Sliding mode techniques for robust control, state estimation and parameter identification of uncertain dynamic systems

The aim of this work is to present robust ways for control, state estimation and parameter identification guaranteeing stability of a considered system for all times. Robustness of sliding mode approaches can be improved, if bounded and stochastic uncertainty are taken into account. As a consequence, interval analysis and stochastic differential equations are combined in a novel way with sliding mode techniques for robust control and state estimation in real-time implementations. Therefore, a suitable candidate of a Lyapunov function is taken into account which is used, on the one hand, to guarantee stability by applying the Itô differential operator and, on the other hand, to calculate the variable-structure gain in sliding mode control and estimation approaches. It is shown that this can be done adaptively in each time step to reduce chattering and to decrease unnecessary large actuator effort. The practical applicability of these techniques is demonstrated for two scenarios: a drive-train test rig and the thermal behavior of a high-temperature fuel cell system. For both applications, firstly, the control-oriented modeling and the application of interval-based sliding mode techniques are described. Secondly, simulation results highlight the improved performance in comparison with state-of-the-art approaches. Finally, experimental results are presented to show real-time capability of the developed techniques.