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Receding horizon online optimization for torque control of gasoline engines
Affiliation:1. Department of Applied Mathematics, Hakim Sabzevari University, Sabzevar, Iran;2. Institute computational Mathematics, Technische Universität Braunschweig, Braunschweig D-38106, Germany;3. Laboratoire Analyse, Geometrie et Applications, Paris 13 University, France
Abstract:This paper proposes a model-based nonlinear receding horizon optimal control scheme for the engine torque tracking problem. The controller design directly employs the nonlinear model exploited based on mean-value modeling principle of engine systems without any linearizing reformation, and the online optimization is achieved by applying the Continuation/GMRES (generalized minimum residual) approach. Several receding horizon control schemes are designed to investigate the effects of the integral action and integral gain selection. Simulation analyses and experimental validations are implemented to demonstrate the real-time optimization performance and control effects of the proposed torque tracking controllers.
Keywords:Receding horizon control  Nonlinear optimization  Model predictive control  Torque tracking control  Gasoline engine
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