LAUSR

Electric mechanical transmission (EMT) is a low-cost production for the electric vehicle to expend its torque and speed. Gear shifting is the essential function of EMT, but it is a multi-stage process with nonlinearity and uncertainty. In practice, gear shifting control is a difficult problem because of the bad consistency and large shift shock. Hence, a single control method is hard to solve it with satisfying performance. In this paper, motivated by the analysis of experimental results, a novel multi-stage gear shifting control scheme is proposed to improve shifting position tracking performance and decrease shift shock. First, the entire process is divided into three stages and each stage is modeled based on historical data and theoretical analysis. Then, a multi-stage scheme is designed, which is composed of iterative learning control (ILC), linear quadratic regulator (LQR), and $\boldsymbol {H_{2}}$ control based on Takagi and Sugeno (T-S) model corresponding to each stage. Finally, the feasibility and effectiveness of the proposed scheme are verified by bench experiments, which might provide guides for the engineering application of the EMT.