Flatness-Based Adaptive Fuzzy Control Of Spark-Ignited Engines

Abstract

An adaptive fuzzy controller is designed for spark-ignited (SI) engines, under the constraint that the system's model is unknown. The control algorithm aims at satisfying the H ∞ tracking performance criterion, which means that the influence of the modeling errors and the external disturbances on the tracking error is attenuated to an arbitrary desirable level. After transforming the SI-engine model into the canonical form, the resulting control inputs are shown to contain nonlinear elements which depend on the system's parameters. The nonlinear terms which appear in the control inputs are approximated with the use of neuro-fuzzy networks. It is shown that a suitable learning law can be defined for the aforementioned neuro-fuzzy approximators so as to preserve the closed-loop system stability. With the use of Lyapunov stability analysis it is proven that the proposed adaptive fuzzy control scheme results in H ∞ tracking performance. The efficiency of the proposed adaptive fuzzy control scheme is checked through simulation experiments.

Nonlinear feedforward controller design of gasoline engine air-path system for reducing engine torque overshoot

“…In Rigatos et al, 12 the concept of flatness was employed for feedback linearization, and a single-input, multi-output feedback control system utilizing the Kalman filter was presented. Rigatos and Siano 13 proposed a nonlinear adaptive-fuzzy control strategy using the concept of flatness for SI engines. Hall et al 14 applied the concept of flatness to combustion control in diesel engines to ensure a smooth transition between different combustion modes, namely conventional diesel combustion and premixed charge compression ignition.…”