Fuel cell starvation control using model Predictive technique with laguerre and exponential weight functions
Fuel cell system is a complicated system that requires an efficient controller. Model predictive control is a prime candidate for its optimization and constraint handling features. In this work, an improved model predictive control (MPC) with Laguerre and exponential weight functions is proposed t...
| Main Authors: | , |
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| Format: | Article |
| Language: | English English |
| Published: |
Korean Society of Mechanical Engineers
2014
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/43833/ http://irep.iium.edu.my/43833/ http://irep.iium.edu.my/43833/ http://irep.iium.edu.my/43833/1/2014-Fuel_cell_starvation_control_using_model_predictive_technique_with_Laguerre_and_2014.pdf http://irep.iium.edu.my/43833/4/43833_Fuel%20cell%20starvation%20control%20_SCOPUS.pdf |
| Summary: | Fuel cell system is a complicated system that requires an efficient controller. Model predictive control is a prime candidate for its optimization
and constraint handling features. In this work, an improved model predictive control (MPC) with Laguerre and exponential
weight functions is proposed to control fuel cell oxygen starvation problem. To get the best performance of MPC, the control and prediction
horizons are selected as large as possible within the computation limit. An exponential weight function is applied to place more emphasis
on the current time and less emphasis on the future time in the optimization process. This leads to stable numerical solution for
large prediction horizons. Laguerre functions are used to capture most of the control trajectory, while reducing the controller computation
time and memory for large prediction horizons. Robustness and stability of the proposed controller are assessed using Monte-Carlo simulations.
Results verify that the modified MPC is able to mimic the performance of the infinite horizon controller, discrete linear quadratic
regulator (DLQR). The controller computation time is reduced approximately by one order of magnitude compared to traditional MPC
scheme. Results from Monte-Carlo simulations prove that the proposed controller is robust and stable up to system parameters uncertainty
of 40%. |
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