師資
劉德榮，南方科技大學講席教授、博士生導師。1994年從美國圣母大學畢業并獲電氣工程博士學位。從1999年開始，在芝加哥伊利諾依大學電氣與計算機工程系工作，先后任該校助教授（1999–2002)、終身職副教授（2002–2006）和終身職正教授（2006年起)。2008年，入選中國科學院項目。曾任中國科學院自動化研究所復雜系統管理與控制國家重點實驗室副主任（2010–2016)。自1992年以來，共發表了270多篇SCI論文、280多篇國際會議論文。同他人合作共出版過13本書。獲得2018年國際神經網絡學會終身貢獻獎和2022年IEEE計算智能學會神經網絡先驅獎。2017年起連續多年獲得Clarivate高被引學者稱號。曾任《IEEE神經網絡與學習系統匯刊》主編、IFAC理事、亞太神經網絡學會主席。現任中國自動化學會常務理事、《人工智能評論》主編。2005年當選IEEE Fellow、2013年當選INNS Fellow、2016年當選IAPR Fellow、2021年當選歐洲科學院院士。
研究領域
◆ 智能控制理論及應用
◆ 自適應動態規劃與強化學習
◆ 復雜工業系統建模與控制
◆ 計算智能
◆ 智能信息處理
工作經歷
◆ 2022–今，南方科技大學講席教授、博士生導師
◆ 2017–2022年，廣東工業大學自動化學院特聘教授、博士生導師
（2015–2016年，北京科技大學自動化學院副院長、教授、博士生導師、教育部鋼鐵流程先進控制重點實驗室主任）
◆ 2010–2016年，中國科學院自動化研究所研究員、博士生導師、復雜系統管理與控制國家重點實驗室副主任
◆ 2008–2009年，中國科學院自動化研究所研究員、博士生導師
◆ 1999–今，美國伊利諾伊大學芝加哥分校電氣與計算機工程系助教授、終身職副教授、2006年起任終身職正教授
◆ 1993–1995年，美國通用汽車公司研發中心Staff Fellow
◆ 1987–1990年，中國科學院研究生院無線電電子學部助教
◆ 1982–1984年，北方工業公司國營向陽儀表廠技術員
學習經歷
◆ 1990–1993年，美國圣母大學電氣工程系，獲博士學位
◆ 1984–1987年，中國科學院自動化研究所，獲工學碩士學位
◆ 1978–1982年，華東工學院（現南京理工大學）機械電子工程系，獲工學學士學位
所獲榮譽
◆ 歐洲科學院院士 (Academia Europaea, The Academy of Europe), 2021
◆ Fellow，電氣與電子工程學會，2005
◆ Fellow，國際神經網絡學會，2013
◆ Fellow，國際模式識別學會，2016
◆ 中國自動化學會會士，2010
◆ IEEE計算智能學會神經網絡先驅獎，2022
◆ 國際神經網絡學會Dennis Gabor終身貢獻獎，2018
◆ 中國發明協會發明創業獎創新獎一等獎，2021
◆ 中國自動化學會自然科學獎一等獎，2017
◆ "科睿唯安"高被引學者, 2017–今
◆ 亞太神經網絡聯合會杰出成就獎，2014
◆ IEEE Systems, Man, and Cybernetics Society Andrew P. Sage最佳匯刊論文獎，2018
◆ IEEE Transactions on Neural Networks and Learning Systems杰出論文獎，2018
◆ IEEE/CAA Journal of Automatica Sinica錢學森論文獎，2018國家自然科學基金
◆ “海外杰出青年合作研究基金” (杰青B類)，2008
◆ 伊利諾伊大學University Scholar獎，2006
◆ 美國國家科學基金會教授早期事業發展獎，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.