Journal
NEUROCOMPUTING
Volume 299, Issue -, Pages 10-19Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2018.02.088
Keywords
Prescribed performance; Adaptive backstepping control; Disturbance observer; Backlash-like hysteresis; Radial basis function neural networks(RBFNNs)
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Funding
- National Science Foundation of China [U1531101]
- Fundamental Research Funds for the Central Universities, China [26120122012B03714]
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In this paper, an adaptive neural tracking control is studied for a class of strict-feedback nonlinear systems with guaranteed predefined performance subject to unknown backlash-like hysteresis input, uncertain parameters and external unknown disturbance. An adaptive neural control method combined with backstepping technique, and the radial basis function neural networks (RBFNNs) is proposed for the systems under consideration. In recursive backstepping designs, the tracking control performance can be guaranteed by exploiting a new performance function. A disturbance observer is employed to approximate the unknown disturbance. It is shown that by using Lyapunov methods, the designed controller can guarantee the prespecified transient and ensure semi-globally uniformly ultimately bounded (SGUUB) of all signals within the closed-loop systems. Simulation results are presented to illustrate the validity of the approach.
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