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Original Articles

Fault-tolerant optimised tracking control for unknown discrete-time linear systems using a combined reinforcement learning and residual compensation methodology

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Pages 2811-2825 | Received 09 Jul 2016, Accepted 13 Jun 2017, Published online: 06 Jul 2017

References

  • Al-Tamimi, A., Lewis, F., & Abu-Khalaf, M. (2007). Model-free Q-learning designs for linear discrete-time zero-sum games with application to H-infinity control. Automatica, 43(3), 473–481.
  • Blanke, M., Kinnaert, M., Lunze, J., & Staroswiecki, M. (2006). Diagnosis and fault tolerant control, Berlin: Springer Verlag.
  • de Souza, C.E., Trofino, A., & Barbosa, K.A. (2006). Mode-independent H∞ filters for Markovian jump linear systems. IEEE Transactions on Automatic Control, 51(11), 1837–1841.
  • Ding, S.X. (2008). Model-based fault diagnosis techniques: Design schemes, algorithms, and tools, London: Springer Science & Business Media.
  • Ding, S.X. (2014a). Data-driven design of monitoring and diagnosis systems for dynamic processes: A review of subspace technique based schemes and some recent results. Journal of Process Control, 24(2), 431–449.
  • Ding, S.X. (2014b). Data-driven design of fault diagnosis and fault-tolerant control systems, London: Springer Verlag.
  • Ding, S.X., Yang, G., Zhang, P., & Ding, E.L. (2010). Feedback control structures, embedded residual signals, and feedback control schemes with an integrated residual access. IEEE Transactions on Control Systems Technology, 18(2), 352–367.
  • Ding, S.X., Yang, Y., Zhang, Y., & Li, L. (2014). Data-driven realizations of kernel and image representations and their application to fault detection and control system design. Automatica, 50(10), 2615–2623.
  • Feng, J., & Han, K. (2015). Robust full- and reduced-order energy-to-peak filtering for discrete-time uncertain linear systems. Signal Processing, 108, 183–194.
  • Feng, J., Wang, J., Zhang, H., & Han, Z. (2016). Fault diagnosis method of joint fisher discriminant analysis based on the local and global manifold learning and its kernel version. IEEE Transactions on Automation Science and Engineering, 13(1), 122–133.
  • Gao, Z. (2015). Fault estimation and fault-tolerant control for discrete-time dynamic systems. IEEE Transactions on Industrial Electronics, 62(6), 3874–3884.
  • Gao, W., & Jiang, Z.P. (2016). Adaptive dynamic programming and adaptive optimal output regulation of linear systems. IEEE Transactions on Automatic Control, 61(12), 4164–4169. doi:10.1109/TAC.2016.2548662
  • Han, K., & Feng, J. (2015). Improved scalar parameters approach to design robust H∞ filter for uncertain discrete-time linear systems. Signal Processing, 113, 113–123.
  • Huang, B., & Kadali, R. (2008). Dynamic modeling, predictive control and performance monitoring: A data-driven subspace approach, London: Springer Verlag.
  • Jiang, J., & Yu, X. (2012). Fault-tolerant control systems: A comparative study between active and passive approaches. Annual Reviews in Control, 36(1), 60–72.
  • Katayama, T. (2006). Subspace methods for system identification, London: Springer Science & Business Media.
  • Kiumarsi, B., Lewis, F., Modares, H., Karimpour, A., & Mohammad-Bagher, N.S. (2014). Reinforcement Q-learning for optimal tracking control of linear discrete-time systems with unknown dynamics. Automatica, 50(4), 1167–1175.
  • Kiumarsi, B., Lewis, F., Naghibi-Sistani, M.B., & Karimpour, A. (2015). Optimal tracking control of unknown discrete-time linear systems using input-output measured data. IEEE Transactions on Cybernetics, 45(12), 2770–2779.
  • Kruger, U., & Xie, L. (2012). Statistical monitoring of complex multivariate processes: With applications in industrial process control, West Sussex: John Wiley & Sons.
  • Kwakernaak, H., & Sivan, R. (1972). Linear optimal control systems, New York, NY: Wiley-interscience.
  • Liu, L., Wang, Z., & Zhang, H. (2015). Adaptive NN fault-tolerant control for discrete-time systems in triangular forms with actuator fault. Neurocomputing, 152, 209–221.
  • Liu, L., Wang, Z., & Zhang, H. (2016). Adaptive fault-tolerant tracking control for MIMO discrete-time systems via reinforcement learning algorithm with less learning parameters. IEEE Transactions on Automation Science and Engineering, 14(1), 299–313. doi:10.1109/TASE.2016.2517155
  • Liu, Y., Zhang, H., Luo, Y., & Han, J. (2016). ADP based optimal tracking control for a class of linear discrete-time system with multiple delays. Journal of the Franklin Institute, 353(9), 2117–2136.
  • Luan, X., Liu, F., & Shi, P. (2010). Neural-network-based finite-time H∞ control for extended Markov jump nonlinear systems. International Journal of Adaptive Control and Signal Processing, 24(7), 554–567.
  • MacGregor, J., & Cinar, A. (2012). Monitoring, fault diagnosis, fault-tolerant control and optimization: Data driven methods. Computers & Chemical Engineering, 47, 111–120.
  • Modares, H., & Lewis, F. (2014). Linear quadratic tracking control of partially-unknown continuous-time systems using reinforcement learning. IEEE Transactions on Automatic Control, 59(11), 3051–3056.
  • Qin, S.J. (2003). Statistical process monitoring: Basics and beyond. Journal of Chemometrics, 17, 480–502.
  • Qin, S.J. (2006). An overview of subspace identification. Computers & Chemical Engineering, 30(10), 1502–1513.
  • Tong, C., El-Farra, N.H., Palazoglu, A., & Yan, X. (2014). Fault detection and isolation in hybrid process systems using a combined data-driven and observer-design methodology. AIChE Journal, 60(8), 2805–2814.
  • Wang, G., & Huang, Z. (2015). Data-driven fault-tolerant control design for wind turbines with robust residual generator. IET Control Theory & Applications, 9(7), 1173–1179.
  • Wang, Z., Liu, L., Zhang, H., & Xiao, G. (2016). Fault-tolerant controller design for a class of nonlinear MIMO discrete-time systems via online reinforcement learning algorithm. IEEE Transactions on Systems, Man and Cybernetics: Systems, 46(5), 611–622.
  • Wei, Q., Lewis, F., Sun, Q., Yan, P., & Song, R. (2016). Discrete-time deterministic Q-learning: A novel convergence analysis. IEEE Transactions on Cybernetics, 47(5), 1224–1237. doi:10.1109/TCYB.2016.2542923
  • Wei, Q., & Liu, D. (2014). Data-driven neuro-optimal temperature control of water-gas shift reaction using stable iterative adaptive dynamic programming. IEEE Transactions on Industrial Electronics, 61(11), 6399–6408.
  • Werbos, P.J. (1989). A memu of designs for reinforcement learning over time. Neural Networks for Control, 3,67–95.
  • Xiao, B., & Yin, S. (2016). Velocity-free fault-tolerant and uncertainty attenuation control for a class of nonlinear systems. IEEE Transactions on Industrial Electronics, 63(7), 4400–4411.
  • Xiao, B., Yin, S., & Kaynak, O. (2016). Tracking control of robotic manipulators with uncertain kinematics and dynamics. IEEE Transactions on Industrial Electronics, 63(10), 6439–6449.
  • Yang, Y., Zhang, Y., Ding, S.X., & Li, L. (2015). Design and implementation of life cycle management for industrial control applications. IEEE Transactions on Control Systems Technology, 23(4), 1399–1410.
  • Yin, S., Ding, S.X., Haghani, A., Hao, H., & Zhang, P. (2012). A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process. Journal of Process Control, 22(9), 1567–1581.
  • Yin, S., Luo, H., & Ding, S.X. (2014). Real-time implementation of fault-tolerant control systems with performance optimization. IEEE Transactions on Industrial Electronics, 61(5), 2402–2411.
  • Yu, X., & Jiang, J. (2015). A survey of fault-tolerant controllers based on safety-related issues. Annual Reviews in Control, 39, 46–57.
  • Zhang, H., Cui, L., Zhang, X., & Luo, Y. (2011). Data-driven robust approximate optimal tracking control for unknown general nonlinear systems using adaptive dynamic programming method. IEEE Transactions on Neural Networks, 22(12), 2226–2236.
  • Zhang, H., Liu, D., Luo Y., & Wang, D. (2012). Adaptive dynamic programming for control, London: Springer Verlag.
  • Zhang, D., Shi, P., & Wang, Q.G. (2016). Energy-efficient distributed control of large-scale systems: A switched system approach. International Journal of Robust and Nonlinear Control, 26, 3101–3117.
  • Zhang, D., Shi, P., Zhang, W.A., & Yu, L. (2016). Energy-efficient distributed filtering in sensor networks: A unified switched system approach. IEEE Transactions on Cybernetics, 47(7), 1618–1629. doi:10.1109/TCYB.2016.2553043
  • Zhang, H., Wei, Q., & Luo, Y. (2008). A novel infinite-time optimal tracking control scheme for a class of discrete-time nonlinear systems via the greedy HDP iteration algorithm. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 38(4), 937–942.

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