859
Views
51
CrossRef citations to date
0
Altmetric
Original Articles

Model-Free control performance improvement using virtual reference feedback tuning and reinforcement Q-learning

, &
Pages 1071-1083 | Received 30 Mar 2016, Accepted 11 Sep 2016, Published online: 29 Sep 2016

References

  • Basin, M.V., & Ramirez, P.C.R. (2014). A supertwisting algorithm for systems of dimension more than one. IEEE Transactions on Industrial Electronics, 61(11), 6472–6480.
  • Bazanella, A.S., Campestrini, L., & Eckhard, D. (2012). Data-Driven controller design: The H2 approach. Berlin: Springer-Verlag.
  • Bazanella, A.S., & Neuhaus, T. (2014). Tuning nonlinear controllers with the virtual reference approach. Proceedings of 19th World Congress of the International Federation of Automatic Control, Cape Town, South Africa, 10269–10274.
  • Blažič, S., Matko, D., & Škrjanc, I. (2010). Adaptive law with a new leakage term. IET Control Theory & Applications, 4(9), 1533–1542.
  • Campestrini, L., Eckhard, D., Gevers, M., & Bazanella, A. (2011). Virtual reference feedback tuning for non-minimum phase plants. Automatica, 47(8), 1778–1784.
  • Campi, M.C., Lecchini, A., & Savaresi, S.M. (2002). Virtual reference feedback tuning: A direct method for the design of feedback controllers. Automatica, 38(8), 1337–1346.
  • Campi, M.C., & Savaresi, S.M. (2006). Direct nonlinear control design: The Virtual Reference Feedback Tuning (VRFT) approach. IEEE Transactions on Automatic Control, 51(1), 14–27.
  • Eckhard, D., & Bazanella, A.S. (2012). Robust convergence of the steepest descent method for data-based control. International Journal of Systems Science, 43(10), 1969–1975.
  • Ernst, D., Geurts, P., & Wehenkel, L. (2005). Tree-based batch mode reinforcement learning. Journal of Machine Learning Research, 6(4), 503–556.
  • Erokhin, V., & Volkov, V. (2015). Recovering images, registered by device with inexact point-spread function, using Tikhonov's regularized least squares method. International Journal of Artificial Intelligence, 13(1), 123–134.
  • Esparza, A., Sala, A., & Albertos, P. (2011). Neural networks in virtual reference tuning. Engineering Applications of Artificial Intelligence, 24(6), 983–995.
  • Filatovas, E., Podkopaev, D., & Kurasova, O. (2015). A visualization technique for accessing solution pool in interactive methods of multiobjective optimization. International Journal of Computers, Communication & Control, 10(4), 508—519.
  • Filip, F.G. (2008). Decision support and control for large-scale complex systems. Annual Reviews in Control, 32(1), 61–70.
  • Fliess, M., & Join, C. (2013). Model-free control. International Journal of Control, 86(12), 2228–2252.
  • Formentin, S., Savaresi, S.M., & Del Re, L. (2014). Direct multivariable controller tuning for internal combustion engine test benches. Control Engineering Practice, 29(8), 115–122.
  • Giagkiozis, I., Purshouse, R.C., & Fleming, P.J. (2015). An overview of population-based algorithms for multi-objective optimisation. International Journal of Systems Science, 46(9), 1572–1599.
  • Haber, R.E., & Alique, J.R. (2007). Fuzzy logic-based torque control system for milling process optimization. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 37(5), 941–950.
  • Hafner, R., & Riedmiller, M. (2005). Neural fitted Q iteration - first experiences with a data efficient neural reinforcement learning method. In J. Gama, R. Camacho, P. Brazdil, A. Jorge, and L. Torgo (Eds.), Machine learning: ECML 2005 (Lecture Notes in Computer Science, vol. 3720, pp. 317–328). Berlin: Springer-Verlag.
  • Hafner, R., & Riedmiller, M. (2007). Neural reinforcement learning controllers for a real robot application. Proceedings of 2007 IEEE Conference on Robotics and Automation, Rome, Italy, 2098–2103.
  • Hafner, R., & Riedmiller, M. (2011). Reinforcement learning in feedback control. Challenges and benchmarks from technical process control. Machine Learning, 84(1), 137–169.
  • Hjalmarsson, H. (2002). Iterative feedback tuning – an overview. International Journal of Adaptive Control and Signal Processing, 16(5), 373–395.
  • Hou, Z., & Jin, S. (2011). Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems. IEEE Transactions on Neural Networks, 22(12), 2173–2188.
  • Inteco, Ltd. (2007). Two rotor aerodynamical system, user's manual. Krakow: Inteco Ltd.
  • Lewis, F., & Vamvoudakis, K.G. (2011). Reinforcement learning for partially observable dynamic processes: Adaptive dynamic programming using measured output data. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 41(1), 14–25.
  • Lewis, F., Vrabie, D., & Vamvoudakis, K.G. (2012). Reinforcement learning and feedback control: Using natural decision methods to design optimal adaptive controllers. IEEE Control Systems Magazine, 32(6), 76–105.
  • Liu, Y., Chen, H.-W., & Lu, J.-Q. (2014). Data-based controllability analysis of discrete-time linear time-delay systems. International Journal of Systems Science, 45(11), 2411–2417.
  • Liu, D., Javaherian, H., Kovalenko, O., & Huang, T. (2008). Adaptive critic learning techniques for engine torque and air-fuel ratio control. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 38(4), 988–993.
  • Moraes Rêgo, P.H., da Fonseca Neto, J.V., & Ferreira, E.M. (2015). Convergence of the standard RLS method and UDUT factorisation of covariance matrix for solving the algebraic Riccati equation of the DLQR via heuristic approximate dynamic programming. International Journal of Systems Science, 46(11), 2006–2028.
  • Passenbrunner, T.E., Formentin, S., Savaresi, S.M., & Del Re, L. (2014). Direct multivariable controller tuning for internal combustion engine test benches. Control Engineering Practice, 29(8), 115–122.
  • Precup, R.-E., David, R.-C., Petriu, E.M., Preitl, S., & Radac, M.-B. (2011). Gravitational search algorithms in fuzzy control systems tuning. Proceedings of 18th World Congress of the International Federation of Automatic Control, Milano, Italy, 13624–13629.
  • Precup, R.-E., David, R.-C., Petriu, E.M., Preitl, S., & Radac, M.-B. (2013). Fuzzy logic-based adaptive gravitational search algorithm for optimal tuning of fuzzy-controlled servo systems. IET Control Theory & Applications, 7(1), 99–107.
  • Precup, R.-E., David, R.-C., Petriu, E.M., Preitl, S., & Radac, M.-B. (2014). Novel adaptive charged system search algorithm for optimal tuning of fuzzy controllers. Expert Systems with Applications, 41(4), 1168–1175.
  • Precup, R.-E., Radac, M.-B., Tomescu, M.L., Petriu, E.M., & Preitl, S. (2013). Stable and convergent iterative feedback tuning of fuzzy controllers for discrete-time SISO systems. Expert Systems with Applications, 40(1), 188–199.
  • Previdi, F., Schauer, T., Savaresi, S.M., & Hunt, K.J. (2004). Data-driven control design for neuroprotheses: A Virtual Reference Feedback Tuning (VRFT) approach. IEEE Transactions on Control Systems Technology, 12(1), 176–182.
  • Prokhorov, D.V., & Wunsch, D.C. (1997). Adaptive critics design. IEEE Transactions on Neural Networks, 8(5), 997–1007.
  • Qin, C., Zhang, H., Luo, Y., & Wang, B. (2014). Finite horizon optimal control of non-linear discrete-time switched systems using adaptive dynamic programming with ϵ-error bound. International Journal of Systems Science, 45(8), 1683–1693.
  • Radac, M.-B., Precup, R.-E., & Petriu, E.M. (2015). Model-free primitive-based iterative learning control approach to trajectory tracking of MIMO systems with experimental validation. IEEE Transactions on Neural Networks and Learning Systems, 26(11), 2925–2938.
  • Radac, M.-B., Precup, R.-E., Petriu, E.M., Preitl, S., & Dragos, C.-A. (2013). Data-driven reference trajectory tracking algorithm and experimental validation. IEEE Transactions on Industrial Informatics, 9(4), 2327–2336.
  • Spall, J.C., & Cristion, J.A. (1998). Model-free control of nonlinear stochastic systems with discrete-time measurements. IEEE Transactions on Automatic Control, 43(9), 1198–1210.
  • Sutton, R.S., & Barto, A.G. (1998). Reinforcement learning: An introduction. Cambridge, MA: MIT Press.
  • Tikk, D., Johanyák, Z.C., Kovács, S. & Wong, K.W. (2011). Fuzzy rule interpolation and extrapolation techniques: Criteria and evaluation guidelines. Journal of Advanced Computational Intelligence and Intelligent Informatics, 15(3), 254–263.
  • van Heusden, K., Karimi, A., & Bonvin, D. (2011). Data-driven model reference control with asymptotically guaranteed stability. International Journal of Adaptive Control and Signal Processing, 25(4), 331–351.
  • Walsh, T.J., Nouri, A., Li, L., & Littman, M.L. (2007). Planning and learning in environments with delayed feedback. In J.N. Kok et al. (Eds.), Machine learning: ECML 2007 (Lecture Notes in Computer Science, vol. 4701, pp. 442–453). Berlin: Springer-Verlag.
  • Wang, Z., & Liu, D. (2011). Data-based controllability and observability analysis of linear discrete-time systems. IEEE Transactions on Neural Networks, 22(12), 2388–2392.
  • Wang, F.-Y., Zhang, H., & Liu, D. (2009). Adaptive dynamic programming: an introduction. IEEE Computational Intelligence Magazine, 4(2), 39–47.
  • Watkins, C, & Dayan, P. (1992). Q-learning. Machine Learning, 8(3–4), 279–292.
  • Werbos, P.J. (1992). Approximate dynamic programming for real-time control and neural modeling. In D.A. White and D.A. Sofge (Eds.), Handbook of Intelligent Control: Neural, Fuzzy and Adaptive Approaches (ch. 13, pp. 493–525). New York, NY: Van Nostrand Reinhold.
  • Xu, D., Jiang, B., & Shi, P. (2014). A novel model-free adaptive control design for multivariable industrial processes. IEEE Transactions on Industrial Electronics, 61(11), 6391–6398.
  • Yan, P., Liu, D., Wang, D., & Ma, H. (2016). Data-driven controller design for general MIMO nonlinear systems via virtual reference feedback tuning and neural networks. Neurocomputing, 171(1), 815–825.
  • Yin, S., Li, X., Gao, H., & Kaynak, O. (2015). Data-based techniques focused on modern industry: An overview. IEEE Transactions on Industrial Electronics, 62(1), 657–667.
  • Zhai, D., Zhang, Q.-L., & Liu, G.-Y. (2014). Data-driven criteria synthesis of system with two-degree-of-freedom controller. International Journal of Systems Science, 45(11), 2275–2281.
  • Zhang, Y., Ding, S.X., Yang, Y., & Li, L. (2015). Data-driven design of two-degree-of-freedom controllers using reinforcement learning techniques. IET Control Theory and Applications, 9(7), 1011–1021.
  • Zhang, R.K., Hou, Z.S., Ji, H.H., & Yin, C.K. (2016). Adaptive iterative learning control for a class of non-linearly parameterised systems with input saturations. International Journal of Systems Science, 47(5), 1084–1094.
  • Zhang, J., Zhang, H., Wang B., & Cai, T. (2016). Nearly data-based optimal control for linear discrete model-free systems with delays via reinforcement learning. International Journal of Systems Science, 47(7), 1563–1573.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.