Integral reinforcement learning and experience replay for adaptive optimal control of partially-unknown constrained-input continuous-time systems

By Hamidreza Modares, Frank L. Lewis, Mohammad-Bagher Naghibi-Sistani. at - Automatisierungstechnik, 2014

522 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/a510ef16fb44491073e29c933d2668ebfbe8e6b9

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. Derong Liu
2. Qinglai Wei
3. Ding Wang
4. Shaocheng Tong
5. Yong-ming Li
6. Hamidreza Modares
7. Yanjun Liu
8. Biao Luo

In Computational Theory and Mathematics

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

In Electrical and Electronic Engineering

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

Citations in the corpus

• 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].”
• Optimal tracking control of nonlinear partially-unknown constrained-input systems using integral reinforcement learning (2014, methodology): “…is obtained by minimizing the performance function (7), it is not possible to encode the input constraints into the optimization problem by using a nonquadratic performance function, as has been performed in the optimal regulation problem (Abou-Khalaf & Lewis, 2005; Modares et al., 2013, 2014).”
• Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges (2021, background): “…mentioned above, a wide range of branches in the RL field, such as transfer RL [99], meta RL [197], federated RL [198], inverse RL [37], integral RL [13], Bayesian RL [199], hierarchical RL [200], interpretable RL [201], etc., can improve the learning efficiency and tackle specific problems in…”
• Observer-Based Neuro-Adaptive Optimized Control of Strict-Feedback Nonlinear Systems With State Constraints (2021, background): “In particular, based on universal approximators (e.g., NNs and fuzzy logic systems), numerous intelligent adaptive optima control schemes have been presented in recent years [21]–[29].”
• Adaptive Dynamic Programming for Control: A Survey and Recent Advances (2021, methodology): “To deal with this problem, IRL was developed [88], [89], [107], [108].”