Yiting Kang

Qijun Liao, Jue Yang, Subhash Rakheja et al.

This paper proposes a novel method to estimate the wheel dynamic load based on the gas pressure of a hydro-pneumatic suspension. A nonlinear coupled model between suspension chamber pressure and tire-ground contact force is developed, integrating suspension dynamics with its nonlinear stiffness characteristics. An iterative algorithm is developed to estimate wheel dynamic load using data from only one single pressure sensor, thereby eliminating the reliance on traditional tire models and complex multi-sensor fusion frameworks. This method effectively reduces hardware redundancy and minimizes the propagation of measurement errors. The proposed model is experimentally validated on a dedicated suspension test bench, demonstrating satisfactory agreement between the measured and estimated data. Additionally, co-simulation with TruckSim verifies the accuracy of both the calculated damping force and wheel dynamic load, demonstrating the effectiveness of the model on characterizing the mechanical behavior of the hydro-pneumatic suspension system. The proposed method provides a practical, low-cost, and efficient solution with minimal hardware dependencies.

Qijun Liao, Jue Yang, Yiting Kang et al.

Deep reinforcement learning excels in continuous control but often requires extensive exploration, while physics-based models demand complete equations and suffer cubic complexity. This study proposes Hybrid Energy-Aware Reward Shaping (H-EARS), unifying potential-based reward shaping with energy-aware action regularization. H-EARS constrains action magnitude while balancing task-specific and energy-based potentials via functional decomposition, achieving linear complexity O(n) by capturing dominant energy components without full dynamics. We establish a theoretical foundation including: (1) functional independence for separate task/energy optimization; (2) energy-based convergence acceleration; (3) convergence guarantees under function approximation; and (4) approximate potential error bounds. Lyapunov stability connections are analyzed as heuristic guides. Experiments across baselines show improved convergence, stability, and energy efficiency. Vehicle simulations validate applicability in safety-critical domains under extreme conditions. Results confirm that integrating lightweight physics priors enhances model-free RL without complete system models, enabling transfer from lab research to industrial applications.