Spark ignition (SI) engines running at very lean conditions demonstrate significant nonlinear behavior by exhibiting cycle-to-cycle dispersion of heat release even though such operation can significantly reduce NOx emissions and improve fuel efficiency by as much as 5-10%. A suite of neural network (NN) controller without and with reinforcement learning employing output feedback has shown ability to reduce the nonlinear cyclic dispersion observed under lean operating conditions. The neural network controllers consists of three NN: a) A NN observer to estimate the states of the engine such as total fuel and air; b) a second NN for generating virtual input; and c) a third NN for generating actual control input. For reinforcement learning, an additional NN is used as the critic. The uniform ultimate boundedness of all closed-loop signals is demonstrated by using Lyapunov analysis without using the separation principle. Experimental results on a research engine at an equivalence ratio of 0.77 show a drop in NOx emissions by around 98% from stoichiometric levels. A 30% drop in unburned hydrocarbons from uncontrolled case is observed at this equivalence ratio.
B. C. Kaul et al., "Neural Network-Based Output Feedback Controller for Lean Operation of Spark Ignition Engines," Proceedings of the 2006 American Control Conference, Institute of Electrical and Electronics Engineers (IEEE), Jan 2006.
The definitive version is available at http://dx.doi.org/10.1109/ACC.2006.1656497
2006 American Control Conference
Electrical and Computer Engineering
Mechanical and Aerospace Engineering
Keywords and Phrases
Lyapunov Analysis; Lyapunov Methods; NOx Emission; Closed Loop Systems; Closed-Loop Signals; Cycle-To-Cycle Dispersion; Discrete Time Systems; Feedback; Heat Release; Internal Combustion Engines; Learning (Artificial Intelligence); Neural Network Controllers; Neural Network Observer; Neurocontrollers; Nonlinear Behavior; Nonlinear Control Systems; Nonlinear Cyclic Dispersion; Observers; Output Feedback Controller; Reinforcement Learning; Spark Ignition Engines; Stability; Unburned Hydrocarbon; Uniform Ultimate Boundedness
Article - Conference proceedings
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