Approximation-based adaptive control of uncertain non-linear pure-feedback systems with full state constraints

By Bong Su Kim, Sung Jin Yoo. 2014

111 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/a2182644633ef45fa921b44ec9d0b695888c185c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Kai Zhao
2. Yongduan Song
3. Xu Jin

Citations in the corpus

• Neural Network Control-Based Adaptive Learning Design for Nonlinear Systems With Full-State Constraints (2016, unknown): “An adaptive approximation-based constraint approach for nonlinear pure-feedback systems with full-state constraints was studied in [54].”
• Removing the Feasibility Conditions Imposed on Tracking Control Designs for State-Constrained Strict-Feedback Systems (2019, methodology): “[21], barrier Lyapunov function (BLF) and integral BLF (IBLF) based approaches have been widely used to deal with state constraints for various nonlinear systems [5], [6], [9]–[16], [30]–[33], [37], [38], [40], [41].”