Adaptive and Learning Control for SI Engine Model With Uncertainties

Air--fuel ratio control is a challenging control problem for port-fuel-injected and throttle-body-fuel-injected spark ignition (SI) engines, since the dynamics of air manifold and fuel injection of the SI engines are highly nonlinear and often with unmodeled uncertainties and disturbance. This paper presents nonlinear control approaches for multi-input multi-output engine models, by developing adaptive control and learning control design methods. Theoretical proofs are established that ensure that proposed controllers are able to give asymptotical tracking performance. As a comparison, the method applying global linearizing controller can give accurate tracking for the engine model without uncertainty and disturbance, but it fails to keep tracking performance when uncertainty is incorporated into the system. Adaptive control and learning control approaches are capable of dealing with both constant uncertainty and time-varying periodic uncertainty. Simulation results illustrate the efficacy of the proposed controllers.