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- 저자 : Moumen Idres (검색결과 2 건)
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Constrained model predictive control of proton exchange membrane fuel cell
Muhammad Abdullah,Moumen Idres 대한기계학회 2014 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.28 No.9
A constrained model predictive control (MPC) is designed to regulate the air flow rate of proton exchange membrane fuel cell(PEMFC). Oxygen excess ratio, compressor flow rate and supply manifold pressure are constrained to avoid oxygen starvation, surge andchoke phenomena. This is achieved by manipulating compressor voltage and stack current. The choice of the manipulated input to satisfya constraint is investigated. Surge and choke avoidance is successful, when compressor voltage is manipulated. When stack current isutilized to satisfy surge and choke constraints, a large unrealistic current is needed. Oxygen starvation is successfully avoided utilizingstack current, while compressor voltage manipulation fails to prevent oxygen starvation. Thus, a current governor is implemented to handleoxygen starvation, while the compressor voltage is constrained to avoid surge and choke. Quadratic programming optimization,Laguerre and exponential weight function are employed to reduce the computational burden of the controller. The simulation resultsprove that the proposed controller managed to satisfy all constraints without any conflict.
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Muhammad Abdullah,Moumen Idres 대한기계학회 2014 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.28 No.5
Fuel cell system is a complicated system that requires an efficient controller. Model predictive control is a prime candidate for its optimizationand constraint handling features. In this work, an improved model predictive control (MPC) with Laguerre and exponentialweight functions is proposed to control fuel cell oxygen starvation problem. To get the best performance of MPC, the control and predictionhorizons are selected as large as possible within the computation limit. An exponential weight function is applied to place more emphasison the current time and less emphasis on the future time in the optimization process. This leads to stable numerical solution forlarge prediction horizons. Laguerre functions are used to capture most of the control trajectory, while reducing the controller computationtime 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 quadraticregulator (DLQR). The controller computation time is reduced approximately by one order of magnitude compared to traditional MPCscheme. Results from Monte-Carlo simulations prove that the proposed controller is robust and stable up to system parameters uncertaintyof 40%.
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