Abstract
A new robust tracking control approach is proposed for strict-feedback nonlinear systems with state and input constraints. The constraints are tackled by extending the control input as an extended state and introducing an integral barrier Lyapunov function (IBLF) to each step in a backstepping procedure. This extends current research on barrier Lyapunov functions(BLFs)-based control for nonlinear systems with state constraints to IBLF-based control for strict-feedback nonlinear systems with state and input constraints. Since the IBLF allows the original constraints to be mixed with the error terms, the use of IBLF decreases conservatism in barrier Lyapunov functions-based control. In the backstepping procedure, neural networks (NNs) with projection modifications are applied to estimate system uncertainties, due to their ability in guaranteeing estimators in a given bounded area. To facilitate the use of the once-differentiable NNs estimators in the backstepping procedure, the virtual controllers are passed through command filters. Finally, simulation results are presented to illustrate the feasibility and effectiveness of the proposed control.
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Recommended by Associate Editor Sing Kiong Nguang under the direction of Editor Jessie (Ju H.) Park. This journal was supported by the National Natural Science Foundation of China (No. 61503158, No.51379044, and No. 51405303), Natural Science Foundation of Jiangsu Province (No. BK20130536), the Scientific Research Foundation for Advanced Talents by Jiangsu University, and Zhenjiang city 2017 annual science and technology innovation fund (No. NY2017013).
Jun Zhang received the B.Eng. and Ph.D. degrees in control theory and control engineering from Harbin Institute of Technology, China, in 1997 and 2004, respectively. He is currently with the School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China. His research interests include model predictive control, hypersonic vehicle control, underactuated surface vessels control.
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Zhang, J. Integral Barrier Lyapunov Functions-based Neural Control for Strict-feedback Nonlinear Systems with Multi-constraint. Int. J. Control Autom. Syst. 16, 2002–2010 (2018). https://doi.org/10.1007/s12555-017-0564-6
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DOI: https://doi.org/10.1007/s12555-017-0564-6