Limited-information learning observer for simultaneous estimation of states and parameters

The problem of simultaneous estimation of states and uncertainties by using limited information is investigated in this article. A sampled-data learning observer (SLO) is presented for linear time-invariant continuous systems, which can achieve successful estimation while only needs intermittent sampled-data, saves computing resources, and does not require persistent excitation signals. The observer's demand for continuous measurement is reduced, that is, limited-information is sufficient. Notably, the uncertainty estimation is performed by a learning equation with only simple addition operations, which is particularly suitable for actual digital system scenarios. Simulation results illustrate the effectiveness of the proposed SLO.