Real-time simultaneously updating a linearized state-space model and pole placement gain

Data-driven pole placement proposed by Yamamoto et al. (2015) is extended to treat online update of a linearized model and the pole placement gain for a nonlinear system. The data-driven pole placement method can simultaneously derive the pole placement state feedback gain and the state-space model directly from a pair of state and input measurements of a controlled discrete-time system. In this study, we investigate the capability of the real-time data-driven pole placement method by numerical simulations.