Robust Adaptive Dynamic Programming and Feedback Stabilization of Nonlinear Systems

By Yu Jiang, Zhong-Ping Jiang. IEEE Transactions on Neural Networks and Learning Systems, 2014

404 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Theory and Mathematics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/b75a15b1acc09c533134f9d91ea73ded61c1c616

How researchers see this paper

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

In Biomedical Engineering

1. Derong Liu
2. Ding Wang
3. Qinglai Wei
4. Haibo He
5. Biao Luo
6. Frank L. Lewis
7. Hamidreza Modares
8. Xiong Yang

In Computational Theory and Mathematics

1. Derong Liu
2. Ding Wang
3. Qinglai Wei
4. Hamidreza Modares
5. Haibo He
6. Shaocheng Tong
7. Frank L. Lewis
8. Biao Luo

In Electrical and Electronic Engineering

1. Derong Liu
2. Hamidreza Modares
3. Ding Wang
4. Frank L. Lewis
5. Qinglai Wei
6. Haibo He
7. Biao Luo
8. Zhong-Ping Jiang

Citations in the corpus

• Observer-Based Adaptive Fuzzy Fault-Tolerant Optimal Control for SISO Nonlinear Systems (2019, result): “Combining with the result of [41], the authors in [42]–[47] developed the model-based adaptive optimal control design methods for nonlinear systems.”
• Adaptive Dynamic Programming and Adaptive Optimal Output Regulation of Linear Systems (2016, methodology): “The methodology developed in this note may be employed to study the adaptive optimal tracking problem of uncertain nonlinear systems [7], [11], [14], [15], [29], [34] by global or robust adaptive dynamic programming [21], [22].”
• Adaptive Critic Nonlinear Robust Control: A Survey (2017, methodology): “This motivates us to develop an approximate strategy to overcome the difficulty [66], [68], [69], [76], [77], [81]–[85], [120], [126], [131], which results in the ADP-based neural control design.”
• Reinforcement-Learning-Based Robust Controller Design for Continuous-Time Uncertain Nonlinear Systems Subject to Input Constraints (2015, methodology): “More recently, Jiang and Jiang [44] developed a robust ADP algorithm to derive the robust control for a class of uncertain nonlinear systems.”
• Model-Free Optimal Tracking Control via Critic-Only Q-Learning (2016, background): “2585520 RL-based control approaches focused on the regulation control problem, a few of which are [7]–[9], [11], [12], [15], [18], [19], and [22].”
• $ {H}_{ {\infty }}$ Tracking Control of Completely Unknown Continuous-Time Systems via Off-Policy Reinforcement Learning (2015, methodology): “Remark 10: Inspired by the off-policy algorithm in [21], Algorithm 2 has two separate phases.”