Bounding Computational Complexity under Cost Function Scaling in Predictive Control
@article{McInerney2019BoundingCC, title={Bounding Computational Complexity under Cost Function Scaling in Predictive Control}, author={Ian McInerney and Eric C. Kerrigan and George A. Constantinides}, journal={ArXiv}, year={2019}, volume={abs/1902.02221} }
We present a framework for upper bounding the number of iterations required by first-order optimization algorithms implementing constrained LQR controllers. We derive new bounds for the condition number and extremal eigenvalues of the primal and dual Hessian matrices when the cost function is scaled. These bounds are horizon-independent, allowing for their use with receding, variable and decreasing horizon controllers. We considerably relax prior assumptions on the structure of the weight…
One Citation
Modeling Round-off Error in the Fast Gradient Method for Predictive Control
- Computer Science, Mathematics2019 IEEE 58th Conference on Decision and Control (CDC)
- 2019
A method for determining the smallest precision required to have algorithmic stability of an implementation of the Fast Gradient Method when solving a linear Model Predictive Control problem in fixed-point arithmetic and proposes a metric for measuring the amount of round-off error the FGM iteration can tolerate before becoming unstable.
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