Adaptive neural network tracking control for underactuated systems with matched and mismatched disturbances
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This paper studies neural network-based tracking control of underactuated systems with unknown parameters and with matched and mismatched disturbances. Novel adaptive control schemes are proposed with the utilization of multi-layer neural networks, adaptive control and variable structure strategies to cope with the uncertainties containing approximation errors, unknown base parameters and time-varying matched and mismatched external disturbances. Novel auxiliary control variables are designed to establish the controllability of the non-collocated subset of the underactuated systems. The approximation errors and the matched and mismatched external disturbances are efficiently counteracted by appropriate design of robust compensators. Stability and convergence of the time-varying reference trajectory are shown in the sense of Lyapunov. The parameter updating laws for the designed control schemes are derived using the projection approach to reduce the tracking error as small as desired. Unknown dynamics of the non-collocated subset is approximated through neural networks within a local region. Finally, simulation studies on an underactuated manipulator and an underactuated vibro-driven system are conducted to verify the effectiveness of the proposed control schemes.
Liu, P., Yu, H. and Cang, S. (2019) 'Adaptive neural network tracking control for underactuated systems with matched and mismatched disturbances', Nonlinear Dynamics, 98(2), pp.1-18. DOI: 10.1007/s11071-019-05170-8.
Article published in Nonlinear Dynamics on 08 October 2019, available open access at: https://doi.org/10.1007/s11071-019-05170-8.
Cardiff Metropolitan University (Grant ID: Cardiff Metropolian (Internal))
This work was partially supported by the National Natural Science Foundation of China (61803396), European Commission Marie Skłodowska-Curie SMOOTH (smart robots for fire-fighting) project (H2020-MSCA-RISE-2016-734875) and Royal Society International Exchanges Scheme (Adaptive Learning Control of a Cardiovascular Robot using Expert Surgeon Techniques) project (IE151224). FP7 People: Marie-Curie Actions (Grant Number PIRSES-GA-2012-318902).
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