To solve the difficulties of modeling the starting system of dual clutch transmission (DCT) vehicles, a state-dependent autoregressive with exogenous variables (SD-ARX) model whose functional coefficients are approximated by sets of radial basis function (RBF) networks is proposed to describe the dynamic characteristics of DCT vehicles starting process in this study. The validity of this modelling approach is verified via a real vehicle test. On this basis, a nonlinear predictive controller based on SD-ARX model is designed. In addition, the physical constraints of this system, including control variables (change rate of engine torque and clutch torque) and state variables (engine speed and clutch speed), are also taken into account during the controller design process via limiting the relevant parameters in particle swarm optimization or setting saturation demand in control program. To verify the validity and merits of the proposed control approach, many sets of simulation analysis in different driving intentions are conducted. Simulation results shown that: the proposed control approach can well control the starting process of DCT vehicles and effectively reflect the demand of driver's intention; compared with the conventional control method, SD-ARX-MPC can improve the starting performance; the proposed approach is robust to a certain extent according to simulation results under changed starting conditions. INDEX TERMS Starting control, radial basis function (RBF) networks, data-driven, state-dependent autoregressive with exogenous (SD-ARX) model, predictive control, nonlinear system.

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For the purpose of improving the performance of Electric Power Steering System (EPS), phase compensation must be added on the control strategies. Two methods, including speed feedback and differentiation, are discussed in this paper. As to the latter, the emphasis is made on the differences of ideal differentiation and actual one. Simulation results show that the above mentioned methods could obviously increase the stability of EPS system. The control effects are also improved after imbedding the methods to the self-designed EPS system. I. (EPS) [1][2] EPS [3] EPS ( ) II. A. ( ) ( ) Matlab/Simulink EPS 1 ( ) [4] 1 EPS 1 1 EPS h J kg·m 2 h B N·m·s/rad h T N·m h rad s K N·m/rad s T N·m m J ( ) kg·m 2 m B N·m·s/rad m ( ) rad a T N·m 1 ( ) rad I A 1 J kg·m 2 1 B N·m·s/rad r T N·m r K N·m/rad N