Abstract
For the TFC35 steer-by-wire forklift, a linear 3-degree-of-freedom forklift model is given, which provides a verification model for variable steering ratio design. The concept of ideal steering ratio is expounded. The variable steering ratio algorithm based on constant yaw rate gain and the variable steering ratio algorithm based on constant lateral acceleration gain are studied. A variable steering ratio algorithm based on two kinds of gain combination is proposed. The advantages and disadvantages of the above three static variable steering ratio algorithms based on constant gain are simulated and analyzed. Aiming at complex working conditions, a dynamic variable steering ratio control scheme based on fuzzy neural network is proposed. The definition, implementation steps, and adjustment algorithms of each layer of fuzzy neural network are given. The final experiment results show that the dynamic variable steering ratio method based on fuzzy neural network is more resistant to the disturbance of its own parameters and can be used in complex dynamic conditions, which helps to improve the handling stability of the forklift.
Highlights
Due to the increasingly serious environmental problems, people pay more and more attention to the pollution caused by the vehicles
The variable steering ratio (VSR) control scheme based on fuzzy neural network (FNN) can avoid the artificial design of the fuzzy control rules and the difficulty of selecting the amplitude of the membership function, which means the FNN model can achieve the desired performance index
The simulation results show that VSR algorithm based on two kinds of gain combination can more effectively improve the steering performance of the forklift than the VSR algorithm based on constant yaw rate gain and the VSR algorithm based on constant lateral acceleration gain
Summary
Due to the increasingly serious environmental problems, people pay more and more attention to the pollution caused by the vehicles. The handling stability of the SBW vehicle under different disturbances was studied.[14] Wang et al designed a new VSR control strategy, the inputs are velocity, lateral deviation, yaw rate error, and hand-wheel angle, and the output is the driver’s desired steering ratio. L = a + b, kay , and kvg are the high-order steering coefficients, reflecting the effect of roll movement on the steering ratio, it can be chosen based on the model of vehicle under study. These two values are obtained through multiple simulation
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