Adaptive temperature control of a reverse flow process by using reinforcement learning approach
<p>This work focuses on the design of an optimal adaptive control system for temperature regulation in a catalytic flow reversal reactor (CFRR), utilizing a reinforcement learning (RL) approach. First, a policy iteration algorithm is introduced to learn the optimal solution of the associated l...
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2024
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| Summary: | <p>This work focuses on the design of an optimal adaptive control system for temperature regulation in a catalytic flow reversal reactor (CFRR), utilizing a reinforcement learning (RL) approach. First, a policy iteration algorithm is introduced to learn the optimal solution of the associated linear-quadratic control problem online. It should be mentioned that this approach is not reliant on the internal dynamics of the CFRR system, which is a complex process and is most effectively modeled using Partial Differential Equations (PDEs). The convergence of the iteration algorithm is established, assuming the initial policy is stabilizing. Additionally, a second algorithm is presented to enhance the implementability of the reinforcement learning algorithm from a practical perspective. Numerical simulations are carried out to illustrate the efficacy of the proposed algorithm.</p><h2>Other Information</h2> <p> Published in: Journal of Process Control<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.jprocont.2024.103259" target="_blank">https://dx.doi.org/10.1016/j.jprocont.2024.103259</a></p> |
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