Framework for fault-tolerant speed tracking control of gasoline engines using the first principle-based engine model

Optimal engine torque management,a fundamental objective,depends predominantly on engine speed tracking performance.It ensures to attain desired speed profile in the presence of uncertainties,disturbances and malfunctions.On the other hand,certain requirements such as emissions control,fuel efficiency and drivability are degraded in case of poorspeed tracking.Furthermore,constraints on engine speed tracking performance are even more stringent for hybrid power-train architecture as crankshaft speed and engine torque are the basic variables for coordinated control.Speed tracking is also considered essential for gearshift control ofthe automatic transmission.In this research work,a framework for fault-tolerant speed tracking of the gasoline engine is proposed using the First Principle-based Engine Model(FPEM).A high-fidelity direct relationship between fuel injection input and engine speed is derived by the transformation of FPEM.Fault is induced in the fuel injection subsystem to generate the torque imbalance.Using the proposed framework,a second-order sliding mode-based control technique is applied to track desired speed profile by mitigating the faultsin the fuel injection subsystem.Reference data acquired from the engine test rig is used to demonstrate the offline validity and fault tolerance capabilities of the proposed framework in MATLAB/Simulink.

Tracking of time-evolving sound speed profiles with an auto-regressive state-space model

An approach for time-evolving sound speed profiles tracking in shallow water is discussed. The inversion of time-evolving sound speed profiles is modeled as a state-space estimation problem, which includes a state equation for predicting the time-evolving sound speed profile and a measurement equation for incorporating local acoustic measurements. In the paper, auto-regression （AR） method is introduced to obtain a high-order AR evolution model of the sound speed field time variations, and the ensemble Kalman filter is utilized to track the sound speed field. To validate the approach, the accuracy in sound speed estimation is analyzed via a numerical implementation using the ASIAEX experimental environment and the sound velocity measurement data. Compared with traditional approaches based on the state evolution represented as a random walk, simulation results show the proposed AR method can effectively reduce the tracking errors of sound speed, and still keep good tracking performance at low signal-to-noise ratios.