Optimal input design for online state and parameter estimation using interval sliding mode observers

A huge number of real-life models for dynamic systems in control engineering are characterized by nonlinear behavior. These systems often include both state variables that are not directly measurable and unknown or uncertain parameters. This uncertainty results from a lack of knowledge about specific system parameters, inaccurate measured data, and manufacturing tolerances. Considering these facts, the application of common sliding mode techniques may not be reliable if they are used for a simultaneous estimation of time-varying system states as well as for an online parameter identification. This is often caused by an observer design based on simplified system models that have to satisfy restrictive matching conditions even for exactly known parameters. Therefore, a novel interval sliding mode observer providing point-valued estimates is described in this paper. For an efficient operation, an optimal input design is exploited.