Ye Zhuang

In-wheel motor drive systems can increase vehicle unsprung mass and thereby alter suspension dynamics. This study investigates the frequency-domain mechanisms through which an unsprung mass increment affects ride comfort, suspension working space, and road holding, and proposes a frequency-partitioned active impedance control (FPAIC) strategy. A structural sensitivity operator, M(s), is derived from a linear quarter-car model to quantify the mass-perturbation effect and to identify inertia, damping, and tire stiffness as the principal physical propagation channels. Guided by this mapping, the commanded active force is decomposed into virtual-inertia, virtual-damping, and virtual-stiffness components, which are scheduled according to the estimated dominant excitation frequency. Under swept-frequency simulations with Δmu = 60 kg, the comfort-prioritized LQR benchmark reduces the sprung-mass-acceleration RMS to 93.8% of the baseline but increases the tire-deflection RMS to 144.1%, whereas the road-holding-prioritized H∞ benchmark reduces the tire-deflection RMS to 99.1% at the cost of increasing the sprung-mass-acceleration RMS to 170.9%. The FPAIC yields corresponding RMS values of 98.9%, 85.9%, and 99.4% for sprung-mass acceleration, suspension deflection, and dynamic tire deflection, respectively. Overall, the results demonstrate mechanism-guided coordination across ride comfort, suspension working space, and road holding, while the Δmu = 40 kg case shows that the unchanged FPAIC parameterization retains its mitigation effect without retuning.

To meet the real-time computational requirements of active suspension control systems, this study shifts from complex microscopic physical equations to a direct nonlinear functional mapping between the relative motion states (displacement and velocity) and the output force of air springs. This approach aims to preserve critical nonlinear hysteresis characteristics while significantly reducing the computational overhead. A progressive modeling strategy is implemented to characterize these complex behaviors. Initially, polynomial fitting is employed to identify key input features; however, its limited capacity to capture intricate nonlinearities necessitates more advanced methods. Subsequently, standard Feedforward Neural Networks (FNNs) are explored for their nonlinear mapping capabilities, yet their inherent “black-box” nature often leads to convergence difficulties and restricted generalization. To address these issues, a Physics-Informed Neural Network (PINN) architecture is introduced, embedding physical governing equations as regularization constraints within the loss function to integrate data-driven flexibility with mathematical rigor. Recognizing that conventional PINNs often encounter convergence challenges due to conflicts between PDE constraints and data-driven loss terms, this research develops a Physics-Embedded Hierarchical Network (PEHN). By deriving specialized PDE constraints tailored to air spring dynamics and designing a hierarchical architecture aligned with these physical requirements, the PEHN effectively balances physical priors with experimental data. Experimental results demonstrate that, compared to the baseline models, the proposed PEHN exhibits stronger stability and superior accuracy in capturing the complex nonlinearities of air spring dynamics.

To meet the real-time computational requirements of active suspension control systems, this study shifts from complex microscopic physical equations to a direct nonlinear functional mapping between the relative motion states (displacement and velocity) and the output force of air springs. This approach aims to preserve critical nonlinear hysteresis characteristics while significantly reducing the computational overhead. A progressive modeling strategy is implemented to characterize these complex behaviors. Initially, polynomial fitting is employed to identify key input features; however, its limited capacity to capture intricate nonlinearities necessitates more advanced methods. Subsequently, standard Feedforward Neural Networks (FNN) are explored for their nonlinear mapping capabilities, yet their inherent "black-box" nature often leads to convergence difficulties and restricted generalization. To address these issues, a Physics-Informed Neural Network (PINN) architecture is introduced, embedding physical governing equations as regularization constraints within the loss function to integrate data-driven flexibility with mathematical rigor. Recognizing that conventional PINNs often encounter convergence challenges due to conflicts between PDE constraints and data-driven loss terms, this research develops a Physics-Embedded Hierarchical Network (PEHN). By deriving specialized PDE constraints tailored to air spring dynamics and designing a hierarchical architecture aligned with these physical requirements, the PEHN effectively balances physical priors with experimental data. Experimental results demonstrate that the proposed PEHN ensures robust convergence and superior accuracy in capturing complex nonlinearities, hysteresis effects, and dynamic stiffness variations compared to baseline models.

Off-road vehicle control designs often neglect the complex tire–soil interactions inherent to soft terrain. This paper proposes a Variable Impedance Control (VIC) strategy integrated with a high-fidelity terramechanics model. First, a real-time sinkage estimation algorithm is derived using experimentally identified Bekker parameters and the quasi-rigid wheel assumption to capture the nonlinear feedback between soil deformation and vehicle dynamics. Building on this, the VIC strategy adaptively regulates virtual stiffness, damping, and inertia parameters based on real-time suspension states. Comparative simulations on an ISO Class-C soft soil profile demonstrate that this framework effectively balances ride comfort and safety constraints. Specifically, the VIC strategy reduces the root-mean-square of vertical body acceleration by 46.9% compared to the passive baseline, significantly outperforming the Linear Quadratic Regulator (LQR). Furthermore, it achieves a 48.6% reduction in average power relative to LQR while maintaining suspension deflection strictly within the safe range. Moreover, unlike LQR, the VIC strategy improves tire deflection performance, ensuring superior ground adhesion. These results validate the method’s robustness and energy efficiency for off-road applications.

Semi-active suspension systems have emerged as a pivotal technology for enhancing vehicle ride comfort and handling stability, due to their controllable damping properties and energy-efficient operation. Owing to its suboptimal performance, the mixed sky-hook and ADD (SH-ADD) method has become the representative control method in semi-active suspension research. However, the phase difference between the sprung mass acceleration and the relative velocity causes faulty switching events in the frequency range selector, limiting its performance in the targeted frequency range. To address this, the phase-corrected SH-ADD (PC-SHADD) semi-active suspension control method is proposed, featuring a phase correction frequency-dependent damping switching control. The advantage of it lies in replacing the acceleration of the sprung mass with the relative velocity of the suspension at a smaller phase, reducing the misclassification rate of the frequency range selector by 48.1% under frequency-swept sinusoidal excitation. Comprehensive simulations and hardware-in-the-loop (HIL) experiments, utilizing a commercial CDC damper, validate the superiority of the proposed method. Under a standard speed bump impact, simulation results show that PC-SHADD reduces peak sprung mass acceleration by 32.0% compared to a passive system-a substantial improvement over the negligible 0.8% offered by SH-ADD-without demanding additional suspension travel. Under stochastic random road excitations, PC-SHADD consistently improves ride comfort, reducing RMS acceleration by an average of 21.2%. These findings are robustly corroborated by HIL tests, where PC-SHADD achieved a 28.7% reduction in peak acceleration over the speed bump and a 17.1% RMS acceleration reduction on a Class B random road, outperforming the baseline SH-ADD in both transient and stochastic scenarios. The performance gains are concentrated in the critical 0.7-7 Hz frequency band, confirming the enhanced accuracy of the proposed phase correction switching logic.

In the face of external disturbances affecting the trajectory tracking of quadrotors, a control scheme targeted at accurate position and attitude trajectory tracking was designed. Initially, a quadrotor dynamic model, essential for control design, was derived. Adaptive integral backstepping control (AIBS) was then employed within the position loop, enabling the upper boundaries of disturbances to be estimated through adaptive estimation. Subsequently, a new adaptive backstepping fast nonsingular integral terminal sliding mode control (ABFNITSM) was proposed to enable adherence to the desired Euler angles. Rapid convergence and accurate tracking were facilitated by the incorporation of the nonsingular terminal sliding mode and an integral component. The dead zone technique was deployed to curtail estimation errors, while a saturation function was used to eradicate the phenomenon of chattering. Finally, to validate the proposed control scheme, simulation experiments were conducted in the Simulink environment, and the results were contrasted with those obtained from traditional integral terminal sliding mode control (ITSM) and integral backstepping control (IBS), providing evidence of the effectiveness of the proposed method.

To address the trajectory tracking issue for quadrotors working under the influence of external disturbances, a control scheme is designed for position and attitude trajectory tracking. Firstly, the quadrotor dynamic model is derived for control design. Secondly, adaptive integral backstepping control (AIBS) is employed in the position loop in which adaptive estimation is used to estimate the upper bounds of disturbances. Subsequently, a new adaptive backstepping fast nonsingular integral terminal sliding mode control (ABFNITSM) is proposed to follow desired Euler angles. The intro-duction of fast nonsingular terminal sliding mode and integral element is to realize fast convergence and accurate tracking. Dead zone technique is applied to minimize estimation errors. Besides, a saturation function is utilized to eliminate chattering phenomenon. Finally, simulation experiments are conducted under the Simulink environment. The proposed control is verified by comparing it with traditional integral terminal sliding mode control (ITSM) and integral backstepping control (IBS).