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Adaptive-Robust Control for Systems with State-Dependent Upper Bound in Uncertainty

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Adaptive-Robust Control with Limited Knowledge on Systems Dynamics

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 257))

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Abstract

In the previous chapters, the time-delayed estimation (TDE) method based adaptive-Robust control (ARC) designs could only reduce the dependency on the system dynamics to a certain extent, but not completely. It requires the knowledge of the mass-matrix of an Euler-Lagrange (EL) system with an inherent assumption that the available sampling frequency is considerably high compared to the rate of change of uncertainty, implying a TDE-based strategy may not yield satisfactory result under fast changing uncertainty or under limited computation capability. Further, the state-of-the-art ARC designs, which do not utilize TDE, either require structural knowledge of the system or can only tackle a priori bounded uncertainties. However, for an EL system, imposition of a priori boundedness on the uncertainty is very restrictive owing to its inherent state-dependent structure of uncertainty. Therefore, considering various limitations of the existing ARC designs, this chapter provides a new ARC framework that (i) does not require any knowledge of the system structure or the system parameters and (ii) does not impose any restriction on the rate of change uncertainty or any a priori boundedness condition on the uncertainties.

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Authors and Affiliations

  1. Robotics Research Center, International Institute of Information Technology, Hyderabad, Telangana, India

    Spandan Roy

  2. Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India

    Indra Narayan Kar

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  1. Spandan Roy
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  2. Indra Narayan Kar
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Correspondence to Spandan Roy .

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Roy, S., Kar, I.N. (2020). Adaptive-Robust Control for Systems with State-Dependent Upper Bound in Uncertainty. In: Adaptive-Robust Control with Limited Knowledge on Systems Dynamics. Studies in Systems, Decision and Control, vol 257. Springer, Singapore. https://doi.org/10.1007/978-981-15-0640-6_6

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