Design of unknown input observer for discrete-time Takagi Sugeno implicit systems with unmeasurable premise variables

In this study, an unknown input observer (UIO) is developed in explicit form to estimate unmeasurable states and inputs (UIs) for nonlinear implicit systems represented by the discrete-time Takagi-Sugeno (DTSIS) case of premise variables. The method employed based on singular value decomposition (SVD) augmenting state vector, which formed partly system UIs. convergence augmented estimation error provided a Lyapunov function ending with solving linear matrix inequalities (LMI). An application model rolling disc considered evaluate effectiveness approach. It appears that estimated variables converge true quickly accurately.