Finite-horizon differential games for missile–target interception system using adaptive dynamic programming with input constraints

References

• Abu-Khalaf, M., & Lewis, F. L. (2005). Nearly optimal control laws for nonlinear systems with saturating actuators using a neural network HJB approach. Automatica, 41(5), 779–791. doi:http://dx.doi.org/10.1016/j.automatica.2004.11.034
   Web of Science ®Google Scholar
• Abu-Khalaf, M., Lewis, F. L., & Jie, H. (2008). Neurodynamic programming and zero-sum games for constrained control systems. IEEE Transactions on Neural Networks, 19(7), 1243–1252. doi:10.1109/TNN.2008.2000204
   Web of Science ®Google Scholar
• Bardhan, R., & Ghose, D. (2015). Nonlinear differential games-based impact-angle-constrained guidance law. Journal of Guidance, Control, and Dynamics, 38(3), 384–402. doi:10.2514/1.G000940
   Web of Science ®Google Scholar
• Cheng, T., Lewis, F. L., & Abu-Khalaf, M. (2007). A neural network solution for fixed-final time optimal control of nonlinear systems. Automatica, 43(3), 482–490. doi:http://dx.doi.org/10.1016/j.automatica.2006.09.021
   Web of Science ®Google Scholar
• Cui, X., Zhang, H., Luo, Y., & Zu, P. (2016). Online finite-horizon optimal learning algorithm for nonzero-sum games with partially unknown dynamics and constrained inputs. Neurocomputing, 185, 37–44. doi:http://dx.doi.org/10.1016/j.neucom.2015.12.021
   Web of Science ®Google Scholar
• Fu, Y., & Chai, T. (2016). Online solution of two-player zero-sum games for continuous-time nonlinear systems with completely unknown dynamics. IEEE Transactions on Neural Networks and Learning Systems, 27(12), 2577–2587. doi:10.1109/TNNLS.2015.2496299
   PubMed Web of Science ®Google Scholar
• Guzey, H. M., Xu, H., & Sarangapani, J. (2015). Neural network-based finite horizon optimal adaptive consensus control of mobile robot formations. Optimal Control Applications & Methods, 37(5), 1014–1034. doi:10.1002/oca.2222
   Web of Science ®Google Scholar
• Heydari, A., & Balakrishnan, S. N. (2013). Finite-horizon control-constrained nonlinear optimal control using single network adaptive critics. IEEE Transactions on Neural Networks and Learning Systems, 24(1), 145–157. doi:10.1109/TNNLS.2012.2227339
   PubMed Web of Science ®Google Scholar
• Kumar, S. R., Rao, S., & Ghose, D. (2012). Sliding-mode guidance and control for all-aspect interceptors with terminal angle constraints. Journal of Guidance, Control, and Dynamics, 35(4), 1230–1246. doi:10.2514/1.55242
   Web of Science ®Google Scholar
• Kumar, S. R., Rao, S., & Ghose, D. (2014). Nonsingular terminal sliding mode guidance with impact angle constraints. Journal of Guidance, Control, and Dynamics, 37(4), 1114–1130. doi:10.2514/1.62737
   Web of Science ®Google Scholar
• Liu, D., Yang, X., Wang, D., & Wei, Q. (2015). Reinforcement-learning-based robust controller design for continuous-time uncertain nonlinear systems subject to input constraints. IEEE Transactions on Cybernetics, 45(7), 1372–1385. doi:10.1109/TCYB.2015.2417170
   PubMed Web of Science ®Google Scholar
• Modares, H., Lewis, F. L., & Naghibi-Sistani, M.-B. (2014). Integral reinforcement learning and experience replay for adaptive optimal control of partially-unknown constrained-input continuous-time systems. Automatica, 50(1), 193–202. doi:http://dx.doi.org/10.1016/j.automatica.2013.09.043
   Web of Science ®Google Scholar
• Modares, H., Lewis, F. L., & Sistani, M.-B. N. (2014). Online solution of nonquadratic two-player zero-sum games arising in the H∞ control of constrained input systems. International Journal of Adaptive Control and Signal Processing, 28(3–5), 232–254. doi:10.1002/acs.2348
   Web of Science ®Google Scholar
• Rajarshi, B., & Debasish, G. (2015). An SDRE based differential game approach for maneuvering target interception. AIAA guidance, navigation, and control conference, Kissimmee, FL: American Institute of Aeronautics and Astronautics.
   Google Scholar
• Shaferman, V., & Shima, T. (2008). Linear quadratic guidance laws for imposing a terminal intercept angle. Journal of Guidance, Control, and Dynamics, 31(5), 1400–1412. doi:10.2514/1.32836
   Web of Science ®Google Scholar
• Sun, J., Liu, C., & Ye, Q. (2017). Robust differential game guidance laws design for uncertain interceptor-target engagement via adaptive dynamic programming. International Journal of Control, 90(5), 990–1004. doi:10.1080/00207179.2016.1192687
   Web of Science ®Google Scholar
• Vamvoudakis, K., Vrabie, D., & Lewis, F. (2012). Adaptive optimal control algorithm for zero-sum Nash games with integral reinforcement learning. In AIAA guidance, navigation, and control conference (p. 4773). Minneapolis, MN: American Institute of Aeronautics and Astronautics.
   Google Scholar
• Vamvoudakis, K. G., Miranda, M. F., & Hespanha, J. P. (2016). Asymptotically stable adaptive-optimal control algorithm with saturating actuators and relaxed persistence of excitation. IEEE Transactions on Neural Networks and Learning Systems, 27(11), 2386–2398. doi:10.1109/TNNLS.2015.2487972
   PubMed Web of Science ®Google Scholar
• Vrabie, D., & Lewis, F. (2011). Adaptive dynamic programming for online solution of a zero-sum differential game. Journal of Control Theory and Applications, 9(3), 353–360. doi:10.1007/s11768-011-0166-4
   Google Scholar
• Wang, D., Liu, D., & Wei, Q. (2012). Finite-horizon neuro-optimal tracking control for a class of discrete-time nonlinear systems using adaptive dynamic programming approach. Neurocomputing, 78(1), 14–22. doi:10.1016/j.neucom.2011.03.058
   Web of Science ®Google Scholar
• Wang, D., Mu, C., Zhang, Q., & Liu, D. (2016). Event-based input-constrained nonlinear H∞ state feedback with adaptive critic and neural implementation. Neurocomputing, 214, 848–856. Retrieved from https://doi.org/10.1016/j.neucom.2016.07.002
   Web of Science ®Google Scholar
• Wang, F. Y., Jin, N., Liu, D., & Wei, Q. (2011). Adaptive dynamic programming for finite-horizon optimal control of discrete-time nonlinear systems with ϵ−error bound. IEEE Transactions on Neural Networks, 22(1), 24–36. doi:10.1109/TNN.2010.2076370
   PubMed Web of Science ®Google Scholar
• Wei, Q., Song, R., & Yan, P. (2016). Data-driven zero-sum neuro-optimal control for a class of continuous-time unknown nonlinear systems with disturbance using ADP. IEEE Transactions on Neural Networks and Learning Systems, 27(2), 444–458. doi:10.1109/TNNLS.2015.2464080
   PubMed Web of Science ®Google Scholar
• Wei, Q., Wang, F. Y., Liu, D., & Yang, X. (2014). Finite-approximation-error-based discrete-time iterative adaptive dynamic programming. IEEE Transactions on Cybernetics, 44(12), 2820–2833. doi:10.1109/TCYB.2014.2354377
   PubMed Web of Science ®Google Scholar
• Weiss, M., & Shima, T. (2014). Optimal linear-quadratic missile guidance laws with penalty on command variability. Journal of Guidance, Control, and Dynamics, 38(2), 226–237. doi:10.2514/1.G000738
   Web of Science ®Google Scholar
• Xu, H. (2015). Finite-horizon near optimal design of nonlinear two-player zero-sum game in presence of completely unknown dynamics. Journal of Control, Automation and Electrical Systems, 26(4), 361–370. doi:10.1007/s40313-015-0180-8
   Web of Science ®Google Scholar
• Xu, H., & Jagannathan, S. (2015). Neural network-based finite horizon stochastic optimal control design for nonlinear networked control systems. IEEE Transactions on Neural Networks and Learning Systems, 26(3), 472–485. doi:10.1109/TNNLS.2014.2315622
   PubMed Web of Science ®Google Scholar
• Xu, H., Zhao, Q., Dierks, T., & Jagannathan, S. (2014). Neural network-based finite-horizon approximately optimal control of uncertain affine nonlinear continuous-time systems. In 2014 American control conference (pp. 1243–1248). Portland, OR: IEEE.
   Google Scholar
• Yang, X., Liu, D., & Huang, Y. (2013). Neural-network-based online optimal control for uncertain non-linear continuous-time systems with control constraints. IET Control Theory & Applications, 7(17), 2037–2047. doi:10.1049/iet-cta.2013.0472
   Web of Science ®Google Scholar
• Zhang, H., Wei, Q., & Liu, D. (2011). An iterative adaptive dynamic programming method for solving a class of nonlinear zero-sum differential games. Automatica, 47(1), 207–214. doi:10.1016/j.automatica.2010.10.033
   Web of Science ®Google Scholar
• Zhao, Q., Xu, H., Dierks, T., & Jagannathan, S. (2013). Finite-horizon neural network-based optimal control design for affine nonlinear continuous-time systems. In The 2013 international joint conference on neural networks (IJCNN) (pp. 1–6). Dallas, TX: IEEE.
   Google Scholar
• Zhao, Q., Xu, H., & Jagannathan, S. (2015). Neural network-based finite-horizon optimal control of uncertain affine nonlinear discrete-time systems. IEEE Transactions on Neural Networks and Learning Systems, 26(3), 486–499. doi:10.1109/TNNLS.2014.2315646
   PubMed Web of Science ®Google Scholar
• Zhou, D., Sun, S., & Teo, K. L. (2009). Guidance laws with finite time convergence. Journal of Guidance, Control, and Dynamics, 32(6), 1838–1846. doi:10.2514/1.42976
   Web of Science ®Google Scholar
• Zhou, D., & Xu, B. (2016). Adaptive dynamic surface guidance law with input saturation constraint and autopilot dynamics. Journal of Guidance, Control, and Dynamics, 39(5), 1155–1162. doi:10.2514/1.G001236
   Web of Science ®Google Scholar