Ultraviolet-curable resin was introduced as a bonding agent into the fabrication process of precision abrasive machining tools in this study, aiming to deliver a rapid, flexible, economical, and environment-friendly additive manufacturing process to replace the hot press and sintering process with thermal-curable resin. A laboratory manufacturing process was established to develop an ultraviolet-curable resin bond diamond lapping plate, the machining performance of which on the ceramic workpiece was examined through a series of comparative experiments with slurry-based iron plate lapping. The machined surface roughness and weight loss of the workpieces were periodically recorded to evaluate the surface finish quality and the material removal rate. The promising results in terms of a 12% improvement in surface roughness and 25% reduction in material removal rate were obtained from the ultraviolet-curable resin plate-involved lapping process. A summarized hypothesis was drawn to describe the dynamically-balanced state of the hybrid precision abrasive machining process integrated both the two-body and three-body abrasion mode.

This paper puts forward the robust control problem of rail vehicle virtual coupling platoon that is subject to nonlinear and time-varying uncertainties. The uncertainties are caused by external disturbances and parameter variations. To this end, this paper designs a robust controller based on the Udwadia-Kalaba (UK) equation. First, we introduce the concept of virtual coupling and establish the longitudinal dynamics model of the rail vehicle. In order to ensure the control objective of the virtual coupling process, the spatial constraint on position and bidirectional inequality constraints for the spacing error between the marshalling trains are established. After changing the bounded state into the unbounded state through the state transformation, the UK approach can be used. Then, the constraint-following robust controller design is based on the UK approach and Lyapunov stability theory. The controller can render the unbounded state eventual uniform boundedness and uniform ultimate boundedness, which also satisfies the spatial constraint and the bidirectional restriction for the spacing error. Moreover, an optimized design scheme for the tunable parameter of this controller is proposed, which minimizes the control cost and comprehensive index of system performance. Finally, numerical simulations are given to illustrate the effectiveness of the proposed methods.

This paper presents a fuzzy fixed‐time learning path tracking control strategy for autonomous vehicles with saturated input and uncertainties. First, Takagi‐Sugeno (T‐S) model is designed to realize an accurate description of the nonlinear path tracking systems. The T‐S model is the weighted sum of a series of linear dynamics models. The weighting coefficients are described by fuzzy membership functions. Then, with the consideration of the saturated input, a fixed‐time learning control is proposed. It contains a learning law to estimate the uncertainties of the system on‐line. The fixed‐time convergence and the robustness properties are analyzed through Lyapunov stability theory. Finally, the effectiveness of the proposed control method has been verified through the CarSim/Simulink co‐simulations under different driving conditions. The results demonstrate that the developed controller significantly improves the performance of path tracking and follows the desired path well even in the case of parameter uncertainties and external disturbances.