Online reinforcement learning of controller parameters adaptation law

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.