T. Bian and Z. P. Jiang, “Reinforcement learning and adaptive optimal control for continuous-time nonlinear systems: A value iteration approach,” IEEE transactions on neural networks and learning systems, doi: 10.1109/TNNLS.2020.3045087., 2021.
B. Pang, T. Bian, and Z. P. Jiang, “Robust policy iteration for continuous-time linear quadratic regulation,” IEEE Transactions on Automatic Control, doi: 10.1109/TAC.2021.3085510., 2021.
Z. P. Jiang, T. Bian, and W. Gao, “Learning-based control: A tutorial and some recent results,” Foundations and Trends in Systems and Control, vol. 8, no 3, pp. 176-284, 2020.
T. Bian, D. M. Wolpert, and Z. P. Jiang, “Model-free robust optimal feedback mechanisms of biological motor control,” Neural Computation, vol. 32, no 3, pp. 562–595, 2020.
Y.-W. Wang, Y. Lei, T. Bian, and Z.-H. Guan, “Distributed control of nonlinear multi-agent systems with unknown and nonidentical control directions via event-triggered communication," IEEE Transactions on Cybernetics, vol. 50, no 5, pp. 1820-1832, 2020.
T. Bian and Z. P. Jiang, “Continuous-time robust dynamic programming,” SIAM Journal on Control and Optimization, vol. 67, no 6, pp. 4150–4174, 2019.
T. Bian and Z. P. Jiang, “Reinforcement learning for linear continuous-time systems: an incremental learning approach," IEEE/CAA Journal of Automatica Sinica, vol. 6, no 2, pp. 433–440, 2019.
B. Pang, T. Bian and Z. P. Jiang, “Adaptive dynamic programming for finite-horizon optimal control of linear time-varying discrete-time systems," Control Theory and Technology, vol. 17, no 1, pp. 73–84, 2019.
T. Bian and Z. P. Jiang, “Stochastic and adaptive optimal control of uncertain interconnected systems,” Systems & Control Letters, vol. 115, no 5, pp. 48–54, May 2018.
T. Bian and Z. P. Jiang, “A tool for the global stabilization of stochastic nonlinear systems,” IEEE Transactions on Automatic Control, vol. 62, no 4, pp. 1946–1951, April 2017.
T. Bian and Z. P. Jiang, “Value iteration and adaptive dynamic programming for data-driven adaptive optimal control design,” Automatica, vol. 71, pp. 348–360, 2016.
T. Bian and Z. P. Jiang, “Adaptive dynamic programming for stochastic systems with state and control dependent noise,” IEEE Transactions on Automatic Control, vol. 61, no 12, pp. 4170–4175, Dec 2016.
T. Bian and Z. P. Jiang, “New results in global stabilization for stochastic nonlinear systems,” Control Theory and Technology, vol. 14, no 1, pp. 57–67, 2016.
Y.-W. Wang, T. Bian, J.-W. Xiao, and C. Wen, “Global synchronization of complex dynamical networks through digital communication with limited data rate,” IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no 10, pp. 2487–2499, Oct 2015.
T. Bian, Y. Jiang, and Z. P. Jiang, “Decentralized adaptive optimal control of large-scale systems with application to power systems,” IEEE Transactions on Industrial Electronics, vol. 62, pp. 2439–2447, April 2015.
T. Bian, Y. Jiang, and Z. P. Jiang, “Adaptive dynamic programming and optimal control of nonlinear nonaffine systems,” Automatica, vol. 50, pp. 2624–2632, 2014.
Y.-W. Wang, T. Bian, J.-W. Xiao, and Y. Huang, “Robust synchronization of complex switched networks with parametric uncertainties and two types of delays,” International Journal of Robust and Nonlinear Control, vol. 23, no. 2, pp. 190–207, 2013.
B. Pang, T. Bian and Z. P. Jiang, “Data-driven finite-horizon optimal control for linear time-varying discrete-time systems,” in 2018 IEEE Conference on Decision and Control (CDC), pp. 861–866, Dec 2018.
E. Mauro, T. Bian and Z. P. Jiang, “Adaptive dynamic programming for human postural balance control," in Proceedings of the 24th International Conference on Neural Information Processing (ICONIP 2017), pp. 249–257, 2017.
T. Bian and Z. P. Jiang, “Value iteration, adaptive dynamicprogramming, and optimal control of nonlinear systems,” in Proceedings of the 55th IEEE Conference on Decision and Control (CDC), pp. 3375–3380, Dec 2016.
P. Lu, X. Liu, and T. Bian, “Robust adaptive dynamic programming for a three-player zero-sum differential game with unmatched uncertainties," in Proceedings of the 35th Chinese Control Conference (CCC), pp. 2565–2570, July 2016.
T. Bian and Z. P. Jiang, “Model-free robust optimal feedback mechanisms of biological motor control," in Proceedings of 12th World Congress on Intelligent Control and Automation (WCICA 2016), pp. 2029–2034, June 2016.
T. Hong, T. Bian, and F. de León, “Supplementary damping controller of grid connected dc micro-grids based on Q-learning," in Proceedings of the 2016 IEEE Power & Energy Society General Meeting (PESGM2016), July 2016, doi:10.1109/PESGM.2016.7741189.
T. Bian and Z. P. Jiang, “Data-driven robust optimal control design for uncertain cascaded systems using value iteration,” in Proceedings of the 54th IEEE Conference on Decision and Control (CDC), pp. 7610–7615, Dec 2015.
T. Bian and Z. P. Jiang, “Value iteration and adaptive optimal control for linear continuous-time systems,” in Proceedings of the the 7th IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and the 7th IEEE International Conference on Robotics, Automation and Mechatronics (RAM), pp. 53–58, July 2015.
T. Bian and Z. P. Jiang, “Adaptive optimal control for linear stochastic systems with additive noise,” in Proceedings of the 34th Chinese Control Conference (CCC), pp. 3011–3019, July 2015.
B. Yuan, P. Lu, X. Liu, and T. Bian, “Robust adaptive dynamic programming for a zero-sum differential game," in Proceedings of the 34th Chinese Control Conference (CCC), pp. 2468–2473, July 2015.
T. Bian and Z. P. Jiang, “Robust adaptive dynamic programming for continuous-time linear stochastic systems,” in 2014 IEEE International Symposium on Intelligent Control (ISIC), pp. 536–541, Oct 2014.
T. Bian, Y. Jiang, and Z. P. Jiang, “Adaptive dynamic programming for nonlinear nonaffine systems,” in Proceedings of the 53rd IEEE Conference on Decision and Control (CDC), pp. 3603–3608, Dec 2014.
T. Bian, Y.-W. Wang, and J.-W. Xiao, “Robust stability criteria for neural Cohen-Grossberg networks with both time-varying delay and parametric uncertainties,” in Proceedings of the 2nd International Conference on Intelligent Control and Information Processing (ICICIP), vol. 2, pp. 579–584, July 2011.
T. Bian and Y.-W. Wang, “Average consensus of multi-agent systems under logarithm quantized communications,” in Proceedings of the 12th International Conference on Control Automation Robotics Vision (ICARCV), pp. 418–423, Dec 2012.
T. Bian, Biologically inspired adaptive optimal control and learning. PhD thesis, New York University, Tandon School of Engineering, Department of Electrical and Computer Engineering, Brooklyn, NY, May 2017.