|Integral Barrier Lyapunov Functions-based Neural Control for Strictfeedback Nonlinear Systems with Multi-constraint
International Journal of Control, Automation, and Systems, vol. 16, no. 4, pp.2002-2010, 2018
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 functionsbased
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."
"Barrier Lyapunov function, dynamic surface control, input saturation, neural networks, state constraints, strict-feedback nonlinear system."
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