scholarly journals Control Method for PEMFC Using Improved Deep Deterministic Policy Gradient Algorithm

2021 ◽  
Vol 9 ◽  
Author(s):  
Jiawen Li ◽  
Yaping Li ◽  
Tao Yu
Keyword(s):  
Optimal Control ◽  
Output Voltage ◽  
Control Method ◽  
Voltage Control ◽  
Control Policy ◽  
Gradient Algorithm ◽  
Data Driven ◽  
Q Learning ◽  
Policy Gradient

A data-driven PEMFC output voltage control method is proposed. Moreover, an Improved deep deterministic policy gradient algorithm is proposed for this method. The algorithm introduces three techniques: Clipped multiple Q-learning, policy delay update, and policy smoothing to improve the robustness of the control policy. In this algorithm, the hydrogen controller is treated as an agent, which is pre-trained to fully interact with the environment and obtain the optimal control policy. The effectiveness of the proposed algorithm is demonstrated experimentally.

scholarly journals Distributed Imitation-Orientated Deep Reinforcement Learning Method for Optimal PEMFC Output Voltage Control

2021 ◽  
Vol 9 ◽  
Author(s):  
Jiawen Li ◽  
Yaping Li ◽  
Tao Yu
Keyword(s):  
Reinforcement Learning ◽  
Control Strategy ◽  
Output Voltage ◽  
Proton Exchange Membrane ◽  
Control Method ◽  
Voltage Control ◽  
Proton Exchange ◽  
Gradient Algorithm ◽  
Voltage Control Strategy ◽  
The Stability

In order to improve the stability of proton exchange membrane fuel cell (PEMFC) output voltage, a data-driven output voltage control strategy based on regulation of the duty cycle of the DC-DC converter is proposed in this paper. In detail, an imitation-oriented twin delay deep deterministic (IO-TD3) policy gradient algorithm which offers a more robust voltage control strategy is demonstrated. This proposed output voltage control method is a distributed deep reinforcement learning training framework, the design of which is guided by the pedagogic concept of imitation learning. The effectiveness of the proposed control strategy is experimentally demonstrated.

Output voltage control method of PWM-controlled cycloconverters with space vectors

1991 ◽  
Vol 111 (5) ◽  
pp. 117-126 ◽  
Author(s):  
Akio Ishiguro ◽  
Takeshi Furuhashi ◽  
Shigeru Okuma ◽  
Yoshiki Uchikawa ◽  
Muneaki Ishida
Keyword(s):  
Output Voltage ◽  
Control Method ◽  
Voltage Control ◽  
Space Vectors

scholarly journals Improved Q-Learning Method for Linear Discrete-Time Systems

Processes ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 368
Author(s):  
Jian Chen ◽  
Jinhua Wang ◽  
Jie Huang
Keyword(s):  
Optimal Control ◽  
Linear Systems ◽  
Discrete Time ◽  
Control Method ◽  
Learning Method ◽  
Control Laws ◽  
Q Learning ◽  
Model Free ◽  
Quadratic Optimal Control ◽  
Time Linear

In this paper, the Q-learning method for quadratic optimal control problem of discrete-time linear systems is reconsidered. The theoretical results prove that the quadratic optimal controller cannot be solved directly due to the linear correlation of the data sets. The following corollaries have been made: (1) The correlation of data is the key factor in the success for the calculation of quadratic optimal control laws by Q-learning method; (2) The control laws for linear systems cannot be derived directly by the existing Q-learning method; (3) For nonlinear systems, there are some doubts about the data independence of current method. Therefore, it is necessary to discuss the probability of the controllers established by the existing Q-learning method. To solve this problem, based on the ridge regression, an improved model-free Q-learning quadratic optimal control method for discrete-time linear systems is proposed in this paper. Therefore, the computation process can be implemented correctly, and the effective controller can be solved. The simulation results show that the proposed method can not only overcome the problem caused by the data correlation, but also derive proper control laws for discrete-time linear systems.

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