刘德荣，南方科技大学讲席教授、博士生导师。1994年从美国圣母大学毕业并获电气工程博士学位。从1999年开始，在芝加哥伊利诺依大学电气与计算机工程系工作，先后任该校助教授（1999–2002)、终身职副教授（2002–2006）和终身职正教授（2006年起)。2008年，入选中国科学院项目。曾任中国科学院自动化研究所复杂系统管理与控制国家重点实验室副主任（2010–2016)。自1992年以来，共发表了270多篇SCI论文、280多篇国际会议论文。同他人合作共出版过13本书。获得2018年国际神经网络学会终身贡献奖和2022年IEEE计算智能学会神经网络先驱奖。2017年起连续多年获得Clarivate高被引学者称号。曾任《IEEE神经网络与学习系统汇刊》主编、IFAC理事、亚太神经网络学会主席。现任中国自动化学会常务理事、《人工智能评论》主编。2005年当选IEEE Fellow、2013年当选INNS Fellow、2016年当选IAPR Fellow、2021年当选欧洲科学院院士。 
 
研究领域
◆ 智能控制理论及应用
◆ 自适应动态规划与强化学习
◆ 复杂工业系统建模与控制
◆ 计算智能
◆ 智能信息处理
 
工作经历
◆ 2022–今，南方科技大学讲席教授、博士生导师
◆ 2017–2022年，广东工业大学自动化学院特聘教授、博士生导师
（2015–2016年，北京科技大学自动化学院副院长、教授、博士生导师、教育部钢铁流程先进控制重点实验室主任）
◆ 2010–2016年，中国科学院自动化研究所研究员、博士生导师、复杂系统管理与控制国家重点实验室副主任
◆ 2008–2009年，中国科学院自动化研究所研究员、博士生导师
◆ 1999–今，美国伊利诺伊大学芝加哥分校电气与计算机工程系助教授、终身职副教授、2006年起任终身职正教授
◆ 1993–1995年，美国通用汽车公司研发中心Staff Fellow
◆ 1987–1990年，中国科学院研究生院无线电电子学部助教
◆ 1982–1984年，北方工业公司国营向阳仪表厂技术员
 
学习经历
◆ 1990–1993年，美国圣母大学电气工程系，获博士学位
◆ 1984–1987年，中国科学院自动化研究所，获工学硕士学位
◆ 1978–1982年，华东工学院（现南京理工大学）机械电子工程系，获工学学士学位
 
所获荣誉
◆ 欧洲科学院院士 (Academia Europaea, The Academy of Europe), 2021
◆ Fellow，电气与电子工程学会，2005
◆ Fellow，国际神经网络学会，2013
◆ Fellow，国际模式识别学会，2016
◆ 中国自动化学会会士，2010
◆ IEEE计算智能学会神经网络先驱奖，2022
◆ 国际神经网络学会Dennis Gabor终身贡献奖，2018
◆ 中国发明协会发明创业奖创新奖一等奖，2021
◆ 中国自动化学会自然科学奖一等奖，2017
◆ "科睿唯安"高被引学者, 2017–今
◆ 亚太神经网络联合会杰出成就奖，2014
◆ IEEE Systems, Man, and Cybernetics Society Andrew P. Sage最佳汇刊论文奖，2018
◆  IEEE Transactions on Neural Networks and Learning Systems杰出论文奖，2018
◆ IEEE/CAA Journal of Automatica Sinica钱学森论文奖，2018国家自然科学基金
◆ “海外杰出青年合作研究基金” (杰青B类)，2008  
◆ 伊利诺伊大学University Scholar奖，2006
◆ 美国国家科学基金会教授早期事业发展奖，1999  
 

代表文章

[1] 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.

[2] 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.

[3] 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.

[4] 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.

[5] 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.

[6] 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.

[7] 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.

[8] 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.

[9] 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.

[10] 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.

[11] 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.

[12] 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.

[13] 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.

[14] 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.

[15] 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.

[16] 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.

[17] 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.

[18] 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.

[19] 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.

[20] 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.

[21] 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.

[22] 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.

[23] 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.

[24] 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.

[25] 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.

[26] 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.

[27] 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.

[28] 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.

[29] 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.

[30] 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.

[31] 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.

[32] 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.

[33] 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.

[34] 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.

[35] 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.

[36] 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.

[37] 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.

[38] 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.

[39] 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.

[40] 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.

[41] 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.

[42] 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.

[43] 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.

[44] 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.

[45] 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.

[46] 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.

[47] 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.

[48] 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.

[49] 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.

[50] 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.

[51] 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.

[52] 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.

[53] 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.

[54] 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.

[55] 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.

[56] 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.

[57] 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.

[58] 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.

[59] 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.

[60] 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.

[61] 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.

[62] 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.

[63] 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.

[64] 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.

[65] 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.

[66] 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.

[67] 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.

[68] 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.

[69] 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.

[70] 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.

[71] 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.

[72] 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.

[73] 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.

[74] 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.

[75] 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.

[76] 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.

[77] 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.

[78] 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.

[79] 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.

[80] 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.

[81] 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.

[82] 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.

[83] 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.

[84] 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.

[85] 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.

[86] 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.

[87] 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.

[88] 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.