Shared lateral control with on-line adaptation of the automation degree for driver steering assist system: A weighting design approach
This work addresses the problem of improving driver-assist system performance for automotive applications, but it is incremental as it builds on existing control techniques.
The paper tackles shared lateral control for driver steering assist systems by introducing a fictive nonlinear term to represent driver activity, enabling the controller to adapt to driver behaviors and handle state constraints and input saturation, validated through simulations.
This paper addresses the shared lateral control for both lane-keeping and obstacle avoidance tasks of a driver steering assist system (DSAS). In this work, we propose a novel approach to deal with the interactions between the human (driver) and the machine (DSAS) by introducing into the vehicle system a fictive nonlinear term representing the driver activity. In this way, the actions of the DSAS are computed according to the driver behaviors (actions and intentions). Based on Takagi-Sugeno control technique together with Lyapunov stability tools, the designed controller is able to handle a large range of variation of vehicle longitudinal speed. In particular, this controller can deal with the system state constraints and also the control input saturation. As will be discussed later, the consideration of these constraints into the control design improves significantly the closed-loop performance under various driving situations. The interests of the proposed method are validated by simulations.