Robust pandemic control through linearizing variable transformation

The COVID-19 pandemic is characterized by sys-tem instability, nonlinear dynamics, significant delays and large uncertainty. This combination makes it challenging to design robust feedback controlled mitigation strategies. We proposed a linearizing variable transformation that translates the challenging nonlinear problem to linear control of an integral system with time delay. In the transformed problem, robust linear controller design and analysis tools can used, and this system can be made robust to significant uncertainty. So what is the catch? This paper compares the achievable performance to a gain-scheduling approach. We did not find significant limitations to performance as a result of the transformation. Locally, the same performance can be achieved. In transients between equilibrium points, the variable transformation approach maintains robust stability and performance guarantees.