Optimal tracking control of nonlinear partially-unknown constrained-input systems using integral reinforcement learning

By Hamidreza Modares, Frank L. Lewis. at - Automatisierungstechnik, 2014

630 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Theory and Mathematics, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/101c26bed7d2896a3b04a53598e2ee498d40bddb

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. Hanquan Lin
2. Qinglai Wei
3. Derong Liu
4. Shaocheng Tong
5. Bo Zhao
6. Ding Wang
7. Yong-ming Li
8. Xiong Yang

In Computational Theory and Mathematics

1. Shaocheng Tong
2. Derong Liu
3. Yong-ming Li
4. Qinglai Wei
5. Ding Wang
6. Hanquan Lin
7. Biao Luo
8. Bo Zhao

In Control and Systems Engineering

1. Shaocheng Tong
2. Yong-ming Li
3. Derong Liu
4. Kangkang Sun
5. Ding Wang
6. Qinglai Wei
7. Hanquan Lin
8. Tingwen Huang

Citations in the corpus

• Value Iteration Adaptive Dynamic Programming for Optimal Control of Discrete-Time Nonlinear Systems (2016, background): “ADP to obtain the solution of HJB equation indirectly and have received more and more attention [33]–[42].”
• Adaptive Dynamic Programming for Control: A Survey and Recent Advances (2021, background): “In [113], the standard form solution of the tracking control problem was first given.”
• Model-Free Optimal Tracking Control via Critic-Only Q-Learning (2016, methodology): “Without requiring the internal system dynamics, online policy iteration methods were employed for the optimal tracking control design of linear continuous-time systems [45] and nonlinear continuous-time systems with control constraints [43], nonlinear discrete-time systems with control constraints [”
• Observer-Based Adaptive Fuzzy Fault-Tolerant Optimal Control for SISO Nonlinear Systems (2019, background): “It should be mentioned that the aforementioned control approaches in [41]–[50] are only suitable for those nonlinear systems in affine form.”
• Reinforcement-Learning-Based Robust Controller Design for Continuous-Time Uncertain Nonlinear Systems Subject to Input Constraints (2015, methodology): “Traditionally, for utilizing RL approaches, a second NN called the action NN is introduced to approximate the control policy [23], [31]–[33], [39], [40].”