A dynamic thermal transfer model of a proton exchange membrane fuel cell (PEMFC) stack is developed based on energy conservation in order to reach better temperature control of PEMFC stack. Considering its uncertain p...A dynamic thermal transfer model of a proton exchange membrane fuel cell (PEMFC) stack is developed based on energy conservation in order to reach better temperature control of PEMFC stack. Considering its uncertain parameters and disturbance, we propose a robust adaptive controller based on backstepping algorithm of Lyaponov function. Numerical simulations indicate the validity of the proposed controller.展开更多
A neural network model and fuzzy neural network controller was designed to control the inner impedance of a proton exchange membrane fuel cell (PEMFC) stack. A radial basis function (RBF) neural network model was trai...A neural network model and fuzzy neural network controller was designed to control the inner impedance of a proton exchange membrane fuel cell (PEMFC) stack. A radial basis function (RBF) neural network model was trained by the input-output data of impedance. A fuzzy neural network controller was designed to control the impedance response. The RBF neural network model was used to test the fuzzy neural network controller. The results show that the RBF model output can imitate actual output well, the maximal error is not beyond 20 m-, the training time is about 1 s by using 20 neurons, and the mean squared errors is 141.9 m-2. The impedance of the PEMFC stack is controlled within the optimum range when the load changes, and the adjustive time is about 3 min.展开更多
文摘A dynamic thermal transfer model of a proton exchange membrane fuel cell (PEMFC) stack is developed based on energy conservation in order to reach better temperature control of PEMFC stack. Considering its uncertain parameters and disturbance, we propose a robust adaptive controller based on backstepping algorithm of Lyaponov function. Numerical simulations indicate the validity of the proposed controller.
基金Project(2003AA517020) supported by the National High Technology Research and Development Program of China
文摘A neural network model and fuzzy neural network controller was designed to control the inner impedance of a proton exchange membrane fuel cell (PEMFC) stack. A radial basis function (RBF) neural network model was trained by the input-output data of impedance. A fuzzy neural network controller was designed to control the impedance response. The RBF neural network model was used to test the fuzzy neural network controller. The results show that the RBF model output can imitate actual output well, the maximal error is not beyond 20 m-, the training time is about 1 s by using 20 neurons, and the mean squared errors is 141.9 m-2. The impedance of the PEMFC stack is controlled within the optimum range when the load changes, and the adjustive time is about 3 min.
