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Adaptive Control for Nonlinear Pure-Feedback Systems With High-Order Sliding Mode Observer

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3 Author(s)
Jing Na ; Fac. of Mech. & Electr. Eng., Kunming Univ. of Sci. & Technol., Kunming, China ; Xuemei Ren ; Dongdong Zheng

Most of the available control schemes for pure-feedback systems are derived based on the backstepping technique. On the contrary, this paper presents a novel adaptive control design for nonlinear pure-feedback systems without using backstepping. By introducing a set of alternative state variables and the corresponding transform, state-feedback control of the pure-feedback system can be viewed as output-feedback control of a canonical system. Consequently, backstepping is not necessary and the previously encountered explosion of complexity and circular issue are also circumvented. To estimate unknown states of the newly derived canonical system, a high-order sliding mode observer is adopted, for which finite-time observer error convergence is guaranteed. Two adaptive neural controllers are then proposed to achieve tracking control. In the first scheme, a robust term is introduced to account for the neural approximation error. In the second scheme, a novel neural network with only a scalar weight updated online is constructed to further reduce the computational costs. The closed-loop stability and the convergence of the tracking error to a small compact set around zero are all proved. Comparative simulation and practical experiments on a servo motor system are included to verify the reliability and effectiveness.

Published in:

Neural Networks and Learning Systems, IEEE Transactions on  (Volume:24 ,  Issue: 3 )