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Shift quality is an important indicator to measure the performance of dual-clutch transmissions (DCT). To obtain optimal driving comfort and reduce the vehicle jerk as much as possible, this paper proposes an integrated gearshift controller to control the engine and the on-coming clutch in inertia phase. First of all, a dynamic model of DCT during gearshift is established. Key factors determining shift quality are analyzed. In order to reduce the vehicle jerk, a reference trajectory of the engine speed and the derivative of the desired torque transferred by the on-coming clutch in inertia phase are programmed respectively. A back-stepping sliding mode controller (BPSMC) is designed to make the actual engine speed track the reference trajectory and an incremental proportional-integrative (PI) controller is designed to make the actual clutch torque to track the desired clutch torque.

Autonomous vehicle performance is increasingly highlighted in many highway driving scenarios, which leads to more priorities to vehicle stability as well as tracking accuracy. In this paper, a nonlinear model predictive controller for autonomous vehicle trajectory tracking is designed and verified through a real-time simulation bench of a virtual test track. The dynamic stability constraints of nonlinear model predictive control (NLMPC) are obtained by a novel quadrilateral stability region criterion instead of the conventional phase plane method using the double-line region. First, a typical lane change scene of overtaking is selected and a new composited trajectory model is proposed as a reference path that combines smoothness of sine wave and comfort of linear functional path. Reference lateral velocity, azimuth angle, yaw rate, and front wheel steering angle are subsequently taken into account.