Some remarks on robustness of sample-and-hold stabilization
By: Patrick Schmidt, Pavel Osinenko, Stefan Streif
Potential Business Impact:
Makes robots park better by fixing control problems.
This work studies robustness to system disturbance and measurement noise of some popular general practical stabilization techniques, namely, Dini aiming, optimization-based stabilization and inf-convolution stabilization. Common to all these techniques is the explicit usage of a (general nonsmooth) control Lyapunov function, thus allowing to see them as a kind of generalization to the celebrated Sontag's formula. It turns out that certain details of the above described robustness properties have not yet received the attention in literature they deserved. We provide new remarks, formalized in mathematical propositions, on robustness of selected popular stabilization techniques along with an extensive statistical case study on a robot parking problem.
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