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Volume 9 Issue 10
Oct.  2022

IEEE/CAA Journal of Automatica Sinica

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Article Contents
X. B. Ping, J. C. Hu, T. Y. Lin, B. C. Ding, P. Wang, and  Z. W. Li,  “A survey of output feedback robust MPC for linear parameter varying systems,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 10, pp. 1717–1751, Oct. 2022. doi: 10.1109/JAS.2022.105605
Citation: X. B. Ping, J. C. Hu, T. Y. Lin, B. C. Ding, P. Wang, and  Z. W. Li,  “A survey of output feedback robust MPC for linear parameter varying systems,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 10, pp. 1717–1751, Oct. 2022. doi: 10.1109/JAS.2022.105605

A Survey of Output Feedback Robust MPC for Linear Parameter Varying Systems

doi: 10.1109/JAS.2022.105605
Funds:  This work was supported in part by the National Natural Science Foundation of China (62103319, 62073053, 61773396)
More Information
  • For constrained linear parameter varying (LPV) systems, this survey comprehensively reviews the literatures on output feedback robust model predictive control (OFRMPC) over the past two decades from the aspects on motivations, main contributions, and the related techniques. According to the types of state observer systems and scheduling parameters of LPV systems, different kinds of OFRMPC approaches are summarized and compared. The extensions of OFRMPC for LPV systems to other related uncertain systems are also investigated. The methods of dealing with system uncertainties and constraints in different kinds of OFRMPC optimizations are given. Key issues on OFRMPC optimizations for LPV systems are discussed. Furthermore, the future research directions on OFRMPC for LPV systems are suggested.

     

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  • [1]
    Y. Xi and D. Li, Predictive Control: Fundamentals and Developments. Hoboken, NJ: John Wiley & Sons, Inc, 2019.
    [2]
    Y. Xi, D. Li, and S. Lin, “Model predictive control–status and challenges,” Acta Automatica Sinica, vol. 39, no. 3, pp. 222–236, 2013. doi: 10.1016/S1874-1029(13)60024-5
    [3]
    D. Q. Mayne, J. B. Rawlings, C. V. Rao, and P. O. M. Scokaert, “Constrained model predictive control: Stability and optimality,” Automatica, vol. 36, no. 6, pp. 789–814, 2000. doi: 10.1016/S0005-1098(99)00214-9
    [4]
    J. H. Lee, “Model predictive control: Review of the three decades of development,” Int. Journal of Control,Automation and Systems, vol. 9, no. 3, pp. 415–424, 2011. doi: 10.1007/s12555-011-0300-6
    [5]
    D. Q. Mayne, “Model predictive control: Recent developments and future promise,” Automatica, vol. 50, no. 12, pp. 2967–2986, 2014. doi: 10.1016/j.automatica.2014.10.128
    [6]
    S. V. Raković and W. S. Levine, Handbook of Model Predictive Control. Springer, Switzerland, 2018.
    [7]
    S. Gros, M. Zanon, R. Quirynen, A. Bemporad, and M. Diehl, “From linear to nonlinear MPC: Bridging the gap via the real-time iteration,” Int. Journal of Control, vol. 93, no. 1, pp. 62–80, 2020. doi: 10.1080/00207179.2016.1222553
    [8]
    S. J. Qin and T. A. Badgwell, “A survey of industrial model predictive control technology,” Control Engineering Practice, vol. 11, no. 7, pp. 733–764, 2003. doi: 10.1016/S0967-0661(02)00186-7
    [9]
    M. L. Darby and M. Nikolaou, “MPC: Current practice and challenges,” Control Engineering Practice, vol. 20, no. 4, pp. 328–342, 2012. doi: 10.1016/j.conengprac.2011.12.004
    [10]
    A. Parisio, E. Rikos, G. Tzamalis, and L. Glielmo, “Use of model predictive control for experimental microgrid optimization,” Applied Energy, vol. 115, pp. 37–46, 2014. doi: 10.1016/j.apenergy.2013.10.027
    [11]
    S. Kouro, M. A. Perez, J. Rodriguez, A. M. Llor, and H. A. Young, “Model predictive control: MPC’s role in the evolution of power electronics,” IEEE Industrial Electronics Magazine, vol. 9, no. 4, pp. 8–21, 2015. doi: 10.1109/MIE.2015.2478920
    [12]
    S. Vazquez, J. Rodriguez, M. Rivera, L. G. Franquelo, and M. Norambuena, “Model predictive control for power converters and drives: Advances and trends,” IEEE Trans. Industrial Electronics, vol. 64, no. 2, pp. 935–947, 2017. doi: 10.1109/TIE.2016.2625238
    [13]
    B. Kouvaritakis and M. Cannon, Model Predictive Control. Switzerland: Springer Int. Publishing, 2016.
    [14]
    M. B. Saltik, L. Özkan, J. H. Ludlage, S. Weiland, and P. M. Van den Hof, “An outlook on robust model predictive control algorithms: Reflections on performance and computational aspects,” Journal of Process Control, vol. 61, pp. 77–102, 2018. doi: 10.1016/j.jprocont.2017.10.006
    [15]
    G. C. Goodwin, H. Kong, G. Mirzaeva, and M. M. Seron, “Robust model predictive control: Reflections and opportunities,” Journal of Control and Decision, vol. 1, no. 2, pp. 115–148, 2014. doi: 10.1080/23307706.2014.913837
    [16]
    C. Hoffmann and H. Werner, “A survey of linear parameter-varying control applications validated by experiments or high-fidelity simulations,” IEEE Trans. Control Systems Technology, vol. 23, no. 2, pp. 416–433, 2015. doi: 10.1109/TCST.2014.2327584
    [17]
    J. Mohammadpour and C. W. Scherer, Control of Linear Parameter Varying Systems With Applications. New York, NY: Springer Science and Business Media, 2012.
    [18]
    O. Sename, P. Gaspar, and J. Bokor, Robust Control and Linear Parameter Varying Approaches: Application to Vehicle Dynamics. New York, NY: Springer, 2013.
    [19]
    H. S. Abbas, R. Tóth, M. Petreczky, N. Meskin, and J. Mohammadpour, “Embedding of nonlinear systems in a linear parameter-varying representation,” IFAC Proceedings Volumes, vol. 47, no. 3, pp. 6907–6913, 2014.
    [20]
    M. M. Morato, G. Q. B. Tran, G. N. dos Reis, J. E. Normey-Rico, and O. Sename, “NMPC through qLPV embedding: A tutorial review of different approaches,” in Proc. 7th IFAC Conf. Nonlinear Model Predictive Control, Bratislava, Slovakia, 2021.
    [21]
    J. Hanema, R. Tóth, and M. Lazar, “Stabilizing non-linear model predictive control using linear parameter-varying embeddings and tubes,” IET Control Theory &Applications, vol. 15, no. 10, pp. 1404–1421, 2021.
    [22]
    R. Findeisen, L. Imsland, F. Allgöwer, and B. A. Foss, “State and output feedback nonlinear model predictive control: An overview,” European Journal of Control, vol. 9, no. 2–3, pp. 190–206, 2003.
    [23]
    L. Imsland, R. Findeisen, E. Bullinger, F. Allgöwer, and B. A. Foss, “A note on stability, robustness and performance of output feedback nonlinear model predictive control,” Journal of Process Control, vol. 13, no. 7, pp. 633–644, 2003. doi: 10.1016/S0959-1524(03)00006-4
    [24]
    F. Blanchini, “Set invariance in control,” Automatica, vol. 35, no. 11, pp. 1747–1767, 1999. doi: 10.1016/S0005-1098(99)00113-2
    [25]
    F. Blanchini and S. Miani, Set-Theoretic Methods in Control. Birkhäuser: Basel, 2008.
    [26]
    S. Boyd, L. El Ghaoui, E. Feron, and V. Balakrishnan, Linear Matrix Inequalities in System and Control Theory. SIAM, Philadelphia, USA, 1994.
    [27]
    W. Langson, I. Chryssochoos, S. V. Raković, D. Q. Mayne, “Robust model predictive control using tubes,” Automatica, vol. 40, no. 1, pp. 125–133, 2004. doi: 10.1016/j.automatica.2003.08.009
    [28]
    D. Q. Mayne, M. M. Seron, and S. V. Raković, “Robust model predictive control of constrained linear systems with bounded disturbances,” Automatica, vol. 41, no. 2, pp. 219–224, 2005. doi: 10.1016/j.automatica.2004.08.019
    [29]
    M. V. Kothare, V. Balakrishnan, and M. Morari, “Robust constrained model predictive control using linear matrix inequalities,” Automatica, vol. 32, no. 10, pp. 1361–1379, 1996. doi: 10.1016/0005-1098(96)00063-5
    [30]
    M. M. Morato, J. E. Normey-Rico, and O. Sename, “Model predictive control design for linear parameter varying systems: A survey,” Annual Reviews in Control, vol. 49, pp. 64–80, 2020. doi: 10.1016/j.arcontrol.2020.04.016
    [31]
    A. Casavola, D. Famularo, and G. Franzè, “A feedback min-max MPC algorithm for LPV systems subject to bounded rates of change of parameters,” IEEE Trans. Automatic Control, vol. 47, no. 7, pp. 1147–1153, 2002. doi: 10.1109/TAC.2002.800662
    [32]
    S. Yu, C. Böhm, H. Chen, and F. Allgöwer, “Model predictive control of constrained LPV systems,” Int. Journal of Control, vol. 85, no. 6, pp. 671–683, 2012. doi: 10.1080/00207179.2012.661878
    [33]
    Y. Lu and Y. Arkun, “Quasi-min-max MPC algorithm for LPV systems,” Automatica, vol. 36, no. 4, pp. 527–540, 2000. doi: 10.1016/S0005-1098(99)00176-4
    [34]
    D. Li and Y. Xi, “The feeback robust MPC for LPV systems with bounded rates of parameters changes,” IEEE Trans. Automation Control, vol. 55, no. 2, pp. 503–507, 2010. doi: 10.1109/TAC.2009.2037464
    [35]
    P. Zheng, D. Li, Y. Xi, and J. Zhang, “Improved model prediction and RMPC design for LPV systems with bounded parameter changes,” Automatica, vol. 49, no. 12, pp. 3695–3699, 2013. doi: 10.1016/j.automatica.2013.09.024
    [36]
    D. He, H. Huang, and Q. Chen, “Quasi-min-max MPC for constrained nonlinear systems with guaranteed input-to-state stability,” Journal of the Franklin Institute, vol. 351, no. 6, pp. 3405–3423, 2014. doi: 10.1016/j.jfranklin.2014.03.006
    [37]
    D. R. Ramirez, T. Alamo, E. F. Camacho, “Computational burden reduction in min-max MPC,” Journal of the Franklin Institute, vol. 348, no. 9, pp. 2430–2447, 2011. doi: 10.1016/j.jfranklin.2011.07.008
    [38]
    Z. Wan and M. V. Kothare, “An efficient off-line formulation of robust model predictive control using linear matrix inequalities,” Automatica, vol. 39, no. 5, pp. 837–846, 2003. doi: 10.1016/S0005-1098(02)00174-7
    [39]
    B. Ding, Y. Xi, and M. T. Cychowski, T. O’Mahony, “Improving off-line approach to robust MPC based-on nominal performance cost,” Automatica, vol. 43, no. 1, pp. 158–163, 2007. doi: 10.1016/j.automatica.2006.07.022
    [40]
    P. Bumroongsri and S. Kheawhom, “An ellipsoidal off-line model predictive control strategy for linear parameter varying systems with applications in chemical processes,” Systems &Control Letters, vol. 61, no. 3, pp. 435–442, 2012.
    [41]
    T. Besselmann, J. Lofberg, and M. Morari, “Explicit MPC for LPV systems: Stability and optimality,” Int. Journal of Control, vol. 57, no. 9, pp. 2322–2332, 2012.
    [42]
    H. S. Abbas, G. Männel, C. H. né Hoffmann, and P. Rostalski, “Tube-based model predictive control for linear parameter-varying systems with bounded rate of parameter variation,” Automatica, vol. 107, pp. 21–28, 2019. doi: 10.1016/j.automatica.2019.04.046
    [43]
    J. Hanema, M. Lazar, and R. Tóth, “Heterogeneously parameterized tube model predictive control for LPV systems,” Automatica, vol. 111, Art. no. 108622, 2020.
    [44]
    R. Heydari and M. Farrokhi, “Robust tube-based model predictive control of LPV systems subject to adjustable additive disturbance set,” Automatica, vol. 129, Art. no. 109672, 2021.
    [45]
    Q. T. Dinh, S. Gumussoy, W. Michiels, and M. Diehl, “Combining convex-concave decompositions and linearization approaches for solving BMIs, with application to static output feedback,” IEEE Trans. Automatic Control, vol. 57, no. 6, pp. 1377–1390, 2012. doi: 10.1109/TAC.2011.2176154
    [46]
    S. Kanev, C. Scherer, M. Verhaegen, and B. De Schutter, “Robust output-feedback controller design via local BMI optimization,” Automatica, vol. 40, no. 7, pp. 1115–1127, 2004. doi: 10.1016/j.automatica.2004.01.028
    [47]
    F. Schweppe, “Recursive state estimation: Unknown but bounded errors and system inputs,” IEEE Trans. Automatic Control, vol. 13, no. 1, pp. 22–28, 1968. doi: 10.1109/TAC.1968.1098790
    [48]
    D. Q. Mayne, S. V. Raković, R. Findeisen, and F. Allgöwer, “Robust output feedback model predictive control of constrained linear systems,” Automatica, vol. 42, no. 7, pp. 1217–1222, 2006. doi: 10.1016/j.automatica.2006.03.005
    [49]
    D. Q. Mayne, S. V. Raković, R. Findeisen, and F. Allgöwer, “Robust output feedback model predictive control of constrained linear systems: Time varying case,” Automatica, vol. 45, no. 9, pp. 2082–2087, 2009. doi: 10.1016/j.automatica.2009.05.009
    [50]
    S. Subramanian, S. Lucia, and S. Engell, “A novel tube-based output feedback MPC for constrained linear systems,” in Proc. American Control Conf., Seattle, USA, pp. 3060–3065, 2017.
    [51]
    J. Lorenzetti and M. Pavone, “A simple and efficient tube-based robust output feedback model predictive control scheme,” in Proc. European Control Conf., pp. 1775–1782, Saint Petersburg, Russia, 2020.
    [52]
    Y. Su, K. Tan, and T. H. Lee, “Tube based quasi-min-max output feedback MPC for LPV systems,” IFAC Proceedings Volumes, vol. 45, no. 15, pp. 186–191, 2012. doi: 10.3182/20120710-4-SG-2026.00010
    [53]
    C. Hu, X. Zhou, Y. Ren, and Y. Tao, “Output feedback polytopic LPV tube-RMPC control for air-breathing hypersonic vehicles,” Int. Journal of Modelling Identification and Control, vol. 28, no. 4, pp. 336–348, 2017. doi: 10.1504/IJMIC.2017.087052
    [54]
    X. Ping, J. Yao, B. Ding, P. Wang, and Z. Li, “Tube-based output feedback robust MPC for LPV systems with scaled terminal constraint sets,” IEEE Trans. Cybernetics, vol. 52, no. 8, pp. 7563–7576, 2022.
    [55]
    B. Ding, X. Tang, and J. Hu, “A summary of dynamic output feedback robust MPC for linear polytopic uncertainty model with bounded disturbance,” Mathematical Problems in Engineering, Art. no. 3830724, 2020.
    [56]
    B. Ding and L. Xie, “Robust model predictive control via dynamic output feedback”, in Proc. IEEE 7th World Congr. Intelligent Control and Automation, Chongqing, China, pp. 3388–3393, 2008.
    [57]
    B. Ding, L. Xie, and F. Xue, “Improving robust model predictive control via dynamic output feedback,” in Proc. Chinese Control and Decision Conf., Guilin, China, pp. 2116–2121, 2009.
    [58]
    B. Ding, “Constrained robust model predictive control via parameter-dependent dynamic output feedback,” Automatica, vol. 46, no. 9, pp. 1517–1523, 2010. doi: 10.1016/j.automatica.2010.06.014
    [59]
    X. Ping, B. Ding, and C. Han, “Dynamic output feedback robust model predictive control,” Acta Automatica Sinica, vol. 38, no. 1, pp. 31–37, 2012. doi: 10.3724/SP.J.1004.2012.00031
    [60]
    X. Ping and N. Sun, “Dynamic output feedback robust model predictive control via zonotopic set-membership estimation for constrained quasi-LPV systems,” Journal of Applied Mathematics, Art. no. 875850, 2015.
    [61]
    B. Ding, X. Ping, and H. Pan, “On dynamic output feedback robust MPC for constrained quasi-LPV systems,” Int. Journal of Control, vol. 86, no. 12, pp. 2215–2227, 2013. doi: 10.1080/00207179.2013.809796
    [62]
    X. Ping and B. Ding, “Dynamic output feedback robust model predictive control based on ellipsoidal estimation error bound for quasi-LPV systems,” in Proc. Chinese Control and Decision Conf., Guiyang, China, pp. 726–731, 2013.
    [63]
    B. Ding, B Huang, and F. Xu, “Dynamic output feedback robust model predictive control,” Int. Journal of Systems Science, vol. 42, no. 10, pp. 1669–1682, 2011. doi: 10.1080/00207721003624543
    [64]
    J. Hu and B. Ding, “An off-line output feedback MPC strategy for nonlinear systems represented by quasi-LPV model,” IFAC-PapersOnLine, vol. 51, no. 20, pp. 66–71, 2018. doi: 10.1016/j.ifacol.2018.10.176
    [65]
    A. Alessandri, M. Baglietto, and G. Battistelli, “On estimation error bounds for receding-horizon filters using quadratic boundedness,” IEEE Trans. Automatic Control, vol. 49, no. 8, pp. 1350–1355, 2004. doi: 10.1109/TAC.2004.832652
    [66]
    A. Alexandria, M. Baglietto, and G. Battistelli, “Design of state estimators for uncertain linear systems using quadratic boundedness,” Automatica, vol. 42, no. 3, pp. 497–502, 2006. doi: 10.1016/j.automatica.2005.10.013
    [67]
    T. Alamo, J. M. Bravo, and E. F. Camacho, “Guaranteed state estimation by zonotopes,” Automatica, vol. 41, no. 6, pp. 1035–1043, 2005. doi: 10.1016/j.automatica.2004.12.008
    [68]
    B. Ding and H. Pan, “Dynamic output feedback-predictive control of a Takagi-Sugeno model with bounded disturbance,” IEEE Trans. Fuzzy Systems, vol. 25, no. 3, pp. 653–667, 2017. doi: 10.1109/TFUZZ.2016.2574907
    [69]
    K. Derinkuyu and M. C. Pınar, “On the S-procedure and some variants,” Mathematical Methods of Operations Research, vol. 64, no. 1, pp. 55–77, 2006. doi: 10.1007/s00186-006-0070-8
    [70]
    J. Hu and B. Ding, “A periodic approach to dynamic output feedback MPC for quasi-LPV model,” IEEE Trans. Automatic Control, vol. 66, no. 5, pp. 2257–2264, 2021. doi: 10.1109/TAC.2020.3002162
    [71]
    W. Jiang, H. Wang, J. Lu, G. Cai, and W. Qin, “Synchronization for chaotic systems via mixed-objective dynamic output feedback robust model predictive control,” Journal of the Franklin Institute, vol. 354, no. 12, pp. 4838–4860, 2017. doi: 10.1016/j.jfranklin.2017.05.007
    [72]
    B. Ding, “Dynamic output feedback MPC for LPV systems via near-optimal solutions,” in Proc. Chinese Control Conf., Yantai, China, pp. 3340–3345, 2011.
    [73]
    B. Ding, “Dynamic output feedback MPC for LPV systems via iterative optimization,” in Proc. Chinese Control and Decision Conf., Mianyang, China, pp. 3280–3285, 2011.
    [74]
    X. Ping and B. Ding, “Dynamic output feedback robust model predictive control based on ellipsoidal estimation error bound,” Acta Automatica Sinica, vol. 40, no. 2, pp. 219–226, 2014.
    [75]
    X. Ping, Z. Li, and A. Al-Ahmari, “Dynamic output feedback robust MPC for LPV systems subject to input saturation and bounded disturbance,” Int. Journal of Control Automation and Systems, vol. 15, no. 3, pp. 976–985, 2017. doi: 10.1007/s12555-016-0004-z
    [76]
    B. Ding and H. Pan, “Output feedback robust MPC for LPV system with polytopic model parametric uncertainty and bounded disturbance,” Int. Journal of Control, vol. 89, no. 8, pp. 1554–1571, 2016. doi: 10.1080/00207179.2016.1138144
    [77]
    B. Ding, Y. Xi, and X. Ping, “A comparative study on output feedback MPC for constrained LPV systems,” in Proc. Chinese Control Conf., Hefei, China, pp. 4189–4194, 2012.
    [78]
    B. Ding, Y. Xi, and X. Ping, “A general reformulation of output feedback MPC for constrained LPV systems,” in Proc. Chinese Control Conf., Hefei, China, pp. 4195–4200, 2012.
    [79]
    B. Ding, J. Hu, X. Tang, and J. Wang, “A synthesis approach to output feedback MPC for LPV model with bounded disturbance,” IEEE Access, vol. 8, pp. 228337–228348, 2020. doi: 10.1109/ACCESS.2020.3042654
    [80]
    J. Hu and B. Ding, “An efficient offline implementation for output feedback min-max MPC,” Int. Journal of Robust and Nonlinear Control, vol. 29, no. 2, pp. 492–506, 2019. doi: 10.1002/rnc.4401
    [81]
    B. Ding, J. Dong, and J. Hu, “Output feedback robust MPC using general polyhedral and ellipsoidal true state bounds for LPV model with bounded disturbance,” Int. Journal of Systems Science, vol. 50, no. 3, pp. 625–637, 2019. doi: 10.1080/00207721.2019.1567862
    [82]
    B. Ding, Y. Xi, X. Ping, and T. Zou, “Dynamic output feedback robust MPC with relaxed constraint handling for LPV system with bounded disturbance,” in Proc. 11th World Congr. Intelligent Control and Automation, Shenyang, China, pp. 2624–2629, 2014.
    [83]
    B. Ding and H. Pan, “Output feedback robust model predictive control with unmeasurable model parameters and bounded disturbance,” Chinese Journal of Chemical Engineering, vol. 24, no. 10, pp. 1431–1441, 2016. doi: 10.1016/j.cjche.2016.05.041
    [84]
    J. Hu and B. Ding, “Dynamic output feedback robust MPC with convex optimisation for system with polytopic uncertainty,” Int. Journal of Systems Science, vol. 50, no. 4, pp. 739–748, 2019. doi: 10.1080/00207721.2019.1568606
    [85]
    X. Ping, S. Yang, B. Ding, T. Raïssi, and Z. Li, “A convexity approach to dynamic output feedback robust MPC for LPV systems with bounded disturbances,” Int. Journal of Control,Automation and Systems, vol. 18, no. 6, pp. 1378–1391, 2020. doi: 10.1007/s12555-019-0089-2
    [86]
    D. Li and Y. Xi, “Robust model predictive control based on a category of dynamic output feedback,” in Proc. Asian Control Conf., Taiwan, China, pp. 994–999, 2011.
    [87]
    D. Li, Y. Xi, and F. Gao, “Synthesis of dynamic output feedback RMPC with saturated inputs,” Automatica, vol. 49, no. 4, pp. 949–954, 2013. doi: 10.1016/j.automatica.2013.01.010
    [88]
    X. Ping, P. Wang, and L. Hong, “An improved off-line dynamic output feedback robust MPC design for ellipsoidal estimation error sets,” in Proc. Chinese Control Conf., Chengdu, China, pp. 4265–4270, 2016.
    [89]
    X. Ping, N. Sun, and B. Ding, “An off-line approach to dynamic output feedback robust model predictive control with ellipsoidal estimation error set,” in Proc. Chinese Control and Decision Conf., Changsha, China, pp. 1078–1083, 2014.
    [90]
    B. Ding and L. Xie, “Dynamic output feedback robust model predictive control with guaranteed quadratic boundedness,” in Proc. 48th IEEE Conf. Decision and Control, Shanghai, China, pp. 8034–8039, 2009.
    [91]
    B. Ding, “New formulation of dynamic output feedback robust model predictive control with guaranteed quadratic boundedness,” Asian Journal of Control, vol. 15, no. 1, pp. 302–309, 2013. doi: 10.1002/asjc.496
    [92]
    X. Ping and B. Ding, “An off-line approach to dynamic output feedback robust model predictive control,” Acta Automatica Sinica, vol. 39, no. 6, pp. 790–798, 2013.
    [93]
    X. Ping and B. Ding, “Off-line approach to dynamic output feedback robust model predictive control,” Systems and Control Letters, vol. 62, no. 11, pp. 1038–1048, 2013. doi: 10.1016/j.sysconle.2013.07.011
    [94]
    B. Ding, C. Gao, and X. Ping, “Dynamic output feedback robust MPC using general polyhedral state bounds for the polytopic uncertain system with bounded disturbance,” Asian Journal of Control, vol. 18, no. 2, pp. 699–708, 2016. doi: 10.1002/asjc.1082
    [95]
    X. Ping and N. Sun, “Dynamic output feedback robust MPC via zonotope-based set-membership estimation for general LPV systems,” in Proc. Chinese Control Conf., Hangzhou, China, pp. 4095–4100. 2015.
    [96]
    B. Ding, Y. Xi, M. T. Cychowski, and T. O’Mahony, “A synthesis approach for output feedback robust constrained model predictive control,” Automatica, vol. 44, no. 1, pp. 258–264, 2008. doi: 10.1016/j.automatica.2007.04.005
    [97]
    L. El Ghaoui, F. Oustry, and M. AitRami, “A cone complementarity linearization algorithm for static output-feedback and related problems,” IEEE Trans. Automatic Control, vol. 42, no. 8, pp. 1171–1176, 1997. doi: 10.1109/9.618250
    [98]
    A. Bemporad and A. Garulli, “Output-feedback predictive control of constrained linear systems via set-membership state estimation,” Int. Journal of Control, vol. 73, no. 8, pp. 655–665, 2000. doi: 10.1080/002071700403420
    [99]
    T. Shi, Z. Wu, and H. Su, “Improved dynamic output feedback RMPC for linear uncertain systems with input constraints,” Int. Journal of Robust and Nonlinear Control, vol. 26, no. 12, pp. 2729–2742, 2016. doi: 10.1002/rnc.3484
    [100]
    J. R. Colombo Junior, R. K. H. Galvão, and E. Assuncao, “A new OFRMPC formulation with on-line synthesis of the dynamic output feedback controller,” Int. Journal of Robust and Nonlinear Control, vol. 27, no. 17, pp. 3921–3936, 2017.
    [101]
    H. Kheloufi, F. Bedouhene, A. Zemouche, and A. Alessandri, “Observer-based stabilisation of linear systems with parameter uncertainties by using enhanced LMI conditions,” Int. Journal of Control, vol. 88, no. 6, pp. 1189–1200, 2015. doi: 10.1080/00207179.2014.999258
    [102]
    J. Hu, “Dynamic output feedback MPC of polytopic uncertain systems: Efficient LMI conditions,” IEEE Trans. Circuits and Systems II: Express Briefs, vol. 68, no. 7, pp. 2568–2572, 2021. doi: 10.1109/TCSII.2021.3053113
    [103]
    J. Hu and B. Ding, “Off-line output feedback robust MPC with general polyhedral and ellipsoidal true state bound,” Journal of the Franklin Institute, vol. 357, no. 8, pp. 4505–4523, 2020. doi: 10.1016/j.jfranklin.2020.01.027
    [104]
    B. Ding, P. Wang, and J. Hu, “Dynamic output feedback robust MPC with one free control move for LPV model with bounded disturbance,” Asian Journal of Control, vol. 20, no. 2, pp. 755–767, 2018. doi: 10.1002/asjc.1617
    [105]
    X. Ping, “Output feedback robust MPC based on off-line observer for LPV systems via quadratic boundedness,” Asian Journal of Control, vol. 19, no. 4, pp. 1641–1653, 2017. doi: 10.1002/asjc.1469
    [106]
    W. Yang, J. Gao, G. Feng, and T. Zhang, “An optimal approach to output-feedback robust model predictive control of LPV systems with disturbances,” Int. Journal of Robust and Nonlinear Control, vol. 26, no. 15, pp. 3253–3273, 2016. doi: 10.1002/rnc.3505
    [107]
    X. Ping, P. Wang, and J. Zhang, “A multi-step output feedback robust MPC approach for LPV systems with bounded parameter changes and disturbance,” Int. Journal of Control Automation and Systems, vol. 16, no. 5, pp. 2157–2168, 2018. doi: 10.1007/s12555-017-0630-0
    [108]
    W. Jiang, H. Wang, J. Lu, W. Qin, and G. Cai, “Synthesis of mixed objective output feedback robust model predictive control,” Asian Journal of Control, vol. 19, no. 6, pp. 1977–1990, 2017. doi: 10.1002/asjc.1494
    [109]
    B. Ding and H. Pan, “Output feedback robust MPC with one free control move for the linear polytopic uncertain system with bounded disturbance,” Automatica, vol. 50, no. 11, pp. 2929–2935, 2014. doi: 10.1016/j.automatica.2014.10.021
    [110]
    Q. Qiu, F. Yang, Y. Zhu, and E. Mousavinejad, “Output feedback model predictive control based on set-membership state estimation,” IET Control Theory and Applications, vol. 14, no. 4, pp. 558–567, 2020. doi: 10.1049/iet-cta.2019.0881
    [111]
    M. H. Zibaeenejad and V. J. Majd, “An output feedback robust model predictive controller design based on quasi-min max algorithm,” in Proc. American Control Conf., Louis, USA, pp. 4874–4879, 2009.
    [112]
    J. Gao, W. Yang, and T. Zhang, “Output feedback model predictive control of linear parameter varying systems,” in Proc. ASME Int. Mechanical Engineering Congress and Exposition, vol. 46476, American Society of Mechanical Engineers, Montreal, Canada, 2014.
    [113]
    J. H. Park, T. H. Kim, and T. Sugie, “Output feedback model predictive control for LPV systems based on quasi-min-max algorithm,” Automatica, vol. 47, no. 9, pp. 2052–2058, 2011. doi: 10.1016/j.automatica.2011.06.015
    [114]
    T. H. Kim and H. W. Lee, “Quasi-min-max output-feedback model predictive control for LPV systems with input saturation,” Int. Journal of Control Automation and Systems, vol. 15, no. 3, pp. 1069–1076, 2017. doi: 10.1007/s12555-016-0378-y
    [115]
    X. Ping, S. Yang, B. Ding, Raïssi Tarek, and Z. Li, “Observer-based output feedback robust MPC via zonotopic set-membership state estimation for LPV systems with bounded disturbances and noises,” Journal of the Franklin Institute, vol. 357, no. 11, pp. 7368–7398, 2020. doi: 10.1016/j.jfranklin.2020.05.014
    [116]
    X. Ping, S. Yang, P. Wang, and Z. Li, “An observer-based output feedback robust MPC approach for constrained LPV systems with bounded disturbance and noise,” Int. Journal of Robust and Nonlinear Control, vol. 30, no. 4, pp. 1512–1533, 2020. doi: 10.1002/rnc.4836
    [117]
    X. Ping, X. Wang, T. Lin, B. Ding, and A. Polyakov, “An off-line approach for output feedback robust model predictive control,” Journal of the Franklin Institute, vol. 358, no. 17, pp. 9263–9287, 2021. doi: 10.1016/j.jfranklin.2021.09.006
    [118]
    X. Ping, S. Yang, Y. Xiao, B. Ding, and Z. Li, “Interval state estimation-based robust model predictive control for linear parameter varying systems,” Int. Journal of Robust and Nonlinear Control, vol. 31, no. 15, pp. 7026–7052, 2021. doi: 10.1002/rnc.5676
    [119]
    Y. Su and K. Tan, “Comments on output feedback model predictive control for LPV systems based on quasi-min-max algorithm,” Automatica, vol. 48, no. 9, pp. 2385–2385, 2012. doi: 10.1016/j.automatica.2012.06.100
    [120]
    S. Liu, Y. Song, G. Wei, and X. Huang, “RMPC-based security problem for polytopic uncertain system subject to deception attacks and persistent disturbances,” IET Control Theory and Applications, vol. 11, no. 10, pp. 1611–1618, 2017. doi: 10.1049/iet-cta.2017.0153
    [121]
    J. Wang, B. Ding, and J. Hu, “Security control for LPV system with deception attacks via model predictive control: A dynamic output feedback approach,” IEEE Trans. Automatic Control, vol. 66, no. 2, pp. 760–767, 2021. doi: 10.1109/TAC.2020.2984221
    [122]
    Z. Wan and M. V. Kothare, “Robust output feedback model predictive control using off-line linear matrix inequalities,” Journal of Process Control, vol. 12, no. 7, pp. 763–774, 2002. doi: 10.1016/S0959-1524(02)00003-3
    [123]
    M. H. Asemani, V. J. Majd, and M. Z. Nejad, “An improved off-line approach for output feedback robust model predictive control,” IFAC Proceedings Volumes, vol. 41, no. 2, pp. 10886–10891, 2008. doi: 10.3182/20080706-5-KR-1001.01844
    [124]
    B. Ding and T. Zou, “Synthesizing output feedback predictive control for constrained uncertain time-varying discrete systems,” Acta Automatica Sinica, vol. 33, no. 1, pp. 78–83, 2007. doi: 10.1360/aas-007-0078
    [125]
    S. Liu, Y. Song, G. Wei, D. Ding, and Y. Liu, “Event-triggered dynamic output feedback RMPC for polytopic systems with redundant channels: Input-to-state stability,” Journal of the Franklin Institute, vol. 354, no. 7, pp. 2871–2892, 2017. doi: 10.1016/j.jfranklin.2017.02.008
    [126]
    J. S. Lim, S. Y. Son, and Y. I. Lee, “Receding horizon output feedback control for constrained uncertain systems using periodic invariance,” Int. Journal of Control, vol. 83, no. 6, pp. 1277–1286, 2010. doi: 10.1080/00207171003682689
    [127]
    K. Zhu, Y. Song, S. Zhang, and Z. Zhong, “Non-fragile observer-based output feedback control for polytopic uncertain system under distributed model predictive control approach,” Int. Journal of Systems Science, vol. 48, no. 9, pp. 1891–1901, 2017. doi: 10.1080/00207721.2017.1295329
    [128]
    R. Tóth, H. S. Abbas, and H. Werner, “On the state-space realization of LPV input-output models: Practical approaches,” IEEE Trans. Control Systems Technology, vol. 20, no. 1, pp. 139–153, 2012.
    [129]
    B. Ding, “Output feedback robust MPC based-on direct input-output model,” in Proc. Chinese Control and Decision Conf., Taiyuan, China, pp. 86–91, 2012.
    [130]
    B. Ding and T. Zou, “A synthesis approach for output feedback robust model predictive control based-on input-output model,” Journal of Process Control, vol. 24, no. 3, pp. 60–72, 2014. doi: 10.1016/j.jprocont.2013.12.006
    [131]
    X. Ping and B. Qian, “Output feedback robust MPC subject to input saturation based on input-output model,” in Proc. Chinese Control and Decision Conf., Yinchuan, China, pp. 741–745, 2016.
    [132]
    T. Shi, R. Lu, and Q. Lv, “Robust static output feedback infinite horizon RMPC for linear uncertain systems,” Journal of the Franklin Institute, vol. 353, no. 4, pp. 891–902, 2016. doi: 10.1016/j.jfranklin.2016.01.012
    [133]
    S. M. Lee and J. H. Park, “Output feedback model predictive control for LPV systems using parameter-dependent Lyapunov function,” Applied Mathematics and Computation, vol. 190, no. 1, pp. 671–676, 2007. doi: 10.1016/j.amc.2007.01.061
    [134]
    Q. Cheng, D. Muñoz-Carpintero, M. Cannon, and B. Kouvaritakis, “Efficient robust output feedback MPC,” in Proc. Chinese Control Conf., Xi’an China, pp. 4149–4154, 2013.
    [135]
    A. D. R. de Souza, D. Efimov, and T. Raïssi, “Robust output feedback MPC for LPV systems using interval observers,” IEEE Trans. Automatic Control, vol. 67, no. 6, pp. 3188–3195, 2022.
    [136]
    A. D. R. de Souza, D. Efimov, T. Raïssi, and X. Ping, “Robust output feedback model predictive control for constrained linear systems via interval observers,” Automatica, vol.135, Art. no. 109951, 2022.
    [137]
    A. R. de Souza, D. Efimov, T. Raïssi, and X. Ping, “Robust output feedback MPC: An interval-observer approach,” in Proc. IEEE Conf. Decision and Control, Jeju Island, Korea, pp. 2529–2534, 2020.
    [138]
    D. Rotondo, V. Puig, F. Nejjari, and M. Witczak, “Automated generation and comparison of Takagi-Sugeno and polytopic quasi-LPV models,” Fuzzy Sets and Systems, vol. 277, pp. 44–64, 2015. doi: 10.1016/j.fss.2015.02.002
    [139]
    H. Li, L. Wu, H. K. Lam, and Y. Gao, Analysis and Synthesis for Interval Type-2 Fuzzy-Model-Based Systems. Singapore: Springer, 2016.
    [140]
    A. Casavola, D. Famularo, and G. Franzè, “Norm-bounded robust MPC strategies for constrained control of nonlinear systems,” IEE Proceedings-Control Theory and Applications, vol. 152, no. 3, pp. 285–295, 2005. doi: 10.1049/ip-cta:20041307
    [141]
    B. Ding, “Dynamic output feedback predictive control for nonlinear systems represented by a Takagi-Sugeno model,” IEEE Trans. Fuzzy Systems, vol. 19, no. 5, pp. 831–843, 2011. doi: 10.1109/TFUZZ.2011.2147320
    [142]
    B. Ding and X. Ping, “Output feedback predictive control with one free control move for nonlinear systems represented by a Takagi-Sugeno model,” IEEE Trans. Fuzzy Systems, vol. 22, no. 2, pp. 249–263, 2014. doi: 10.1109/TFUZZ.2013.2251637
    [143]
    X. Tang, L. Deng, N. Liu, S. Yang, and J. Yu, “Observer-based output feedback MPC for T-S fuzzy system with data loss and bounded disturbance,” IEEE Trans. Cybernetics, vol. 49, no. 6, pp. 2119–2132, 2019. doi: 10.1109/TCYB.2018.2820138
    [144]
    J. Hu and B. Ding, “Dynamic output feedback predictive control with one free control move for the Takagi-Sugeno model with bounded disturbance,” IEEE Trans. Fuzzy Systems, vol. 27, no. 3, pp. 462–473, 2019. doi: 10.1109/TFUZZ.2018.2859905
    [145]
    X. Ping, J. Yao, B. Ding, P. Wang, and Z. Li, “Time-varying tube-based output feedback robust MPC for T-S fuzzy systems,” IEEE Trans. Fuzzy Systems, vol. 30, no. 5, pp. 1460–1474, 2022.
    [146]
    X. Tang and L. Deng, “Multi-step output feedback predictive control for uncertain discrete-time T-S fuzzy system via event-triggered scheme,” Automatica, vol. 107, pp. 362–370, 2019. doi: 10.1016/j.automatica.2019.05.057
    [147]
    J. Hu and B. Ding, “Output feedback model predictive control with steady-state target calculation for fuzzy systems,” IEEE Trans. Fuzzy Systems, vol. 28, no. 12, pp. 3442–3449, 2020. doi: 10.1109/TFUZZ.2019.2950885
    [148]
    X. Tang, L. Deng, J. Yu, and H. Qu, “Output feedback predictive control of interval type-2 T-S fuzzy systems with Markovian packet loss,” IEEE Trans. Fuzzy Systems, vol. 26, no. 4, pp. 2450–2459, 2018. doi: 10.1109/TFUZZ.2017.2771502
    [149]
    F. Liu, C. Wang, and Q. Geng, “Observer-based MPC for NCS with actuator saturation and DoS attacks via interval type-2 T-S fuzzy model,” IET Control Theory &Applications, vol. 14, no. 20, pp. 3537–3546, 2020.
    [150]
    X. Tang, L. Dend, J. Yu, and H. Qu, “Output feedback model predictive control for interval type-2 T-S fuzzy networked control systems,” Acta Automatica Sinica, vol. 45, no. 3, pp. 604–616, 2019.
    [151]
    X. Tang, L. Deng, and H. Qu, “Predictive control for networked interval type-2 T-S fuzzy system via an event-triggered dynamic output feedback scheme,” IEEE Trans. Fuzzy Systems, vol. 27, no. 8, pp. 1573–1586, 2019. doi: 10.1109/TFUZZ.2018.2883370
    [152]
    X. Ping and W. Pedrycz, “Output feedback model predictive control of interval type-2 T-S fuzzy system with bounded disturbance,” IEEE Trans. Fuzzy Systems, vol. 28, no. 1, pp. 148–162, 2020. doi: 10.1109/TFUZZ.2019.2900844
    [153]
    D. Famularo and G. Franzè, “Output feedback model predictive control of uncertain norm-bounded linear systems,” Int. Journal of Robust Nonlinear Control, vol. 21, no. 8, pp. 838–862, 2011. doi: 10.1002/rnc.1629
    [154]
    G. Franzè, M. Mattei, L. Ollio, and V. Scordamaglia, “A robust constrained model predictive control scheme for norm-bounded uncertain systems with partial state measurements,” Int. Journal of Robust and Nonlinear Control, vol. 29, no. 17, pp. 6105–6125, 2019. doi: 10.1002/rnc.4721
    [155]
    J. Yang, B. Ding, and H. Pan, “Output feedback robust MPC for time-varying system with norm-bounded model parametric uncertainty and disturbance,” in Proc. Chinese Control Conf., Chengdu, China, pp. 4330–4335, 2016.
    [156]
    J. Yang, Y. Cai, and B. Ding, “Robust output feedback model predictive control for systems with norm-bounded uncertainty: An LMI approach,” IEEE Access, vol. 7, pp. 183869–183876, 2019. doi: 10.1109/ACCESS.2019.2955380
    [157]
    J. Hu and B. Ding, “Output feedback robust MPC for linear systems with norm-bounded model uncertainty and disturbance,” Automatica, vol. 108, Art. no. 108489, 2019.
    [158]
    J. Hu, B. Ding, Y. Wang, J. Zhao, Z. Xu, T. Zou, and X. Ping, “An effcient iterative approach for dynamic output feedback robust model predictive control,” in Proc. Asian Control Conf., Kitakyushu, Japan, pp. 1283–1288, 2019.
    [159]
    J. Hu and B. Ding, “Output feedback robust MPC of uncertain norm-bounded linear systems with disturbance,” Int. Journal of Control, vol. 94, no. 9, pp. 2388–2395, 2021. doi: 10.1080/00207179.2019.1707290
    [160]
    A. Sala and C. Ariño, “Asymptotically necessary and sufficient conditions for stability and performance in fuzzy control: Applications of Polya’s theorem,” Fuzzy Sets and Systems, vol. 158, no. 24, pp. 2671–2686, 2007. doi: 10.1016/j.fss.2007.06.016
    [161]
    A. A. Kurzhanskiy and P. Varaiya, “Ellipsoidal toolbox (ET),” in Proc. IEEE Conf. Decision and Control, San Diego, USA, pp. 1498–1503, 2006.
    [162]
    X. Chang, Z. Li, and J. H. Park, “Fuzzy generalized H2 filtering for nonlinear discrete-time systems with measurement quantization,” IEEE Trans. Systems,Man,and Cybernetics: Systems, vol. 48, no. 12, pp. 2419–2430, 2018. doi: 10.1109/TSMC.2017.2743012
    [163]
    J. Hu and B. Ding, “One-step ahead robust MPC for LPV model with bounded disturbance,” European Journal of Control, vol. 52, pp. 59–66, 2020. doi: 10.1016/j.ejcon.2019.09.004
    [164]
    P. Gahinet, A. Nemirovski, A. J. Laub, and M. Chilali, LMI Control Toolbox for Use With MATLAB, User’s Guide. The Math Works Inc, Natick, MA, USA, 1995.
    [165]
    J. C. Geromel, P. Colaneri, and P. Bolzern, “Differential linear matrix inequality in optimal sampled-data control,” Automatica, vol. 100, pp. 289–298, 2019. doi: 10.1016/j.automatica.2018.11.021
    [166]
    F. A. C. C. Fontes, “Discontinuous feedbacks, discontinuous optimal controls, and continuous-time model predictive control,” Int. Journal of Robust and Nonlinear Control, vol. 13, no. 3–4, pp. 191–209, 2003. doi: 10.1002/rnc.813
    [167]
    J. C. Geromel, “Sampled-data model predictive control,” IEEE Trans. Automatic Control, vol. 67, no. 5, pp. 2466–2472, 2022.
    [168]
    W. Esterhuizen, K. Worthmann, and S. Streif, “Recursive feasibility of continuous-time model predictive control without stabilising constraints,” IEEE Control Systems Letters, vol. 5, no. 1, pp. 265–270, 2021. doi: 10.1109/LCSYS.2020.3001514
    [169]
    T. W. Nguyen, I. V. Kolmanovsky, and D. S. Bernstein, “Sampled-data output-feedback model predictive control of nonlinear plants using online linear system identification,” in Proc. American Control Conf., New Orleans, USA, pp. 4692–4697, 2021.
    [170]
    A. H. K. Palmeira, J. M. G. da Silva Jr, J. V. Flores, and A. Seuret, “Aperiodic sampled-data MPC strategy for LPV systems,” Journal of the Franklin Institute, vol. 359, no. 2, pp. 786–815, 2022. doi: 10.1016/j.jfranklin.2021.03.031
    [171]
    S. Mate, H. Kodamana, S. Bhartiya, and P. S. V. Nataraj, “A stabilizing sub-optimal model predictive control for quasi-linear parameter varying systems,” IEEE Control Systems Letters, vol. 4, no. 2, pp. 402–407, 2020. doi: 10.1109/LCSYS.2019.2937921
    [172]
    T. Shi and H. Su, “Sampled-data MPC for LPV systems with input saturation,” IET Control Theory &Applications, vol. 8, no. 17, pp. 1781–1788, 2014.
    [173]
    M. Kögel and R. Findeisen, “Sampled-data, output feedback predictive control of uncertain, nonlinear systems,” IFAC-PapersOnLine, vol. 49, no. 18, pp. 47–52, 2016. doi: 10.1016/j.ifacol.2016.10.138
    [174]
    D. Mayne, “Robust and stochastic model predictive control: Are we going in the right direction?” Annual Reviews in Control, vol. 41, pp. 184–192, 2016. doi: 10.1016/j.arcontrol.2016.04.006
    [175]
    A. Mesbah, “Stochastic model predictive control: An overview and perspectives for future research,” IEEE Control Systems Magazine, vol. 36, no. 6, pp. 30–44, 2016. doi: 10.1109/MCS.2016.2602087
    [176]
    G. C. Calafiore and L. Fagiano, “Stochastic model predictive control of LPV systems via scenario optimization,” Automatica, vol. 49, no. 6, pp. 1861–1866, 2013. doi: 10.1016/j.automatica.2013.02.060
    [177]
    S. Chitraganti, R. Tóth, N. Meskin, and J. Mohammadpour, “Stochastic model predictive control for LPV systems,” in Proc. American Control Conf., Seattle, USA, pp. 5654–5659, 2017.
    [178]
    M. Cannon, Q. Cheng, B. Kouvaritakis, and S. V. Raković, “Stochastic tube MPC with state estimation,” Automatica, vol. 48, no. 3, pp. 536–541, 2012. doi: 10.1016/j.automatica.2011.08.058
    [179]
    M. Farina, L. Giulioni, L. Magni, and R. Scattolini, “An approach to output-feedback MPC of stochastic linear discrete-time systems,” Automatica, vol. 55, pp. 140–149, 2015. doi: 10.1016/j.automatica.2015.02.039
    [180]
    C. Mark and S. Liu, “A stochastic output-feedback MPC scheme for distributed systems,” in Proc. American Control Conf., Denver, USA, pp. 1937–1942, 2020.
    [181]
    J. Hanema, Anticipative Model Predictive Control for Linear Parameter-Varying Systems. Ph.D. dissertation, Technische Universiteit Eindhoven, Eindhoven, The Netherlands, 2018.
    [182]
    J. Hanema, R. Tóth, and M. Lazar, “Tube-based anticipative model predictive control for linear parameter-varying systems,” in Proc. IEEE Conf. Decision and Control, Las Vegas, USA, pp. 1458–1463, 2016.
    [183]
    T. Peschke and D. Görges, “Tube-based anticipative robust MPC for systems with multiplicative uncertainty,” IFAC-PapersOnLine, vol. 53, no. 2, pp. 7091–7096, 2020. doi: 10.1016/j.ifacol.2020.12.463
    [184]
    Z. Hou and Z. Wang, “From model-based control to data-driven control: Survey, classification and perspective,” Information Sciences, vol. 235, pp. 3–35, 2013. doi: 10.1016/j.ins.2012.07.014
    [185]
    C. De Persis and P. Tesi, “Formulas for data-driven control: Stabilization, optimality, and robustness,” IEEE Trans. Automatic Control, vol. 65, no. 3, pp. 909–924, 2020. doi: 10.1109/TAC.2019.2959924
    [186]
    J. Coulson, J. Lygeros, and F. Dörfler, “Data-enabled predictive control: In the shallows of the DeePC,” in Proc. European Control Conf., Naples, Italy, pp. 307–312, 2019.
    [187]
    J. Berberich, J. Köhler, M. A. Müller, and F. Allgöwer, “Data-driven model predictive control with stability and robustness guarantees,” IEEE Trans. Automatic Control, vol. 66, no. 4, pp. 1702–1717, 2021. doi: 10.1109/TAC.2020.3000182
    [188]
    D. Gidon, H. S. Abbas, A. D. Bonzanini, D. B. Graves, J. M. Velni, and A. Mesbah, “Data-driven LPV model predictive control of a cold atmospheric plasma jet for biomaterials processing,” Control Engineering Practice, vol. 109, Art. no. 104725, 2021.
    [189]
    P. S. G. Cisneros, A. Datar, P. Göttsch, and H. Werner, “Data-driven quasi-LPV model predictive control using Koopman operator techniques,” IFAC-PapersOnLine, vol. 53, no. 2, pp. 6062–6068, 2020. doi: 10.1016/j.ifacol.2020.12.1676
    [190]
    C. Verhoek, H. S. Abbas, R. Tóth, and S. Haesaert, “Data-driven predictive control for linear parameter-varying systems,” IFAC-PapersOnLine, vol. 54, no. 8, pp. 101–108, 2021. doi: 10.1016/j.ifacol.2021.08.588
    [191]
    K. Zhang and Y. Shi, “Adaptive model predictive control for a class of constrained linear systems with parametric uncertainties,” Automatica, vol. 117, Art. no. 108974, 2020.
    [192]
    X. Lu and M. Cannon, “Robust adaptive tube model predictive control,” in Proc. American Control Conf., Philadelphia, USA, pp. 3695–3701, 2019.
    [193]
    J. Köhler, E. Andina, R. Soloperto, M. A. Müller, and F. Allgöwer, “Linear robust adaptive model predictive control: Computational complexity and conservatism,” in Proc. IEEE Conf. Decision and Control, Nice, France, pp. 1383–1388, 2019.
    [194]
    M. Lorenzen, M. Cannon, and F. Allgöwer, “Robust MPC with recursive model update,” Automatica, vol. 103, pp. 461–471, 2019. doi: 10.1016/j.automatica.2019.02.023
    [195]
    J. Köhler, P. Kötting, R. Soloperto, F. Allgöwer, and M. Müller, “A robust adaptive model predictive control framework for nonlinear uncertain systems,” Int. Journal of Robust and Nonlinear Control, vol. 31, no. 18, pp. 8725–8749, 2021. doi: 10.1002/rnc.5147

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    Highlights

    • Different classifications of output feedback robust model predictive control approaches for linear parameter varying systems and the related uncertain systems are summarized and compared
    • The methods of dealing with system uncertainties and physical constraints in different classifications of output feedback robust model predictive control optimizations for linear parameter varying systems are given
    • Key issues on output feedback robust model predictive control optimizations for linear parameter varying systems are discussed
    • Future research directions on output feedback robust model predictive control for linear parameter varying systems are suggested

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