System identification: theory for the user
System identification: theory for the user
Numerical Experience with a Reduced Hessian Methodfor Large Scale Constrained Optimization
Computational Optimization and Applications
Digital Control of Dynamic Systems
Digital Control of Dynamic Systems
A Neural Network Model Based MPC of Engine AFR with Single-Dimensional Optimization
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Robust air/fuel ratio control with adaptive DRNN model and AD tuning
Engineering Applications of Artificial Intelligence
Residual generation for fault detection of internal combustion engine
Proceedings of the 12th International Conference on Computer Systems and Technologies
Planning for mechatronics systems-Architecture, methods and case study
Engineering Applications of Artificial Intelligence
Model predictive engine air-ratio control using online sequential relevance vector machine
Journal of Control Science and Engineering - Special issue on Advanced Control in Micro-/Nanosystems
Enhanced model and fuzzy strategy of air to fuel ratio control for spark ignition engines
Computers & Mathematics with Applications
Comparative analysis of artificial neural networks and dynamic models as virtual sensors
Applied Soft Computing
Fault detection and isolation for PEM fuel cell stack with independent RBF model
Engineering Applications of Artificial Intelligence
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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.