Nonlinear recurrent neural network predictive control for energy distribution of a fuel cell powered robot.
This paper presents a neural network predictive control strategy to optimize power distribution for a fuel cell/ultracapacitor hybrid power system of a robot. We model the nonlinear power system by employing time variant auto-regressive moving average with exogenous (ARMAX), and using recurrent neural network to represent the complicated coefficients of the ARMAX model. Because the dynamic of the system is viewed as operating- state- dependent time varying local linear behavior in this frame, a linear constrained model predictive control algorithm is developed to optimize the power splitting between the fuel cell and ultracapacitor. The proposed algorithm significantly simplifies implementation of the controller and can handle multiple constraints, such as limiting substantial fluctuation of fuel cell current. Experiment and simulation results demonstrate that the control strategy can optimally split power between the fuel cell and ultracapacitor, limit the change rate of the fuel cell current, and so as to extend the lifetime of the fuel cell.
School of Automation, Wuhan University of Technology, Wuhan 430070, China.
Qihong
Q
Chen
School of Automation, Wuhan University of Technology, Wuhan 430070, China ; College of Science, Huazhong Agricultural University, Wuhan 430070, China.
Rong
R
Long
School of Automation, Wuhan University of Technology, Wuhan 430070, China.
Shuhai
S
Quan
School of Automation, Wuhan University of Technology, Wuhan 430070, China.
Liyan
0000-0001-6308-0069
L
Zhang
D000465:Algorithms; D001674:Bioelectric Energy Sources; D003198:Computer Simulation; D016014:Linear Models; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D012371:Robotics / Q000379:methods