Combustion Timing Control of Natural Gas HCCI Engines Using Physics-Based Modeling and LQR Controller

Homogeneous Charge Compression Ignition (HCCI) Engines hold promises of being the next generation of internal combustion engines due to their ability to produce high thermal efficiencies and low emission levels. HCCI combustion is achieved through the auto-ignition of a compressed homogenous fuel-air mixture, thus making it a "fusion" between spark-ignition and compression-ignition engines. The main challenge in developing HCCI engines is the absence of a combustion trigger hence making it difficult to control its combustion timing.

The aim of this research project is to model and control a natural gas HCCI engine. Since HCCI depends primarily on temperature and chemical composition of the mixture, Exhaust Gas Recirculation (EGR) is used to control ignition timing. In this research, a thermodynamical, physics-based nonlinear model is developed to capture the main features of the HCCI engine. In addition, the Modified Knock Integral Model (MKIM), used to predict ignition timing, is optimized. To validate the nonlinear model, ignition timing under varying conditions using the MKIM approach is shown to be in accordance with data acquired from a model developed using a sophisticated engine simulation program, GT-Power. Most control strategies are based on a linear model, therefore, the nonlinear model is linearized using the perturbation method. The linear model is validated by comparing its performance with the nonlinear model about a suitable operating point.

The control of ignition timing can be defined as a regulation process where the goal is to force the nonlinear model to track a desired ignition timing by controlling the EGR ratio. Parameters from the linear model are used to determine the gains of the LQR controller. The performance of the controller is validated by implementing it on the nonlinear model and observing its ability to track the desired timing with 0.5% error within a certain operating range. To increase the operating range of the controller and reduce steady-state error, an integrator is added to the LQR. Finally, it is shown that the LQR controller is able to successfully reject disturbance, parameter variation, as well as noise.