Barrier Lyapunov Functions-based adaptive control for a class of nonlinear pure-feedback systems with full state constraints

By Yanjun Liu, Shaocheng Tong. at - Automatisierungstechnik, 2016

862 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/61dd7d53c653e3068afbf5c54b57909eb468bc63

How researchers see this paper

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

In Computational Theory and Mathematics

1. Wei He
2. Shaocheng Tong
3. Yanjun Liu
4. Yongduan Song
5. Kai Zhao
6. Chun Lung Philip Chen
7. Shuzhi Sam Ge
8. Xiaoping Liu

In Control and Systems Engineering

1. Shaocheng Tong
2. Wei He
3. Yanjun Liu
4. Chun Lung Philip Chen
5. Kai Zhao
6. Yongduan Song
7. Shuzhi Sam Ge
8. Xiaoping Liu

Citations in the corpus

• Prescribed Performance Control of Uncertain Euler–Lagrange Systems Subject to Full-State Constraints (2018, neutral): “Remark 5: In [12] and [13], adaptive control is proposed for a class of nonlinear systems with full-state constraints.”
• Barrier Lyapunov functions-based dynamic surface control for pure-feedback systems with full state constraints (2017, background): “In [15], a class of non-linear purefeedback systems with full state constraints were considered.”
• Adaptive Fuzzy Practical Fixed-Time Tracking Control of Nonlinear Systems (2021, background): “Practically, many actual systems can fall into the class given in (1), such as single forcemechanicalarm[38],andsoon.”
• Adaptive Neural Network Control of a Robotic Manipulator With Time-Varying Output Constraints (2017, background): “To improve the compensation performance for the output constraints, BLFs are considered to investigate the stability of the nonlinear system in [50].”
• Neural Network Control-Based Adaptive Learning Design for Nonlinear Systems With Full-State Constraints (2016, neutral): “The adaptive control of full-stateconstrained nonlinear systems was studied in [7] and [8].”
• Removing the Feasibility Conditions Imposed on Tracking Control Designs for State-Constrained Strict-Feedback Systems (2019, methodology): “Remark 2: The novelty of the nonlinear transformation as given in (3) is twofold: 1) it provides an explicit (direct) way to deal with the full-state constraints, preventing the conservative step of converting original state constraints into new bounds on tracking errors [6], [9], [12], [13], [15], ”
• Neural Networks-Based Adaptive Control for Nonlinear State Constrained Systems With Input Delay (2019, background): “Assumption 1 [39]: There exist the constants kc1 and A0, A1, .”