical studies.