# Linear genetic programming control for strongly nonlinear dynamics with frequency crosstalk

@inproceedings{Li2017LinearGP, title={Linear genetic programming control for strongly nonlinear dynamics with frequency crosstalk}, author={Ruiying Li and Bernd R. Noack and Laurent Cordier and Jacques Bor{\'e}e and Eurika Kaiser and Fabien Harambat}, year={2017} }

We advance Machine Learning Control (MLC), a recently proposed model-free control framework which explores and exploits strongly nonlinear dynamics in an unsupervised manner. The assumed plant has multiple actuators and sensors and its performance is measured by a cost functional. The control problem is to find a control logic which optimizes the given cost function. The corresponding regression problem for the control law is solved by employing linear genetic programming as an easy and simple… CONTINUE READING

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#### References

##### Publications referenced by this paper.

SHOWING 1-10 OF 37 REFERENCES

## Machine Learning Control – Taming Nonlinear Dynamics and Turbulence

VIEW 5 EXCERPTS

HIGHLY INFLUENTIAL

## Cluster-based control of nonlinear dynamics

VIEW 1 EXCERPT

## Closed-Loop Turbulence Control: Progress and Challenges

VIEW 1 EXCERPT