Integral Barrier Lyapunov Functions-based Neural Control for Strict-feedback Nonlinear Systems with Multi-constraint
- 35 Downloads
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.
KeywordsBarrier Lyapunov function dynamic surface control input saturation neural networks state constraints strict-feedback nonlinear system
Unable to display preview. Download preview PDF.
- W. Meng, Q. Yang, J. Si, and Y. Sun, “Adaptive neural control of a class of output-constrained nonaffine systems,” IEEE Transactions on Neural Netwokrs and Learning Systems, vol. 46, no. 1, pp. 85–95, Jan 2016.Google Scholar
- K. P. Tee and S. S. Ge, “Control of state-constrained nonlinear systems using integral barrier Lyapunov functions,” 51st IEEE Conference on Decision and Control, pp. 3239–3244, 2012.Google Scholar
- Z. L. Tang, S. S. Ge, and K. P. Tee, “Adaptive NN control for uncertain fure-feedback nonlinear systems with state constaints subject to unknown disturbances,” IEEE 54th Anneul Conference on Decision and Control, pp. 7572–7577, 2015.Google Scholar