Abstract
In this chapter, we demonstrate the use of genetic programming for machine learning control (MLC) on linear systems where optimal control laws are known. In particular, we benchmark MLC against the linear quadratic regulator (LQR) for full-state feedback and the Kalman filter for full-state estimation, providing code for each example. MLC is able to identify the optimal linear control solutions and outperforms linear control even for small nonlinearity.
All stable processes we shall predict. All unstable processes we shall control.
John von Neumann
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© 2017 Springer International Publishing Switzerland
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Duriez, T., Brunton, S.L., Noack, B.R. (2017). Benchmarking MLC Against Linear Control. In: Machine Learning Control – Taming Nonlinear Dynamics and Turbulence. Fluid Mechanics and Its Applications, vol 116. Springer, Cham. https://doi.org/10.1007/978-3-319-40624-4_4
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DOI: https://doi.org/10.1007/978-3-319-40624-4_4
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Publisher Name: Springer, Cham
Print ISBN: 978-3-319-40623-7
Online ISBN: 978-3-319-40624-4
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