Articulated steering vehicles (ASV) are widely used in many industries for their high efficiency and excellent maneuverability. The autonomous driving and intelligent control of ASV are extremely critical owing to the operation characteristics. As a very important parameter, the tire-road friction coefficient (TRFC) determines the extreme tire force directly in the process of intelligent control. However, it cannot be obtained with the existing methods for the harsh environment and special structure of ASV. This paper proposed a two-layer model-based method of tire-road friction coefficient estimation for ASV. The dynamic models of ASV in the XY plane, including the longitudinal and lateral models of frames, tire forces, and steering system models, are established first. The dynamic models are embedded into the upper layer with a Kalman filter (KF) to estimate the tire forces in longitudinal and lateral directions. During the process, some self-contained sensors, including the state sensors of frames and steering system, are used to provide the observation data. In the lower layer, a recursive least square (RLS) method with a forgetting factor is used to obtain the TRFC and tire stiffness parameters with the aid of the tire model. The simulation and field test are carried out to validate the method under comprehensive conditions, in which different steering commands, velocities, and roads are included. The simulation and field test results show that the forgetting factor has a significant influence on the convergence and robustness of the proposed method. The forgetting factor τ = 0.95 is used in the field test, the estimation result of dry concrete road friction coefficient is around 0.83. The results indicated that the proposed method can obtain the TRFC and tire parameters dynamically for ASVs.

Hydraulic vibratory hammer is a key equipment during piling process, and mechanical, hydraulic part of the hammer and workload are coupled with each other during operation. The vibration performance depends on the design parameters and the driving system. In order to investigate the vibration performance, a coupled dynamic model is established for hydraulic vibratory hammer, in which the mechanical model of hammer, hydraulic model for driving, and the model of workload are included. The filed test was carried out to validate the dynamic model under idle and operating conditions. Pressure and flow of the hydraulic driving system and acceleration of the mechanical part were obtained during different test conditions. The results of test and simulation were analyzed in time and frequency domain to validate the coupled dynamic model. Then, effects of different eccentric block parameters on the vibration performance were investigated based on the validated model, including radius, thickness, and angle of the eccentric block. Further the design parameters of optimal vibration performance of the hammer are obtained under the constraints of hammer structure, in which the angle is 90°, thickness is 190 mm, and radius is 175 mm.Article Highlights We established a coupled dynamic model of the hydraulic vibratory hammer, including the mechanical model of hammer, hydraulic model for driving, and the simplified model of workload.; The field test is proceeded to validate the model under idle and operating condition; the dynamic response of the mechanical part and hydraulic driving part is analyzed in time and frequency domain; The performance of the hammer is analyzed with the coupled dynamic model, in which better performance of the hammer is obtained. The model-based method also provides a rapid and economical solution for the performance optimization of the hammer.