Gear engagement is the most important part in gear-shift process of automated manual transmission (AMT). However, it is practical to encounter complicated nonlinearities, uncertainties, and multistage characteristics in the system model, so the controller design for the AMT gear engagement becomes challenging. This paper proposes a dual-loop self-learning fuzzy control framework. In the outer loop, the self-learning rules based on fuzzy logic is designed to adjust desired trajectory of actuator motor. In the inner loop, the gear engagement is divided into three stages, and a fuzzy controller with model reference self-learning algorithm is designed, which controls the actuator motor to track the desired trajectory. Besides, the control parameters could be adjusted to be optimal automatically when the parameters change. Results of simulations and experiments indicate that the proposed method is able to realize the smooth and fast control of gear engagement. In addition, the self-learning fuzzy controller can be extended to deal with other nonlinear systems with uncertain and even unknown parameters.

中文翻译：

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AMT 齿轮啮合双环自学习模糊控制：设计与实验


齿轮啮合是手自一体变速箱（AMT）换档过程中最重要的部分。然而，实际系统模型中会遇到复杂的非线性、不确定性和多级特性，因此AMT齿轮啮合的控制器设计变得具有挑战性。本文提出了一种双环自学习模糊控制框架。在外环中，设计了基于模糊逻辑的自学习规则来调整执行器电机的期望轨迹。在内环中，齿轮啮合分为三个阶段，设计了具有模型参考自学习算法的模糊控制器，控制执行器电机跟踪期望的轨迹。此外，当参数发生变化时，可以自动调整控制参数至最优。仿真和实验结果表明，该方法能够实现齿轮啮合的平滑、快速控制。此外，自学习模糊控制器可以扩展到处理其他具有不确定甚至未知参数的非线性系统。