An Unknown Input Nonlinear Observer Based Fractional Order PID Control of Fuel Cell Air Supply System

Zhao, Dongdong; Zhao, Dongdong; Ma, Rui; Zhao, Guochang; Huangfu, Yigeng

doi:10.1109/tia.2020.2999037

Cited by 57 publications

(17 citation statements)

References 35 publications

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“…Ou et al relied on PI controller to adjust hydrogen flow, which produced satisfactory control performance [6]. Zhao et al proposed the coefficientoptimized frictional order PID controller (FOPID) for regulating PEMFC air flow, which exhibited superior adaptability Observer feedforward [5] Low cost; Simple structure and easy modification Difficult to cope with nonlinearity; long regulating time and narrow range of control policies PI controller [6] Coefficient-optimized FOPID [7] PSO-PID controller [8] Adaptive control T2-FLS controller [9] Ability to cope with Nonlinear system; anti-interference; excellent robustness Lack of systematic design; low control accuracy; poor dynamic quality MRAC [10] Model predictive control Off-line robust MPC [11] Excellent dynamic performance; excellent stability and strong anti-interference ability Complex controller, Large calculated amount, Over-reliance on accurate mathematical model DMPC [12] Neural network control Neural network feedforward [13] Avoid modelling; learning ability; more accurate model and controller with more data…”

Section: Introductionmentioning

confidence: 99%

Distributed deep reinforcement learning for optimal voltage control of PEMFC

2021

IET Renewable Power Gen

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Proton exchange membrane fuel cells (PEMFCs) are promising components in the renewable energy field due to their high energy efficiency and low pollution output. However, these cells are also characterized by considerable nonlinearity, which in turn adversely affects the PEMFC output voltage. Conventional control algorithms cannot guarantee sufficient output voltage control, as they lack the robust adaptive ability required for adapting to these fluctuations in PEMFCs. In this paper, an optimal output voltage controller based on distributed deep reinforcement learning, which controls the output voltage by regulating the fuel input of the PEMFC, is proposed. In addition, an ensemble intelligence exploration multi-delay deep deterministic policy gradient (EIM-DDPG) algorithm is proposed for this controller. An ensemble intelligence exploration policy plus a classification experience replay mechanism are included within the EIM-DDPG algorithm to improve the exploration ability of the algorithm and thus increase the robustness and adaptive capability of the controller. As a result, the model-free optimal output voltage controller offers high robustness and adaptability. The simulation results in this paper demonstrate that the proposed optimal controller can realize the effective control of PEMFC output voltage.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Introductionmentioning

confidence: 99%

Distributed deep reinforcement learning for optimal voltage control of PEMFC

2021

IET Renewable Power Gen

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“…The Deep Deterministic Policy Gradient (DDPG) algorithm in deep reinforcement learning (Lillicrap et al, 2015) is a model-free method (Yang et al, 2018;Yang et al, 2019a;Yang et al, 2019b;. Due to its strong adaptive ability, the DDPG algorithm can adapt to the uncertainty inherent in nonlinear control systems, and it is applied in various control fields (Zhang et al, 2019;Zhao et al, 2020;Zhang et al, 2021). However, due to its low robustness, DDPG is rarely used in the PEMFC control field.…”

Section: Introductionmentioning

confidence: 99%

Temperature Control of Proton Exchange Membrane Fuel Cell Based on Machine Learning

2021

Front. Energy Res.

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In order to improve the proton exchange membrane fuel cell (PEMFC) working efficiency, we propose a deep-reinforcement-learning based PID controller for realizing optimal PEMFC stack temperature. For this purpose, we propose the Improved Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, a tuner of the PID controller, which can adjust the coefficients of the controller in real time. This algorithm accelerates the learning speed of an agent by continuously changing the soft update parameters during the training process, thereby improving the training efficiency of the agent, and further reducing training costs and obtaining a robust strategy. The effectiveness of the control algorithm is verified through a simulation in which it is compared against a group of existing algorithms.

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“…PID and its associated algorithms have been widely used in fuel cell control and other practical engineering applications due to their simple control policy and good robustness. A number of PID control methods have been proposed, including neural PID control [30], a fuzzy PID control [31], an algorithm combining PID and fuzzy control [32], a feedback linearization control policy (for transforming a nonlinear control model into a linear model) [33], and a fraction-order PID (FOPID) control based on nonlinear observer with unknown input [34].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Controller of PEMFC Output Voltage Based on Ambient Intelligence Large-Scale Deep Reinforcement Learning

Yang

2021

IEEE Access

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In this paper, an adaptive Proportion integration (PI) controller for varying the output voltage of a proton exchange membrane fuel cell (PEMFC) is proposed. The PI controller operates on the basis of ambient intelligence large-scale deep reinforcement learning. It functions as a coefficient tuner based on an ambient intelligence exploration multi-delay deep deterministic policy gradient (AIEM-DDPG) algorithm. This algorithm is an improvement on the original deep deterministic police gradient (DDPG) algorithm, which incorporates ambient intelligence exploration. The DDPG algorithm serves as the core, and the AIEM-DDPG algorithm runs on a variety of deep reinforcement learning algorithms, including soft actorcritic (SAC), deep deterministic policy gradient (DDPG), proximal policy optimization (PPO) and double deep Q-network (DDQN) algorithms, to attain distributed exploration in the environment. In addition, a classified priority experience replay mechanism is introduced to improve the exploration efficiency. Clipping multi-Q learning, policy delayed updating, target policy smooth regularization and other methods are utilized to solve the problem of Q-value overestimation. A model-free algorithm with good global searching ability and optimization speed is demonstrated. Simulation results show that the AIEM-DDPG adaptive PI controller attains better robustness and adaptability, as well as a good control effect.INDEX TERMS distributed deep reinforcement learning; ambient intelligence exploration multi-delay deep deterministic policy gradient; proton exchange membrane fuel cell; air mass flow control; intelligent controller

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An Unknown Input Nonlinear Observer Based Fractional Order PID Control of Fuel Cell Air Supply System

Cited by 57 publications

References 35 publications

Distributed deep reinforcement learning for optimal voltage control of PEMFC

Distributed deep reinforcement learning for optimal voltage control of PEMFC

Temperature Control of Proton Exchange Membrane Fuel Cell Based on Machine Learning

Adaptive Controller of PEMFC Output Voltage Based on Ambient Intelligence Large-Scale Deep Reinforcement Learning

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