This work proposes a robust data-driven predictive control approach for unknown nonlinear
systems in the presence of bounded process and measurement noise. Data-driven
reachable sets are employed for the controller design instead of using an explicit nonlinear
system model. Although the process and measurement noise are bounded, the statistical
properties of the noise are not required to be known. By using the past noisy input-output
data in the learning phase, we propose a novel method to over-approximate exact …