Design of an Adaptive Robust PI Controller for DC/DC Boost Converter Using Reinforcement-Learning Technique and Snake Optimization Algorithm

The DC/DC Boost converter exhibits a non-minimum phase system with a right half-plane zero structure, posing significant challenges for the design of effective control approaches. This article presents the design of a robust Proportional-Integral (PI) controller for this converter with an online ada...

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Vydáno v:IEEE access Ročník 12; s. 141814 - 141829
Hlavní autoři: Ghamari, Seyyed Morteza, Hajihosseini, Mojtaba, Habibi, Daryoush, Aziz, Asma
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Adaptation models
Adaptive algorithms
Aerospace electronics
Boost converter
Control systems design
Controllers
Design optimization
disturbance
DPG
Half planes
Heuristic algorithms
Machine learning
Measurement methods
Metaheuristics
Optimization
Parameter robustness
Parameter sensitivity
PI control
Proportional integral
Real time
reinforcement learning technique
Robust control
robust controller
Tuning
ISSN:2169-3536, 2169-3536
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Shrnutí:The DC/DC Boost converter exhibits a non-minimum phase system with a right half-plane zero structure, posing significant challenges for the design of effective control approaches. This article presents the design of a robust Proportional-Integral (PI) controller for this converter with an online adaptive mechanism based on the Reinforcement-Learning (RL) strategy. Classical PI controllers are simple and easy to build, but they need to be more robust against a wide range of disturbances and more adaptable to operational parameters. To address these issues, the RL adaptive strategy is used to optimize the performance of the PI controller. Some of the main advantages of the RL are lower sensitivity to error, more reliable results through the collection of data from the environment, an ideal model behavior within a specific context, and better frequency matching in real-time applications. Random exploration, nevertheless, can result in disastrous outcomes and surprising performance in real-world settings. Therefore, we opt for the Deterministic Policy Gradient (DPG) technique, which employs a deterministic action function as opposed to a stochastic one. DPG combines the benefits of actor-critics, deep Q-networks, and the deterministic policy gradient method. In addition, this method adopts the Snake Optimization (SO) algorithm to optimize the initial condition of gains, yielding more reliable results with faster dynamics. The SO method is known for its disciplined and nature-inspired approach, which results in faster decision-making and greater accuracy compared to other optimization algorithms. A structure using a hardware setup with CONTROLLINO MAXI Automation is built, which offers a more cost-effective and precise measurement method. Finally, the results achieved by simulations and experiments demonstrate the robustness of this approach.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3440580