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Home People Research Research Publications Teaching Protocol Sharing News Center for Pain Medicine Research Brief Info Software Alumni Join us Contact us Derong Liu Google Scholar ResearcherID Chair Professor School of System Design and Intelligent Manufacturing   Research Group Member, Academia Europaea (The Academy of Europe) Fellow, Institute of Electrical and Electronics Engineers (IEEE) Fellow, International Neural Network Society (INNS) Fellow, International Association for Pattern Recognition (IAPR) Fellow, Chinese Association of Automation (CAA) Highly Cited Researcher, Clarivate Editor-in-Chief of Artificial Intelligence Review Former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems (2010–2015) IEEE Computational Intelligence Society Neural Networks Pioneer Award International Neural Network Society Dennis Gabor Award NSF Faculty Early Career Development (CAREER) Award Personal Profile Biosketch Derong Liu received the PhD degree in electrical engineering from the University of Notre Dame, USA, in 1994. He became a Full Professor of Electrical and Computer Engineering and of Computer Science at the University of Illinois at Chicago in 2006. He was selected for the “100 Talents Program” by the Chinese Academy of Sciences in 2008, and he served as the Associate Director of The State Key Laboratory of Management and Control for Complex Systems at the Institute of Automation, from 2010 to 2016. He has published 13 books. He received the International Neural Network Society’s Gabor Award in 2018 and the IEEE Computational Intelligence Society Neural Network Pioneer Award in 2022. He has been named a highly cited researcher by Clarivate since 2017. He was the Editor-in-Chief of the IEEE Transactions on Neural Networks and Learning Systems from 2010 to 2015. He is the Editor-in-Chief of Artificial Intelligence Review (Springer). He is a Fellow of the IEEE, a Fellow of the International Neural Network Society, a Fellow of the International Association of Pattern Recognition, and a Member of Academia Europaea (The Academy of Europe). Education Ph.D. Electrical Engineering, University of Notre Dame, 1994 M.Sc. Automatic Control Theory and Applications, Institute of Automation, Chinese Academy of Sciences, 1987 B.Sc. Mechanical Engineering, East China Institute of Technology, 1982 Professional Achievements Member, Academia Europaea (The Academy of Europe, https://ae-info.org), 2021 Fellow, Institute of Electrical and Electronics Engineers (IEEE), 2005 Fellow, International Neural Network Society (INNS), 2013 Fellow, International Association for Pattern Recognition (IAPR), 2016 Fellow, Chinese Association of Automation (CAA), 2010 Editor-in-Chief, Artificial Intelligence Review, 2014–present Editor-in-Chief, IEEE Trans. on Neural Networks and Learning Systems, 2010–2015 Editor, Series on Deep Learning Neural Networks, World Scientific, 2019–present Neural Networks Pioneer Award, IEEE Computational Intelligence Society, 2022 Dennis Gabor Award, International Neural Network Society, 2018 Outstanding Achievement Award, Asia Pacific Neural Network Assembly, 2014 Highly Cited Researcher, Clarivate, 2017–present Chair, IEEE Guangzhou Section, 2019–present IEEE SMC Society Andrew P. Sage Best Transactions Paper Award, 2018 IEEE Trans. Neural Networks and Learning Systems Outstanding Paper Award, 2018 IEEE/CCA Journal of Automatica Sinica Hsue-Shen Tsien Paper Award, 2018 President, Asia Pacific Neural Network Society, 2018 Member of the Council, International Federation of Automatic Control, 2014–2017 Distinguished Lecturer, IEEE Computational Intelligence Society, 2012–2014 and 2016–2018 Board of Governors, International Neural Network Society, 2010–2012 (elected) AdCom Member, IEEE Computational Intelligence Society, 2006–2008 (elected), 2015–2017 (elected), and 2022–2024 (elected) General Chair, 24th International Conference on Neural Information Processing, 2017 General Chair, 12th World Congress on Intelligent Control and Automation, 2016 General Chair, IEEE World Congress on Computational Intelligence, 2014 Program Chair, International Joint Conference on Neural Networks, 2008 University Scholar, University of Illinois, 2006–2009 CAREER Award, National Science Foundation, 1999 Harvey N. Davis Distinguished Teaching Award, Stevens Institute of Technology, 1997 Michael J. Birck Fellowship, University of Notre Dame, 1990 Personal Profile Research Adaptive Dynamic Programming and Reinforcement Learning Intelligent Control and Information Processing Modeling and Control of Complex Industrial Processes Neural Networks and Computational Intelligence Smart Grid Teaching Not available Publications Read More BOOKS    D. Liu and A. N. Michel, Dynamical Systems with Saturation Nonlinearities: Analysis and Design. London: Springer-Verlag, 1994 (ISBN: 0-387-19888-1). A. N. Michel and D. Liu, Qualitative Analysis and Synthesis of Recurrent Neural Networks. New York: Marcel Dekker, 2002 (ISBN: 0-8247-0767-2. This book has been translated into Chinese by H. Zhang, C. Ji, and Z. Wang, and has been published by the Science Press, 2004). D. Liu and P. J. Antsaklis, Editors, Stability and Control of Dynamical Systems with Applications. Boston, MA: Birkhauser, 2003 (ISBN: 0-8176-3233-6). H. Zhang and D. Liu, Fuzzy Modeling and Fuzzy Control. Boston, MA: Birkhauser, 2006 (ISBN: 0-8176-4491-1). F.-Y. Wang and D. Liu, Editors, Advances in Computational Intelligence: Theory and Applications. Singapore: World Scientific, 2006 (ISBN: 981-256-734-8). A. N. Michel, L. Hou, and D. Liu, Stability of Dynamical Systems: Continuous, Discontinuous and Discrete Systems. Boston, MA: Birkhauser, 2008 (ISBN: 978-0-8176-4486-4. The second edition of the book was published in 2015, with a subtitle “On the Role of Monotonic and Non-Monotonic Lyapunov Functions”, ISBN: 978-3-319-15274-5). F.-Y. Wang and D. Liu, Editors, Networked Control Systems: Theory and Applications. London: Springer, 2008 (ISBN: 978-1-84800-214-2). H. Zhang, D. Liu, and Z. Wang, Controlling Chaos: Suppression, Synchronization and Chaotification. London: Springer-Verlag, 2009 (ISBN: 978-1-84882-522-2). F. L. Lewis and D. Liu, Editors, Reinforcement Learning and Approximate Dynamic Programming for Feedback Control. Hoboken, NJ: Wiley, 2013 (ISBN: 978-1-118-10420-0). H. Zhang, D. Liu, Y. Luo, and D. Wang, Adaptive Dynamic Programming for Control: Algorithms and Stability. London: Springer-Verlag, 2013 (ISBN: 978-1-4471-4757-2). D. Liu, C. Alippi, D. Zhao, and H. Zhang, Editors, Frontiers of Intelligent Control and Information Processing. Singapore: World Scientific, 2014 (ISBN: 978-981-4616-87-4). J. Keller, D. Liu, and D. Fogel, Fundamentals of Computational Intelligence–Neural Networks, Fuzzy Systems, and Evolutionary Computation. New York: IEEE/Wiley, 2016 (ISBN: 978-1-119-21434-2). D. Liu, Q. Wei, D. Wang, X. Yang, and H. Li, Adaptive Dynamic Programming with Applications in Optimal Control. Cham, Switzerland: Springer, 2017 (ISBN: 978-3-319-50813-9). SELECTED LIST OF JOUNAL PAPERS 全部论文列表见： https://scholar.google.com/citations?user=hiB8lVkAAAAJ&hl=de&oi=ao 下列是部分期刊论文。 D. Liu and A. N. Michel, “Asymptotic stability of discrete-time systems with saturation nonlinearities with applications to digital filters,” IEEE Transactions on Circuits and Systems-I: Fundamental Theory and Applications, vol. 39, no. 10, pp. 798–807, Oct. 1992. D. Liu and A. N. Michel, “Asymptotic stability of systems operating on a closed hypercube,” Systems & Control Letters, vol. 19, no. 4, pp. 281–285, Oct. 1992. D. Liu and A. N. Michel, “Cellular neural networks for associative memories,” IEEE Transactions on Circuits and Systems-II: Analog and Digital Signal Processing, vol. 40, no. 2, pp. 119–121, Feb. 1993. D. Liu and A. N. Michel, “Null controllability of systems with control constraints and state saturation,” Systems & Control Letters, vol. 20, no. 2, pp. 131–139, Feb. 1993. D. Liu and A. N. Michel, “Stability analysis of state-space realizations for two-dimensional filters with overflow nonlinearities,” IEEE Transactions on Circuits and Systems-I: Fundamental Theory and Applications, vol. 41, no. 2, pp. 127–137, Feb. 1994. D. Liu and A. N. Michel, “Sparsely interconnected neural networks for associative memories with applications to cellular neural networks,” IEEE Transactions on Circuits and Systems-II: Analog and Digital Signal Processing, vol. 41, no. 4, pp. 295–307, Apr. 1994. D. Liu and A. N. Michel, “Stability analysis of systems with partial state saturation nonlinearities,” IEEE Transactions on Circuits and Systems-I: Fundamental Theory and Applications, vol. 43, no. 3, pp. 230–232, Mar. 1996. D. Liu and A. N. Michel, “Robustness analysis and design of a class of neural networks with sparse interconnecting structure,” Neurocomputing, vol. 12, no. 1, pp. 59–76, June 1996. D. Liu, “Cloning template design of cellular neural networks for associative memories,” IEEE Transactions on Circuits and Systems-I: Fundamental Theory and Applications, vol. 44, no. 7, pp. 646–650, July 1997. D. Liu and Z. Lu, “A new synthesis approach for feedback neural networks based on the perceptron training algorithm,” IEEE Transactions on Neural Networks, vol. 8, no. 6, pp. 1468–1482, Nov. 1997. D. Liu, “Lyapunov stability of two-dimensional digital filters with overflow nonlinearities,” IEEE Transactions on Circuits and Systems-I: Fundamental Theory and Applications, vol. 45, no. 5, pp. 574–577, May 1998. D. Liu, E. I. Sara, and W. Sun, “Nested auto-regressive processes for MPEG-encoded video traffic modeling,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 11, no. 2, pp. 169–183, Feb. 2001. D. Liu and A. Molchanov, “Criteria for robust absolute stability of time-varying nonlinear continuous-time systems,” Automatica, vol. 38, no. 4, pp. 627–637, Apr. 2002. D. Liu, M. E. Hohil, and S. H. Smith, “N-bit parity neural networks: New solutions based on linear programming,” Neurocomputing, vol. 48, no. 1–4, pp. 477–488, Oct. 2002. D. Liu, T.-S. Chang, and Y. Zhang, “A constructive algorithm for feedforward neural networks with incremental training,” IEEE Transactions on Circuits and Systems-I: Fundamental Theory and Applications, vol. 49, no. 12, pp. 1876–1879, Dec. 2002. D. Liu, S. Hu, and J. Wang, “Global output convergence of a class of continuous-time recurrent neural networks with time-varying thresholds,” IEEE Transactions on Circuits and Systems-II: Express Briefs, vol. 51, no. 4, pp. 161–167, Apr. 2004. D. Liu, Y. Zhang, and S. Hu, “Call admission policies based on calculated power control setpoints in SIR-based power-controlled DS-CDMA cellular networks,” Wireless Networks, vol. 10, no. 4, pp. 473–483, July 2004. D. Liu, X. Xiong, Z.-G. Hou, and B. DasGupta, “Identification of motifs with insertions and deletions in protein sequences using self-organizing neural networks,” Neural Networks, vol. 18, no. 5–6, pp. 835–842, June-July 2005. D. Liu, Y. Zhang, and H. Zhang, “A self-learning call admission control scheme for CDMA cellular networks,” IEEE Transactions on Neural Networks, vol. 16, no. 5, pp. 1219–1228, Sept. 2005. D. Liu and Y. Cai, “Taguchi method for solving the economic dispatch problem with nonsmooth cost functions,” IEEE Transactions on Power Systems, vol. 20, no. 4, pp. 2006–2014, Nov. 2005. D. Liu, Y. Cai, and G. Tu, “Novel packet coding scheme immune to packet collisions for CDMA-based wireless ad hoc networks,” IEE Proceedings–Communications, vol. 153, no. 1, pp. 1–4, Feb. 2006. D. Liu, X. Xiong, B. DasGupta, and H. Zhang, “Motif discoveries in unaligned molecular sequences using self-organizing neural networks,” IEEE Transactions on Neural Networks, vol. 17, no. 4, pp. 919–928, July 2006. D. Liu, S. Hu, and H. Zhang, “Simultaneous blind separation of instantaneous mixtures with arbitrary rank,” IEEE Transactions on Circuits and Systems-I: Regular Papers, vol. 53, no. 10, pp. 2287–2298, Oct. 2006. D. Liu, Z. Pang, and S. R. Lloyd, “A neural network method for detection of obstructive sleep apnea and narcolepsy based on pupil size and EEG,” IEEE Transactions on Neural Networks, vol. 19, no. 2, pp. 308–318, Feb. 2008. D. Liu, H. Javaherian, O. Kovalenko, and T. Huang, “Adaptive critic learning techniques for engine torque and air-fuel ratio control,” IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics, vol. 38, no. 4, pp. 988–993, Aug. 2008. D. Liu, D. Wang, D. Zhao, Q. Wei, and N. Jin, “Neural-network-based optimal control for a class of unknown discrete-time nonlinear systems using globalized dual heuristic programming,” IEEE Transactions on Automation Science and Engineering, vol. 9, no. 3, pp. 628–634, July 2012. D. Wang, D. Liu, Q. Wei, D. Zhao, and N. Jin, “Optimal control of unknown nonaffine nonlinear discrete-time systems based on adaptive dynamic programming,” Automatica, vol. 48, no. 8, pp. 1825–1832, Aug. 2012. D. Liu, D. Wang, and X. Yang, “An iterative adaptive dynamic programming algorithm for optimal control of unknown discrete-time nonlinear systems with constrained inputs,” Information Sciences, vol. 220, pp. 331–342, Jan. 2013. T. Huang and D. Liu, “A self-learning scheme for residential energy system control and management,” Neural Computing and Applications, vol. 22, no. 2, pp. 259–269, Feb. 2013. D. Liu and Q. Wei, “Finite-approximation-error-based optimal control approach for discrete-time nonlinear systems,” IEEE Transactions on Cybernetics, vol. 43, no. 2, pp. 779–789, Apr. 2013. D. Liu, H. Li, and D. Wang, “Neural-network-based zero-sum game for discrete-time nonlinear systems via iterative adaptive dynamic programming algorithm,” Neurocomputing, vol. 110, pp. 92–100, June 2013. D. Liu, Y. Huang, D. Wang, and Q. Wei, “Neural-network-observer-based optimal control for unknown nonlinear systems using adaptive dynamic programming,” International Journal of Control, vol. 86, no. 9, pp. 1554–1566, Sept. 2013. D. Liu, D. Wang, and H. Li, “Decentralized stabilization for a class of continuous-time nonlinear interconnected systems using online learning optimal control approach,” IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 2, pp. 418–428, Feb. 2014. D. Liu and Q. Wei, “Policy iteration adaptive dynamic programming algorithm for discrete-time nonlinear systems,” IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 3, pp. 621–634, Mar. 2014. D. Liu, H. Li, and D. Wang, “Online synchronous approximate optimal learning algorithm for multiplayer nonzero-sum games with unknown dynamics,” IEEE Transactions on Systems, Man and Cybernetics: Systems, vol. 44, no.8, pp. 1015–1027, Aug. 2014. Q. Wei and D. Liu, “Data-driven neuro-optimal temperature control of water-gas shift reaction using stable iterative adaptive dynamic programming,” IEEE Transactions on Industrial Electronics, vol. 61, no. 11, pp. 6399–6408, Nov. 2014. Q. Wei and D. Liu, “Adaptive dynamic programming for optimal tracking control of unknown nonlinear systems with application to coal gasification,” IEEE Transactions on Automation Science and Engineering, vol. 11, no. 4, pp. 1020–1036, Oct. 2014. D. Liu, P. Yan, and Q. Wei, “Data-based analysis of discrete-time linear systems in noisy environment: Controllability and observability,” Information Sciences, vol. 288, pp. 314–329, Dec. 2014. D. Liu, D. Wang, F. Wang, H. Li, and X. Yang, “Neural-network-based online HJB solution for optimal robust guaranteed cost control of continuous-time uncertain nonlinear systems,” IEEE Transactions on Cybernetics, vol. 44, no. 12, pp. 2834–2847, Dec. 2014. Q. Wei, D. Liu, and X. Yang, “Infinite horizon self-learning optimal control of nonaffine discrete-time nonlinear systems,” IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 4, pp. 866–879, Apr. 2015. D. Liu, H. Li, and D. Wang, “Error bounds for adaptive dynamic programming algorithms for solving undiscounted optimal control problems,” IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 6, pp. 1323–1334, June 2015. D. Liu, X. Yang, D. Wang, and Q. Wei, “Reinforcement-learning-based robust controller design for continuous-time uncertain nonlinear systems subject to input constraints,” IEEE Transactions on Cybernetics, vol.45, no.7, pp.1372–1385, July 2015. D. Liu, C. Li, H. Li, D. Wang, and H. Ma, “Neural-network-based decentralized control of continuous-time nonlinear interconnected systems with unknown dynamics,” Neurocomputing, vol. 165, pp. 90–98, Oct. 2015. D. Liu, Q. Wei, and P. Yan, “Generalized policy iteration adaptive dynamic programming for discrete-time nonlinear systems,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 45, no. 12, pp. 1577–1591, Dec. 2015. Q. Wei, D. Liu, and H. Lin, “Value iteration adaptive dynamic programming for optimal control of discrete-time nonlinear systems,” IEEE Transactions on Cybernetics, vol. 46, no. 3, pp. 840–853, Mar. 2016. D. Liu, Y. Xu, Q. Wei, and X. Liu, “Residential energy scheduling for variable weather solar energy based on adaptive dynamic programming,” IEEE/CAA Journal of Automatica Sinica, vol. 5, no. 1, pp. 36–46, Jan. 2018. B. Zhao and D. Liu(*), “Event-triggered decentralized tracking control of modular reconfigurable robots through adaptive dynamic programming,” IEEE Transactions on Industrial Electronics, vol. 67, no. 4, pp. 3054–3064, Apr. 2020. D. Liu, S. Xue, B. Zhao, B. Luo, and Q. Wei, “Adaptive dynamic programming for control: A survey and recent advances,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 1, pp. 142–160, Jan. 2021. M. Ha, D. Wang, and D. Liu, “Generalized value iteration for discounted optimal control with stability analysis,” Systems & Control Letters, vol. 147, Jan. 2021, article no. 104847. B. Zhao, F. Luo, H. Lin, and D. Liu, “Particle swarm optimized neural networks based local tracking control scheme of unknown nonlinear interconnected systems,” Neural Networks, vol. 134, pp. 54–63, Feb. 2021. B. Luo, T. Huang, and D. Liu, “Periodic event-triggered suboptimal control with sampling period and performance analysis,” IEEE Transactions on Cybernetics, vol. 51, no. 3, pp. 1253–1261, Mar. 2021. Y. Li, B. Luo, D. Liu, Y. Yang, and Z. Yang, “Robust exponential synchronization for memristor neural networks with nonidentical characteristics by pinning control,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 3, pp. 1966–1980, Mar. 2021. Y. W. Zhang, B. Zhao, and D. Liu, “Event-triggered adaptive dynamic programming for multi-player zero-zum games with unknown dynamics,” Soft Computing, vol. 25, pp. 2237–2251, 2021. B. Zhao, D. Liu, and C. Alippi, “Sliding-mode surface-based approximate optimal control for uncertain nonlinear systems with asymptotically stable critic structure,” IEEE Transactions on Cybernetics, vol. 51, no. 6, pp. 2858–2869, June 2021. S. Xue, B. Luo, and D. Liu, “Event-triggered adaptive dynamic programming for unmatched uncertain nonlinear continuous-time systems,” IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 7, pp. 2939–2951, July 2021. B. Luo, Y. Yang, and D. Liu, “Policy iteration Q-learning for data-based two-player zero-sum game of linear discrete-time systems,” IEEE Transactions on Cybernetics, vol. 51, no. 7, pp. 3630–3640, July 2021. Q. Wei, T. Li, and D. Liu, “Learning control for air conditioning systems via human expressions,” IEEE Transactions on Industrial Electronics, vol. 68, no. 8, pp. 7662–7671, Aug. 2021. F. Luo, B. Zhao, and D. Liu, “Event-triggered decentralized fault tolerant control for mismatched interconnected nonlinear systems through adaptive dynamic programming,” Optimal Control Applications and Methods, vol. 42, no. 5, pp. 1365–1384, Sept./Oct. 2021. S. Xue, B. Luo, D. Liu, and Y. Gao, “Adaptive dynamic programming-based event-triggered optimal tracking control,” International Journal of Robust and Nonlinear Control, vol. 31, no. 15, pp. 7480–7497, Oct. 2021. S. Zhang, B. Zhao, D. Liu, and Y. W. Zhang, “Observer-based event-triggered control for zero-sum games of input constrained multi-player nonlinear systems,” Neural Networks, vol. 144, pp. 101–112, Dec. 2021. M. Ha, D. Wang, and D. Liu, “Neural-network-based discounted optimal control via an integrated value iteration with accuracy guarantee,” Neural Networks, vol. 144, pp. 176–186, Dec. 2021. Y. W. Zhang, B. Zhao, D. Liu, and S. Zhang, “Event-triggered optimal tracking control of multiplayer unknown nonlinear systems via adaptive critic designs,” International Journal of Robust and Nonlinear Control, vol. 32, no. 1, pp. 29–51, Jan. 2022. S. Xue, B. Luo, D. Liu, and Y. Yang, “Constrained event-triggered H-infinity control based on adaptive dynamic programming with concurrent learning,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 1, pp. 357–369, Jan. 2022. Q. Wei, L. Zhu, R. Song, P. Zhang, D. Liu, and J. Xiao, “Model-free adaptive optimal control for unknown nonlinear multiplayer nonzero-sum game,” IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 2, pp. 879–892, Feb. 2022. J. Li, B. Zhao, and D. Liu, “DMPP: Differentiable multi-pruner and predictor for neural network pruning,” Neural Networks, vol. 147, pp. 103–112, Mar. 2022. Z. Zhang, S. Peng, D. Liu, Y. Wang, and T. Chen, “Leader-following mean-square consensus of stochastic multiagent systems with ROUs and RONs via distributed event-triggered impulsive control,” IEEE Transactions on Cybernetics, vol. 52, no. 3, pp. 1836–1849, Mar. 2022. X. Fang, D. Liu, S. Duan, and L. Wang, “Memristive LIF spiking neuron model and its application in Morse code,” Frontiers in Neuroscience, vol. 16, Article 853010, Apr. 2022. Q. Luo, S. Xue, and D. Liu, “Adaptive critic designs for decentralised robust control of nonlinear interconnected systems via event-triggering mechanism,” International Journal of Systems Science, vol. 53, no. 5, pp. 1031–1047, 2022. Q. Wei, L. Zhu, T. Li, and D. Liu, “A new approach to finite-horizon optimal control for discrete-time affine nonlinear systems via a pseudolinear method,” IEEE Transactions on Automatic Control, vol. 67, no. 5, pp. 2610–2617, May 2022. M. Lin, B. Zhao, and D. Liu, “Policy gradient adaptive critic designs for model-free optimal tracking control with experience replay,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 6, pp. 3692–3703, June 2022. M. Ha, D. Wang, and D. Liu, “Discounted iterative adaptive critic designs with novel stability analysis for tracking control,” IEEE/CAA Journal of Automatica Sinica, vol. 9, no. 7, pp. 1262–1272, July 2022. S. Xue, B. Luo, D. Liu, and Y. Gao, “Event-triggered integral reinforcement learning for nonzero-sum games with asymmetric input saturation,” Neural Networks, vol. 152, pp. 212–223, Aug. 2022. Y. W. Zhang, B. Zhao, D. Liu, and S. Zhang, “Event-triggered control of discrete-time zero-sum games via deterministic policy gradient adaptive dynamic programming,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 8, pp. 4823–4835, Aug. 2022. S. Xue, B. Luo, D. Liu, and Y. Gao, “Event-triggered ADP for tracking control of partially unknown constrained uncertain systems,” IEEE Transactions on Cybernetics, vol. 52, no. 9, pp. 9001–9012, Sep. 2022. S. Xue, B. Luo, D. Liu, and Y. Gao, “Neural network-based event-triggered integral reinforcement learning for constrained H∞tracking control with experience replay,” Neurocomputing, vol. 513, pp. 25–35, Nov. 2022. M. Ha, D. Wang, and D. Liu, “Offline and online adaptive critic control designs with stability guarantee through value iteration,” IEEE Transactions on Cybernetics, vol. 52, no. 12, pp. 13262–13274, Dec. 2022. Q. Wu, B. Zhao, D. Liu, and M. M. Polycarpou, “Event-triggered adaptive dynamic programming for decentralized tracking control of input constrained unknown nonlinear interconnected systems,” Neural Networks, vol. 157, pp. 336–349, Jan. 2023. S. Zhang, B. Zhao, D. Liu, C. Alippi, and Y. W. Zhang, “Event-triggered robust control for multi-player nonzero-sum games with input constraints and mismatched uncertainties,” International Journal of Robust and Nonlinear Control, vol. 33, no. 5, pp. 3086–3106, Mar. 2023. M. Lin, B. Zhao, and D. Liu, “Policy gradient adaptive dynamic programming for nonlinear discrete-time zero-sum games with unknown dynamics,” Soft Computing, vol. 27, pp. 5781–5795, May 2023. R. Chai, D. Liu, A. Tsourdos, Y. Xia, and S. Chai, “Deep learning-based trajectory planning and control for autonomous ground vehicle parking maneuver,” IEEE Transactions on Automation Science and Engineering, vol. 20, no. 3, pp. 1633–1647, July 2023. Y. W. Zhang, B. Zhao, D. Liu, and S. Zhang, “Adaptive dynamic programming-based event-triggered robust control for multiplayer nonzero-sum games with unknown dynamics,” IEEE Transactions on Cybernetics, vol. 53, no. 8, pp. 5151–5164, Aug. 2023. M. Liang, Y. Wang, and D. Liu, “An efficient impulsive adaptive dynamic programming algorithm for stochastic systems,” IEEE Transactions on Cybernetics, vol. 53, no. 9, pp. 5545–5559, Sept. 2023. M. Ha, D. Wang, and D. Liu, “A novel value iteration scheme with adjustable convergence rate,” IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 10, pp. 7430–7442, Oct. 2023. D. Lin, S. Xue, D. Liu, M. Liang, and Y. Wang, “Adaptive dynamic programming-based hierarchical decision-making of non-affine systems,” Neural Networks, vol. 167, pp. 331–341, Oct. 2023. C. Zeng, B. Zhao, and D. Liu, “Fault tolerant control for a class of nonlinear systems with multiple faults using neuro-dynamic programming,” Neurocomputing, vol. 553, Oct. 2023, article no. 126502. B. Zhao, Y. Zhang, and D. Liu, “Adaptive dynamic programming-based cooperative motion/force control for modular reconfigurable manipulators: A joint task assignment approach,” IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 12, pp. 10944–10954, Dec. 2023. B. Zhao, G. Shi, and D. Liu, “Event-triggered local control for nonlinear interconnected systems through particle swarm optimization-based adaptive dynamic programming,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 12, pp. 7342–7353, Dec. 2023. J. Lin, B Zhao, D. Liu, and Y. Wang, “Dynamic compensator-based near-optimal control for unknown nonaffine systems via integral reinforcement learning,” Neurocomputing, vol. 564, Jan. 2024, article no. 126973. People Read More PrevNext UpDown Join us Please contact liudr@sustech.edu.cn if you would like to join us. Read More Contact Us Contact Address Southern University of Science and Technology, Shenzhen 518055, China Office Phone 0755- Email liudr@sustech.edu.cn