Abstract
Uncertainties—more precisely bounded and stochastic disturbances—play a major role in control and estimation tasks in general. Examples for bounded uncertainty are lack of knowledge about specific parameters and manufacturing tolerances. Moreover, stochastic disturbances have a large influence on dynamic systems, especially on sensor measurements. These issues make it difficult to control a system such that robustness and stability are guaranteed if system parameters are not exactly known and system states cannot be measured with high accuracy due to process and measurement noise. Sliding mode techniques are known for their robustness, so that an extension of classical approaches is presented that accounts for uncertainties and estimates non-measurable states as well as unknown parameters.
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Senkel, L., Rauh, A., Aschemann, H. (2016). Experimental Validation of State and Parameter Estimation Using Sliding-Mode-Techniques with Bounded and Stochastic Disturbances. In: Russo, G., Capasso, V., Nicosia, G., Romano, V. (eds) Progress in Industrial Mathematics at ECMI 2014. ECMI 2014. Mathematics in Industry(), vol 22. Springer, Cham. https://doi.org/10.1007/978-3-319-23413-7_91
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DOI: https://doi.org/10.1007/978-3-319-23413-7_91
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