Adaptive neural network model based predictive control for air-fuel ratio of SI engines

The dynamics of air manifold and fuel injection of the spark ignition engines are severely nonlinear. This is reflected in nonlinearities of the model parameters in different regions of the operating space. Control of the engines has been investigated using observer-based methods or sliding-mode methods. In this paper, the model predictive control (MPC) based on a neural network model is attempted for air-fuel ratio, in which the model is adapted on-line to cope with nonlinear dynamics and parameter uncertainties. A radial basis function (RBF) network is employed and the recursive least-squares (RLS) algorithm is used for weight updating. Based on the adaptive model, a MPC strategy for controlling air-fuel ratio is realised to a nonlinear simulation of the engines, and its control performance is compared with that of a conventional PI controller. A reduced Hessian method, a new developed sequential quadratic programming (SQP) method for solving nonlinear programming (NLP) problems, is implemented to speed up the nonlinear optimisation in MPC.

[1]  Yonghong Tan,et al.  Neural-networks-based nonlinear dynamic modeling for automotive eng , 2000, Neurocomputing.

[2]  C. Pinello,et al.  Automotive engine control and hybrid systems: challenges and opportunities , 2000, Proceedings of the IEEE.

[3]  M Sunwoo,et al.  An adaptive sliding mode controller for air-fuel ratio control of spark ignition engines , 2001 .

[4]  Riccardo Scattolini,et al.  Modelling the volumetric efficiency of ic engines: Parametric, non-parametric and neural techniques , 1996 .

[5]  Gene F. Franklin,et al.  Digital control of dynamic systems , 1980 .

[6]  Marimuthu Palaniswami,et al.  Gaussian networks for fuel injection control , 2001 .

[7]  Marimuthu Palaniswami,et al.  Model Predictive Control of a Fuel Injection System with a Radial Basis Function Network Observer , 2002 .

[8]  Elbert Hendricks,et al.  Modelling of the Intake Manifold Filling Dynamics , 1996 .

[9]  Ignacio E. Grossmann,et al.  Retrospective on optimization , 2004, Comput. Chem. Eng..

[10]  Elbert Hendricks,et al.  A Generic Mean Value Engine Model for Spark Ignition Engines , 2000 .

[11]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[12]  Jorge Nocedal,et al.  Numerical Experience with a Reduced Hessian Method for Large Scale Constrained Optimization , 1995, SIAM J. Optim..

[13]  Jorge Nocedal,et al.  A Reduced Hessian Method for Large-Scale Constrained Optimization , 1995, SIAM J. Optim..

[14]  B. Foss,et al.  A new optimization algorithm with application to nonlinear MPC , 2004 .

[15]  J. Karl Hedrick,et al.  An observer-based controller design method for improving air/fuel characteristics of spark ignition engines , 1998, IEEE Trans. Control. Syst. Technol..