The center-of-mass (CoM) coordinates of engineering vehicles vary with payload mass, payload position, and upper-structure posture, whereas active suspension control systems commonly use fixed CoM parameters. To enable convenient CoM updating under different stable operating conditions, this study proposes a multi-agent synchronization control-based method for determining CoM coordinates in engineering vehicles equipped with active suspension. Each active suspension actuator is modeled as an agent, and a four-actuator multi-agent system with a distributed consensus controller is developed to improve displacement synchronization and reduce the influence of actuator asynchrony on vertical support-force identification. A CoM coordinate determination model is then established using the mass reaction method and mass-weighted averaging, so that the vehicle CoM can be recalculated after the vehicle enters a new stable operating condition. Simulations and full-vehicle experiments were conducted on a two-axle crane equipped with active suspension at boom posture angles of 0°, 10°, and 20°. Compared with test-bench reference measurements, the maximum coordinate errors were 10.51 mm, 4.85 mm, and 92.45 mm, respectively. The results indicate that the proposed method can provide CoM coordinates consistent with reference values under stable operating conditions and support control-parameter updating after changes in payload state or upper-structure posture.

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.

The high-performance control of active suspension electro-hydraulic actuators (ASEHA) is limited by a timing mismatch: the primary internal physical state (load pressure) responds to disturbances almost instantaneously, whereas the tracking error used for feedback lags behind. To address this issue, a physics-aware co-design framework introduces three innovations: (i) a pressure-adaptive bandwidth ESO that directly schedules the observer bandwidth via load pressure, enabling faster disturbance estimation; (ii) a disturbance-adaptive constraint controller whose safety boundary is adjusted in real time using the observer‘s disturbance estimates, balancing tracking precision and safety; and (iii) a structured disturbance-separation architecture that reduces observer burden via model-based feedforward. By leveraging load pressure as a feedforward signal, this framework overcomes the latency inherent in error-feedback methods. Comparative simulations show that the proposed method outperforms conventional error-feedback methods by achieving a significant reduction in estimation error, as well as 2.3-times faster convergence, while ensuring both high tracking accuracy and strict constraint satisfaction.