Reconstruction of cylinder pressure for SI engine using recurrent neural network |
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Authors: | Samir Saraswati Satish Chand |
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Affiliation: | (1) Department of Mechanical Engineering, MNNIT, Allahabad, 211004, India |
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Abstract: | Model predictive control (MPC) frequently uses online identification to overcome model mismatch. However, repeated online
identification does not suit the real-time controller, due to its heavy computational burden. This work presents a computationally
efficient constrained MPC scheme using nonlinear prediction and online linearization based on neural models for controlling
air–fuel ratio of spark ignition engine to its stoichiometric value. The neural model for AFR identification has been trained
offline. The model mismatch is taken care of by incorporating a PID feedback correction scheme. Quadratic programming using
active set method has been applied for nonlinear optimization. The control scheme has been tested on mean value engine model
simulations. It has been shown that neural predictive control with online linearization using PID feedback correction gives
satisfactory performance and also adapts to the change in engine systems very quickly. |
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