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Sliding Mode Techniques for Robust Trajectory Tracking as well as State and Parameter Estimation

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Abstract

Practical applications are often affected by uncertainties—more precisely bounded and stochastic disturbances. These have to be considered in robust control procedures to prevent a system from being unstable. Common sliding mode control strategies are often not able to cope with the mentioned impacts simultaneously, because they assume that the considered system is only affected by matched uncertainty. Another problem is the offline computation of the switching amplitude. Under these assumptions, important nonlinear system properties cannot be taken into account within the mathematical model of the system. Therefore, this paper presents sliding mode techniques, that on the one hand are able to consider bounded as well as stochastic uncertainties simultaneously, and on the other hand are not limited to the matched case. Firstly, a sliding mode control procedure taking into account both classes of uncertainty is shown. Additionally, a sliding mode observer for the simultaneous estimation of non-measurable system states and uncertain but bounded parameters is described despite stochastic disturbances. This is possible by using intervals for states and parameters in the resulting stochastic differential equations. Therefore, the Itô differential operator is involved and the system’s stability can be verified despite uncertainties and disturbances for both control and observer procedures. This operator is used for the online computation of the variable structure part gain (matrix of switching amplitudes) which is advantageous in contrast to common sliding mode procedures.

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Authors and Affiliations

  1. Chair of Mechatronics, University of Rostock, D-18059, Rostock, Germany

    Luise Senkel, Andreas Rauh & Harald Aschemann

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  1. Luise Senkel
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  2. Andreas Rauh
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  3. Harald Aschemann
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Correspondence to Luise Senkel.

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Senkel, L., Rauh, A. & Aschemann, H. Sliding Mode Techniques for Robust Trajectory Tracking as well as State and Parameter Estimation. Math.Comput.Sci. 8, 543–561 (2014). https://doi.org/10.1007/s11786-014-0208-7

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  • DOI: https://doi.org/10.1007/s11786-014-0208-7

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