Impulsive Accelerated Reinforcement Learning for $$H_\infty $$ Control
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-8082-6_15
Reference21 articles.
1. Yang, X., Xu, M., Wei, Q.: Adaptive dynamic programming for nonlinear-constrained $$H_\infty $$ control. IEEE Trans. Syst. Man Cybern. Syst. 53(7), 4393–4403 (2023)
2. Luo, B., Wu, H.N., Huang, T.: Off-policy reinforcement learning for $$H_\infty $$ control design. IEEE Trans. Cybern. 45(1), 65–76 (2014)
3. Wang, D., Mu, C.X., Liu, D.R., Ma, H.W.: On mixed data and event driven design for adaptive-critic-based nonlinear $$ H_\infty $$ control. IEEE Trans. Neural Networks Learn. Syst. 29(4), 993–1005 (2017)
4. Kiumarsi, B., Vamvoudakis, K.G., Modares, H., Lewis, F.L.: Optimal and autonomous control using reinforcement learning: a survey. IEEE Trans. Neural Networks Learn. Syst. 29(6), 2042–2062 (2017)
5. Başar, T., Bernhard, P.: $$H_\infty $$ Optimal Control and Related Minimax Design Problems: A Dynamic Game Approach. Springer, Boston (2008). https://doi.org/10.1007/978-0-8176-4757-5
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