A Deep Reinforcement Learning-Based PID Tuning Strategy for Nonlinear MIMO Systems with Time-varying Uncertainty

Author:

Wang Hao,Ricardez-Sandoval Luis A.

Publisher

Elsevier BV

Reference11 articles.

1. An adaptive deep reinforcement learning approach for MIMO PID control of mobile robots;Carlucho;ISA Transactions,2020

2. Ding, T.L., Norris, S., and Subiantoro, A. (2022). Adaptive reinforcement learning PI controllers for vapor compression cycle control.

3. A hybrid search H∞ based synthesis of static output feedback controllers for uncertain systems with application to multivariable PID control;Gopmandal;International Journal of Robust and Nonlinear Control,2021

4. Hollenstein, J., Auddy, S., Saveriano, M., Renaudo, E., and Piater, J. (2022). Action noise in off-policy deep reinforcement learning: Impact on exploration and performance. arXiv preprint arXiv:2206.03787.

5. Lillicrap, T.P., Hunt, J.J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., and Wierstra, D. (2015). Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971.

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