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A Learning-Based Closed-Loop Fluid Flow Regulation
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DOI：10.3390/electronics15050953.png)
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Abstract
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This paper presents a learning-based robust control strategy for fluid flow dynamic systems, designed to compensate for modeling uncertainties and unknown disturbances inherent to closed-loop active flow control applications. The method is grounded in nonlinear control theory and uses gradient descent learning rules to continuously update control parameters and disturbance estimate. The resulting closed-loop system achieves robust regulation while maintaining simplicity and interpretability in both implementation and analysis. Numerical simulations are conducted to validate the approach.
Keywords:
learning
flow control
robustness
uncertainty
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