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Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications.

, , , , and . IEEE CAA J. Autom. Sinica, 11 (1): 18-36 (January 2024)

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Consensus of Discrete-Time Nonlinear Multiagent Systems Using Sliding Mode Control Based on Optimal Control., and . IEEE Access, (2022)Consensus of Nonlinear Multiagent Systems With Uncertainties Using Reinforcement Learning Based Sliding Mode Control., , , and . IEEE Trans. Circuits Syst. I Regul. Pap., 70 (1): 424-434 (January 2023)New Methods for Optimal Operational Control of Industrial Processes Using Reinforcement Learning on Two Time Scales., , , , , , and . IEEE Trans. Ind. Informatics, 16 (5): 3085-3099 (2020)Off-Policy Interleaved $Q$ -Learning: Optimal Control for Affine Nonlinear Discrete-Time Systems., , , , and . IEEE Trans. Neural Networks Learn. Syst., 30 (5): 1308-1320 (2019)Modelling and robust stability of networked control systems with packet reordering and long delay., , and . Int. J. Control, 82 (10): 1773-1785 (2009)Joint Design of Transmission Rate and Control for Wireless Sensor Networked Control Systems., , , , and . J. Electr. Comput. Eng., (2014)Learning Based Optimal Sensor Selection for Linear Quadratic Control with Unknown Sensor Noise Covariance *., , and . ACC, page 4173-4178. IEEE, (2023)Integrated Sliding Mode Control and Neural Networks Based Packet Disordering Prediction for Nonlinear Networked Control Systems., , and . IEEE Trans. Neural Networks Learn. Syst., 30 (8): 2324-2335 (2019)Off-Policy Q-Learning: Set-Point Design for Optimizing Dual-Rate Rougher Flotation Operational Processes., , , , , and . IEEE Trans. Ind. Electron., 65 (5): 4092-4102 (2018)Optimal tracking control for discrete-time systems by model-free off-policy Q-learning approach., , and . ASCC, page 7-12. IEEE, (2017)