A clutch control strategy is proposed to improve the shift quality of power shift transmission and solve the power cycle phenomenon in the shifting process. The strategy effectively avoids the power cycle phenomenon by analyzing the change in the transmission torque and the relationship between the rotating speed of the driving and driven disks of a shifting clutch. The evaluation indexes of the shift quality of power shift transmission are sliding friction power loss, impact degree, and torque oscillation amplitude. Particle swarm optimization (PSO), combined with a genetic algorithm, is used to optimize the shift quality of power shift transmission, solving the problem in which basic PSO frequently converges to the local optimal solution. Simulations and experiments are performed on the optimization results. The simulation and test results show that the proposed control strategy can ensure that the rotating speed of the driving disk is always higher than that of the driven disk when the clutch is insliding and wearing states. Thus, this control strategy effectively avoids power cycle when shifting and improves shift quality.
An anti-roll hydraulic cylinder is designed as an executive mechanism of the lateral stability control to provide lateral support force on the basis of an analysis of the structure of the forklift and the mechanism of lateral instability. A lateral stability layered control method based on Takagi–Sugeno (T–S) fuzzy neural network is proposed, divides the forklift lateral stability control into the upper identification layer, the middle control layer and the lower executive layer. Simulation and real vehicle test results indicate that the forklift lateral stability control method based on the T–S fuzzy neural network can effectively identify the forklift driving state, reduce the forklift rollover possibility and improve forklift safety under limited working conditions.
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