Adaptive Neural Control for Output Feedback Nonlinear Systems Using a Barrier Lyapunov Function

By Beibei Ren, Shuzhi Sam Ge, Keng Peng Tee, Tong-heng Lee. IEEE Transactions on Neural Networks, 2010

1,001 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/73d2ba44f5a7dc1830637d0dfff1ae23e1502a22

How researchers see this paper

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

In Artificial Intelligence

1. Wei He
2. Shaocheng Tong
3. Yanjun Liu
4. Chun Lung Philip Chen
5. Shuzhi Sam Ge
6. Qi Zhou
7. Zhao Yin
8. Changyin Sun

In Computational Theory and Mathematics

1. Wei He
2. Changyin Sun
3. Shaocheng Tong
4. Yanjun Liu
5. Zhao Yin
6. Yiting Dong
7. Shuzhi Sam Ge
8. Chun Lung Philip Chen

In Control and Systems Engineering

1. Wei He
2. Shaocheng Tong
3. Yanjun Liu
4. Changyin Sun
5. Yiting Dong
6. Shuzhi Sam Ge
7. Chun Lung Philip Chen
8. Zhao Yin

Citations in the corpus

• Adaptive Neural Network Control of a Robotic Manipulator With Time-Varying Output Constraints (2017, background): “A BLF is introduced in [51] to make sure that the unknown parameters of the function maintain within the designated superset.”
• Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning (2018, methodology): “In [51], the adaptive NN control is presented for a class of output feedback systems, where the BLF is introduced to ensure the boundedness of the closed-loop signals.”
• Neural Network Control-Based Adaptive Learning Design for Nonlinear Systems With Full-State Constraints (2016, positive): “In [52], it is proved that log k2 b j /(k 2 b j − z(2)j ) < z(2)j /(k2 b j − z(2)j ) in the interval |z j | < kb j .”
• Control of nonlinear systems with partial state constraints using a barrier Lyapunov function (2011, background): “Lemma 2 (Ren et al. 2010): For any positive constant kb, positive integer p and any z2R satisfying jzj< kb, we have log k 2p b k 2p b z2p 5 z2p k 2p b z2p ð7Þ”
• Prescribed Performance Control of Uncertain Euler–Lagrange Systems Subject to Full-State Constraints (2018, neutral): “[16], [19], [22], [26]–[28].”
• Adaptive Neural Network Control of an Uncertain Robot With Full-State Constraints (2016, methodology): “[45] presented a novel adaptive neural control method by employing the state estimation observer for a class of output feedback nonlinear systems in the presence of unknown functions.”
• Adaptive Fuzzy Control for Nontriangular Structural Stochastic Switched Nonlinear Systems With Full State Constraints (2019, neutral): “[12] and Liu et al.”
• Control of nonlinear systems with time-varying output constraints (2009, background): “All rights reserved. doi:10.1016/j.automatica.2011.08.044 but also function uncertainty through the use of neural networks (Ren et al., 2010).”
• Vibration Control of a Flexible Beam With Output Constraint (2015, neutral): “In the neurocontrol field, two challenging and open problems are addressed in [28], by using a barrier Lyapunov function in the presence of unknown functions.”
• Adaptive Fuzzy Control for Nonstrict-Feedback Systems With Input Saturation and Output Constraint (2017, background): “Definition 1 [56]: A BLF V(x) (V(x) > 0) is a scalar continuous function, defined with respect to the system ẋ = f (x) on an open region D containing the origin.”
• Adaptive Neural Network Control of a Marine Vessel With Constraints Using the Asymmetric Barrier Lyapunov Function (2017, methodology): “Barrier Lyapunov functions are used for an SISO nonlinear system with output constraints [42], as well as a nonlinear system with the time-varying constraints [43].”