This paper presents an obstacle avoidance scheme for autonomous vehicles as an active safety procedure in unknown environments. Safe trajectories are generated using the non-linear model predictive framework, in which the simplified dynamics of the vehicle are used to predict the state of the vehicle over the look-ahead horizon. To compensate for the slight dissimilarity between the simplified model and the actual vehicle, a separate controller is designed to track the generated trajectory. The longitudinal dynamics of the vehicle are controlled using the inverse dynamics of the vehicle powertrain model, and the lateral dynamics are controlled using a linear quadratic regulator. In the non-linear model predictive framework, to obtain safe trajectories, local obstacle information is incorporated into the performance index using a parallax-based method. Simulation results on a full non-linear vehicle model show that the proposed combination of model-predictive-control-based trajectory generation and tracking controller gives satisfactory online obstacle avoidance performance.
This paper describes a control-oriented charge mixing and Homogeneous Charge Compression Ignition (HCCI) combustion model, where the in-cylinder charge is divided into the well-mixed and unmixed zones as the result of charge mixing. Simplified fluid dynamics is used to predict the residual gas fraction at the intake valve closing, which defines the size of the unmixed zone, during real-time simulations. The unmixed zone size not only determines how well the in-cylinder charge is mixed, which affects the start of HCCI combustion, the peak in-cylinder pressure and also the temperature during the combustion process. The developed model was validated in the HIL (hardware-in-the-loop) simulation environment. The HIL simulation results show that the proposed charge mixing and HCCI combustion model provides better agreement with these of the corresponding GT-Power than the previously developed one-zone model.
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