Online reinforcement learning of controller parameters adaptation law

Khalid Alhazmi, Mani Sarathy

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Real-time control of highly nonlinear systems is a challenging task in many industrial processes. Here, we propose a learning-based adaptation law for adapting the controller parameters of nonlinear systems. The method applies model- free reinforcement learning to learn an effective parameter adaptation law while maintaining a safe system operation by including a safety layer. The efficacy of the proposed algorithm is demonstrated by controlling thermoacoustic combustion instability, which is a critical issue in developing high-efficiency, low- emission gas turbine technologies. We show that the learning- based mechanism is able to attenuate combustion instabilities in a time-variant system with the presence of process noise. The proposed algorithm outperforms the adaptation performance of other model-free and model-based methods, such as extremum seeking controllers and self-tuning regulators, respectively.
Original languageEnglish (US)
Title of host publication2023 American Control Conference (ACC)
StatePublished - Jul 3 2023


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