Publications

*Google Scholar Citations*

Journal Papers

[20] D. Xie, X. Zhong, “Semicentralized Deep Deterministic Policy Gradient in Cooperative StarCraft Games,” IEEE Trans. on Neural Networks and Learning Systems, 2021, in press, DOI: 10.1109/TNNLS.2020.3042943.

[19] X. Zhong, and H. He, "GrHDP Solution for Optimal Consensus Control of Multi-Agent Discrete-Time Systems," IEEE Transactions on Systems, Man and Cybernetics: Systems, vol. 50, no. 7, pp. 2362-2374, July, 2020.

[18] Q. Wei, X. Wang, X. Zhong, and N. Wu, "Consensus Control of Leader-Following Multi-Agent Systems in Directed Topology With Heterogeneous Disturbances," IEEE/CAA Journal of Automatica Sinica, vol. 8, no. 2, pp. 423 - 431, 2020.

[17] D. Wang and X. Zhong, "Advanced Policy Learning Near-Optimal Regulation," IEEE/CAA Journal of Automatica Sinica, vol. 6, no. 3, pp. 743-749, 2019.

[16] J. Yi, S. Chen, X. Zhong, H. He and W. Zhou, "Event-Triggered Globalized Dual Heuristic Programming and Its Application to Networked Control Systems," IEEE Transactions on Industrial Informatics, vol. 15, no. 3, pp. 1383 - 1392, June 2018.

[15] H. He and X. Zhong, "Learning Without External Reward," IEEE Computational Intelligence Magazine, vol. 13, no. 3, pp. 48-54, Aug. 2018.

[14] X. Yang, H. He, and X. Zhong, "Adaptive Dynamic Programming for Robust Regulation and Its Application to Power Systems," IEEE Trans. on Industrial Electronics, vol. 65, no. 7, pp. 5722 - 5732, July 2018.

[13] X. Zhong, H. He, D. Wang, and Z. Ni, "Model-Free Adaptive Control for Unknown Nonlinear Zero-Sum Differential Game," IEEE Trans. on Cybernetics, vol. 48, no. 5, pp. 1633 - 1646, 2018.

[12] D. Wang, H. He, X. Zhong, and D. Liu, "Event-Driven Nonlinear Discounted Optimal Regulation Involving a Power System Application," IEEE Trans on Industrial Eletronics, vol. 64, no. 10, pp. 8177-8186, 2017.

[11] X. Zhong, Z. Ni, and H. He, "Gr-GDHP: A New Architecture for Globalized Dual Heuristic Dynamic Programming," IEEE Trans. on Cybernetics, vol. 47, no. 10, pp. 3318-3330, 2017.

[10] X. Zhong and H. He, "An Event-Triggered ADP Control Approach for Continuoustime System with Unknown Internal States," IEEE Trans. on Cybernetics, vol. 47, no. 3, pp. 683-694, 2017.

[9] L. Dong, X. Zhong, C. Sun, and H. He, "Event-Triggered Adaptive Dynamic Programming for Continuous-Time System With Control Constrains," IEEE Trans. on Neural Networks and Learning Systems (TNNLS), vol. 28, no. 8, pp. 1941-1952, 2017.

[8] L. Dong, X. Zhong, C. Sun, and H. He, "Adaptive Event-Triggered Control based on Heuristic Dynamic Programming for Nonlinear Discrete-time Systems," IEEE Trans. on Neural Networks and Learning Systems (TNNLS), vol. 28, no. 7, pp. 1594-1605, 2017.

[7] J. Yan, H. He, X. Zhong, Y. Tang, and Yan L. Sun, "Q-learning Based Vulnerability Analysis of Smart Grid against Sequential Topology Attacks," IEEE Transactions on Information Forensics and Security, 2016 (in press)

[6] X. Zhong, Z. Ni, and H. He, "A Theoretical Foundation of Goal Representation Heuristic Dynamic Programming," IEEE Trans. on Neural Networks and Learning Systems (TNNLS), vol. 27, no. 12, pp. 2513-2525, 2016.

[5] X. Zhong, H. He, H. Zhang, and Z. Wang, "A Neural Network based Online Learning and Control Approach for Markov Jump Systems," Neurocomputing, vol. 149, Part A, pp. 116-123, 2015.

[4] Z. Ni, H. He, X. Zhong, and D. V. Prokhorov, "Model-Free Dual Heuristic Dynamic Programming," IEEE Trans. on Neural Networks and Learning Systems (TNNLS), vol. 26, no. 8, pp. 1834-1839, 2015.

[3] Y. Tang, H. He, Z. Ni, X. Zhong, D. Zhao, and X. Xu, "Fuzzy-Based Goal Representation Adaptive Dynamic Programming," IEEE Trans. on Fuzzy Systems, vol. 24, no. 5, pp. 1159-1175, 2016.

[2] X. Zhong, H. He, H. Zhang, and Z.Wang, “Optimal Control for Unknown Discrete-Time Nonlinear Markov Jump Systems Using Adaptive Dynamic Programming,” IEEE Trans. Neural Networks and Learning Systems, vol. 25, no. 12, pp. 2141-2155, 2014. (Top 50 most frequently downloaded papers)

[1] X. Zhong, Z. Wang, and H. Zhang, “Robust Stabilization of a Class of Uncertain Markov Jump Linear Systems with Partly Unknown Transition Probabilities,” Journal of Jilin University Engineering and Technology Edition, vol. 42, issue 6, pp. 1558-1562, 2012. (in Chinese)

Book Chapter

[1] Z. Ni, H. He, and X. Zhong, "Experimental Studies on Data-Driven Heuristic Dynamic Programming for POMDP," in Frontiers of Intelligent Control and Information Processing, Editors: D. Liu, C. Alippi, D. Zhao, and H. Zhang, World Scientific Publishing, 2014.

Selected Conference Papers

[24] X. Zhong, and Z. Ni, "An Intelligent and Secure Control Approach for Nonlinear Systems under Attacks," in IEEE Symposium Series on Computational Intelligence (SSCI), Orlando, FL, Dec. 4-7, 2021.

[23] X. Zhong, and H. He, "A Reinforcement Learning-Based Control Approach for Unknown Nonlinear Systems with Persistent Adversarial Inputs,” in IEEE International Joint Conference on Neural Networks (IJCNN), Jul. 18-22, 2021.

[22] A. Das, Z. Ni, and X. Zhong, “Aggregating Learning Agents for Microgrid Energy Scheduling During ExtremeWeather Events,” in IEEE Power & Energy Society (PES) General Meeting, Jul. 23-26, 2021. (Best Paper Award).

[21] T. Nguyen, W. Gao, H. Gutierrez, X. Zhong, and S. Agarwal, “Reinforcement Learning and Adaptive Optimal Control of Congestion Pricing,” in International Federation of Automatic Control Symposium on Control in Transportation Systems (IFAC-Control), Lille, France, Jun. 8-10, 2021.

[20] W. C. Cheng, Z. Ni, and X. Zhong, “Experimental Evaluation of Proximal Policy Optimization and Advantage Actor-Critic RL Algorithms using MiniGrid Environment,” in Florida Conference on Recent Advances in Robotics, May 13-14, 2021.

[19] X. Zhong, and H. He, “Event-Triggered Multi-Agent Optimal Regulation Using Adaptive Dynamic Programming,” in IEEE International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, Jul. 19-24, 2020.

[18] A. Das, Z. Ni, X. Zhong, and Di Wu, “Experimental Validation of Approximate Dynamic Programming Based Optimization and Convergence on Microgrid Applications,” in IEEE Power & Energy Society (PES) General Meeting, Aug. 3-6, 2020.

[17] D. Xie, X. Zhong, “Deep Deterministic Policy Gradients with Transfer Learning Framework in StarCraft Micromanagement,” in IEEE International Conference on Electro/Information Technology (EIT), Brookings, SD, May 20-22, 2019.

[16] D. Xie, X. Zhong, Q. Yang, and Y. Huang, “Comprehensive Cooperative Deep Deterministic Policy Gradients for Multi-Agent Systems in Unstable Environment,” in SPIE Defense + Commercial Sensing Symposium on Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, Baltimore, Maryland, Apr. 14-18, 2019.

[15] X. Zhong, and Z. Ni, “Data-Driven Reinforcement Learning Design for Multi-agent Systems with Unknown Disturbances,” in IEEE World Congress on Computational Intelligence (WCCI), Rio de Janeiro, Brazio, Jul. 8-13, 2018.

[14] Z. Ni, S. Paul and X. Zhong, “A Reinforcement Learning Approach for Sequential Decision-Making Process in Smart Grid Security,” in IEEE Symposium Series on Computational Intelligence (SSCI), Honolulu, HI, Nov. 27-Dec. 1, 2017.

[13] Z. Ni, P. Paudyal, and X. Zhong, “A Computational Intelligence Approach for Residential Home Energy Management Considering Reward Incentives,” in IEEE Symposium Series on Computational Intelligence (SSCI), Honolulu, HI, Nov. 27-Dec. 1, 2017.

[12] Z. Ni, N. Malla and X. Zhong, “Towards Enabling Deep Learning Techniques for Adaptive Dynamic Programming,” in IEEE International Joint Conference on Neural Network (IJCNN), Anchorage, AK, May 14-19, 2017.

[11] X. Zhong, Z. Ni, and H. He, “Convergence Analysis of GrDHP-based Optimal Control for Discrete-time Nonlinear System,” in IEEE International Joint Conference on Neural Networks (IJCNN), Vancouver, Canada, Jul. 24-29, 2016.

[10] A. Das, Z. Ni, and X. Zhong, “Near Optimal Control for Microgrid Energy Systems Considering Battery Lifetime Characteristics.” in IEEE Symposium Series on Computational Intelligence (SSCI), Athens, Greece, Dec. 6-9, 2016.

[9] S. Poudel, Z. Ni, X. Zhong and H. He, “Comparative Studies of Power Grid Security with Network Connectivity and Power Flow Information Using Unsupervised Learning,” in IEEE International Joint Conference on Neural Networks (IJCNN), Vancouver, Canada, Jul. 24-29, 2016.

[8] X. Zhong, Z. Ni, and H. He, “Event-Triggered ADP for Continuous-time Nonlinear System Using Measured Input-Output Data,” in IEEE International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland, Jul. 12-17, 2015.

[7] Z. Ni, X. Zhong, and H. He, “A Boundedness Theoretical Analysis for GrADP Design: A Case Study on Maze Navigation,” in IEEE International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland, Jul. 12-17, 2015.

[6] L. Dong, X. Zhong, C. Sun, and H. He, “Predictive Event-Triggered Control Based on Heuristic Dynamic Programming for Nonlinear Continuous-Time Systems,” in IEEE International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland, Jul. 12-17, 2015.

[5] X. Zhong, Z. Ni, Y. Tang and H. He, “Data-Driven Partially Observable Dynamic Processes Using Adaptive Dynamic Programming,” in IEEE Symposium Series on Computational Intelligence (SSCI), Orlando, Florida, Dec. 9-12, 2014.

[4] Y. Tang, X. Zhong, Z. Ni, J. Yan and H. He, “Impact of Signal Transmission Delays on Power System Damping Control Using Heuristic Dynamic Programming,” in IEEE Symposium Series on Computational Intelligence (SSCI), Orlando, Florida, Dec. 9-12, 2014.

[3] X. Zhong, Z. Ni, H. He, X. Xu, and D. Zhao, “Event-Triggered Reinforcement Learning Approach for Unknown Nonlinear Continuous-Time System,” in IEEE World Congress on Computational Intelligence (WCCI), Beijing, China, Jul. 6-11, 2014.

[2] X. Zhong, H. He, and D. V. Prokhorov, “Robust Controller Design of Continuous-time Nonlinear System Using Neural Network,” in IEEE International Joint Conference on Neural Networks (IJCNN), Dallas, TX, Aug. 4-9, 2013.

[1] Z. Wang, D. Zhou, D. Gong, X. Zhong, “Synchronization Stability in an Array of Linearly Coupled Connected Neural Networks with Multiple Delays,” in Chinese Control and Decision Conference (CCDC), Taiyuan, China, May 23-25, 2012.