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High hydrocarbon (HC) emission during a cold start still remains one of the major emission control challenges for spark ignition (SI) engines in spite of about three decades of research in this area. This paper proposes a cold start HC emission control strategy based on a reduced order modeling technique. A novel singular perturbation approximation (SPA) technique, based on the balanced realization principle, is developed for a nonlinear experimentally validated cold start emission model. The SPA reduced model is then utilized in the design of a model-based sliding mode controller (SMC). The controller targets to reduce cumulative tailpipe HC emission using a combination of fuel injection, spark timing, and air throttle / idle speed controls. The results from the designed multi-input multi-output (MIMO) reduced order SMC are compared with those from a full order SMC. The results show the reduced SMC outperforms the full order SMC by reducing both engine-out and tailpipe HC emission.

This paper proposes a methodology for collision prevention in car following scenarios. For this purpose, Emotional Learning Fuzzy Inference System (ELFIS) approach is used to simulate and predict the behavior of a driver-vehicle-unit in a short time horizon ahead in the future. Velocity of the follower vehicle and relative distance between the follower and the lead vehicles are predicted in a parallel structure. Performance of the proposed algorithm is assessed using real traffic data and superior accuracy of this method is demonstrated through comparisons with another available technique (ANFIS). The predicted future driving states are then used to judge about safety of the current driving pattern. The algorithm is used to generate a warning message while a safe-distance keeping measure is violated in order to prevent a collision. Satisfactory performance of the proposed method is demonstrated through simulations using real traffic data.

In this paper, an integrated vehicle dynamics control system is designed to improve vehicle yaw stability by coordinating control of Active Front Steering (AFS) and Direct Yaw-moment Control (DYC) based on a new concept. The control system has a hierarchical structure and consists of two controlling layers. A fuzzy logic controller is used in the upper layer (Yaw Rate Controller) to keep the yaw rate in its desired value. The yaw rate error and its rate of change are applied to upper controlling layer as inputs, where the direct yaw moment control signal and the steering angle correction of the front wheels are built as the outputs. In the lower layer (Fuzzy Integrator), a fuzzy logic controller is designed based on the working region of the lateral tire forces to determine percentage of usage of upper layer controlling inputs.

The desired performance of a spark ignition engine is highly related to the position of the spark advance, and consequently to the beginning of the stable combustion inside cylinder. The magnitude of the generated ion current signal, measured by a sensor inside cylinder, is closely correlated to the cylinder pressure magnitude. Their maximum values almost coincide at the same crank angle position. In this paper, a feedback control system is designed to fix the position of the maximum pressure at a given desired value of the crank angle. Noting that SI engines are highly nonlinear systems, a fuzzy logic controller is developed.