Numerical Experience with a Reduced Hessian Methodfor Large Scale Constrained Optimization
Computational Optimization and Applications
Subjective measurement of cosmetic defects using a Computational Intelligence approach
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
Implementation of hybrid ANN-PSO algorithm on FPGA for harmonic estimation
Engineering Applications of Artificial Intelligence
Adaptive dynamic CMAC neural control of nonlinear chaotic systems with L2 tracking performance
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
Engineering Applications of Artificial Intelligence
Adaptive neural complementary sliding-mode control via functional-linked wavelet neural network
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
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Automotive engines are multivariable system with severe non-linear dynamics, and their modelling and control are challenging tasks for control engineers. Current control of engine used look-up table combined with proportional and integral (PI) control and is not robust to system uncertainty and time varying effects. In this paper the model predictive control strategy is applied to engine air/fuel ratio control using neural network model. The neural network model uses information from multivariables and considers engine dynamics to do multi-step ahead prediction. The model is adapted in on-line mode to cope with system uncertainty and time varying effects. Thus, the control performance is more accurate and robust compared with non-adaptive model based methods. To speed up algorithm calculation, different optimisation algorithms are investigated and performance compared. Finally, the developed method is evaluated on a well-known engine benchmark, a simulated mean value engine model (MVEM). The simulation results demonstrate the effectiveness of the developed method.