dc.relation.references | BORELLI F., B. A.; MORARI, M. Predictive control for linear and hybrid systems. 1st. ed. New York: Cambridge University Press, 2017.
CAMACHO, E.; BORDONS, C. Model Predictive Control. 1st. ed. Great Britain: Springer, 1999.
CHEN, C. T. Linear system theory and design. 3rd. ed. New York: Oxford University Press, 1999.
CHISCI, L.; ROSSITER, J.; ZAPPA, G. Systems with persistent disturbances: predictive control with restricted constraints. Automatica, v. 37, n. 7, p. 1019–1028, 2001. ISSN 0005-1098. Disponível em: <https://www.sciencedirect.com/science/article/pii/S0005109801000516>.
CLARKE, D.; MOHTADI, C.; TUFFS, P. Generalized predictive control—part i. the basic algorithm. Automatica, v. 23, n. 2, p. 137–148, 1987. ISSN 0005-1098.
CUNHA, V. M.; SANTOS, T. L. M. Robust nonlinear model predictive control based on nominal predictions with piecewise constant references and bounded disturbances. International Journal of Robust and Nonlinear Control, v. 32, n. 6, p. 3944–3968, 2022.
CUTLER, C. R.; RAMAKER, B. L. Dynamic matrix control??a computer control algorithm. Joint Automatic Control Conference, v. 17, p. 72, 1980.
DWORAK P., G. J. A. S.; GHOSH, S. Effective use of MPC for dynamic decoupling of MIMO systems. Elektronika ir Elektrotechnika, v. 25, n. 2, p. 3–8, 2019.
FALB, P.; WOLOVICH, W. A. Decoupling in the design and synthesis of multivariable control systems. [S.l.], 1967.
FERRAMOSCA, A. et al. Mpc for tracking with optimal closed-loop performance. Automatica, v. 45, n. 8, p. 1975–1978, 2009. ISSN 0005-1098. Disponível em:<https://www.sciencedirect.com/science/article/pii/S000510980900212X>.
FERRAMOSCA, A. et al. Mpc for tracking zone regions. Journal of Process Control - J PROCESS CONTROL, v. 20, p. 506–516, 04 2010.
FERRAMOSCA, A. et al. Robust mpc for tracking zone regions based on nominal predictions. Journal of Process Control, v. 22, n. 10, p. 1966–1974, 2012. ISSN 0959-1524. Disponível em: <https://www.sciencedirect.com/science/article/pii/S0959152412002077>.
GAGNON, E.; POMERLEAU, A.; DESBIENS, A. Simplified, ideal or inverted decoupling? ISA Transactions, v. 37, p. 265–276, 1998.
GAGNON E., P. A.; DESBIENS, A. Simplified, ideal ou inverted decoupling? ISA Transactions, v. 37, 1998. GARCIA-GABIN, W.; CAMACHO, E. Application of multivariable GPC to a four tank process with unstable transmission zeros. In: Proceedings of the 2002 International Conference on Applications. [S.l.: s.n.], 2002.
GARCIA-GABIN, W.; CAMACHO, E. F. Application of multivariable gpc to a four tank process with unstable transmission zeros. In: IEEE. Proceedings of the International Conference on Control Applications. [S.l.], 2002. v. 2, p. 645–650.
GILBERT, E.; TAN, K. Linear systems with state and control constraints: the theory and application of maximal output admissible sets. IEEE Transactions on Automatic Control, v. 36, n. 9, p. 1008–1020, 1991.
GUPTA, Y. Control of integrating processes using dynamic matrix control. Chemical Engineering Research and Design, v. 76, n. 4, p. 465–470, 1998. ISSN 0263-8762. Process Operations and Control.
HERCEG, M. et al. Multi-Parametric Toolbox 3.0. In: Proc. of the European Control Conference. Zürich, Switzerland: [s.n.], 2013. p. 502–510. <http://control.ee.ethz.ch/~mpt>.
JOHANSSON, K. The quadruple-tank process: a multivariable laboratory process with an adjustable zero. IEEE Transaction on Control System Technology, v. 8, n. 3, p. 456–465, 2000.
JOHANSSON, K. The quadruple-tank process: a multivariable laboratory process with an adjustable zero. IEEE Transaction on Control System Technology, v. 8, n. 3, p. 456–465, 2000.
KOLMANOVSKY, I.; GILBERT, E. G. Theory and computation of disturbance invariant sets for discrete-time linear systems. Mathematical problems in engineering, Hindawi, v. 4, n. 4, p. 317–367, 1998.
KOUVARITAKIS, B.; CANNON, M. Model predictive control: Classical, robust and stochastic. [S.l.]: Springer, 2015.
LIMA, C.; SANTOS, T. Estudo sobre a redução do acoplamento em estratégias de controle preditivo baseado em modelo. In: XI Simpósio Brasileiro de Automação Inteligente. [S.l.: s.n.], 2013. p. Artigo 7701.
LIMON, D. et al. Mpc for tracking of piece-wise constant references for constrained linear systems. IFAC Proceedings Volumes, v. 38, n. 1, p. 135–140, 2005. ISSN 1474-6670. 16th IFAC World Congress. Disponível em: <https://www.sciencedirect.com/science/article/pii/S1474667016368951>.
LIMON, D. et al. Mpc for tracking piecewise constant references for constrained linear systems. Automatica, v. 44, n. 9, p. 2382–2387, 2008. ISSN 0005-1098. Disponível em:
<https://www.sciencedirect.com/science/article/pii/S0005109808001106>.
LIMON, D. et al. On the design of robust tube-based MPC for tracking. IFAC Proceedings Volumes, v. 41, n. 2, p. 15333–15338, 2008. ISSN 1474-6670. 17th IFAC World Congress.
LIMÓN, D. et al. Robust tube-based MPC for tracking of constrained linear systems with additive disturbances. Journal of Process Control, Elsevier, v. 20, n. 3, p. 248–260, 2010.
LIU L., T. S. X. D. Z. T. C. Y.; ZHANG, S. A review of industrial MIMO decoupling control. International Journal of Control, Automation and Systems, v. 17, 2019.
MACIEJOWSKI, J. Predictive Control with Constraints. 1st. ed. .: Prentice Hall, 2001.
MAYNE, D. Robust and stochastic mpc: Are we going in the right direction? IFAC-PapersOnLine, v. 48, n. 23, p. 1–8, 2015. ISSN 2405-8963. 5th IFAC Conference on Nonlinear Model Predictive Control NMPC 2015. Disponível em: <https://www.sciencedirect.com/science/article/pii/S2405896315025392>.
MAYNE, D. et al. Robust output feedback model predictive control of constrained linear systems. Automatica, v. 42, n. 7, p. 1217–1222, 2006. ISSN 0005-1098. Disponível em: <https://www.sciencedirect.com/science/article/pii/S0005109806001221>.
MAYNE, D.; SERON, M.; RAKOVIć, S. Robust model predictive control of constrained linear systems with bounded disturbances. Automatica, v. 41, n. 2, p. 219–224, 2005. ISSN 0005-1098. Disponível em: <https://www.sciencedirect.com/science/article/pii/S0005109804002870>.
MAYNE D.Q., R. J. R. C.; SCOKAERT, P. Constrained model predictive control: Stability and optimality. Automatica, v. 36, n. 6, p. 789–814, 2000.
MIDDLETON, R.; ADAMS, G. Modification of model predictive control to reduce cross-coupling. In: 17th World Congress The International Federation of Automatic Control. [S.l.]: IFAC, 2008. p. 9940–9945.
ORDYS, A. W.; CLARKE, D. W. A state-space description for gpc controllers. International Journal of Systems Science, v. 24, p. 1727–1744, 1993. Disponível em: <https://api.semanticscholar.org/CorpusID:121709281>.
PAULSON, J. A.; SANTOS, T. L.; MESBAH, A. Mixed stochastic-deterministic tube MPC for offset-free tracking in the presence of plant-model mismatch. Journal of process control, Elsevier, v. 83, p. 102–120, 2019.
PEREIRA, G. da F.; FLESCH, R. C. C. Desacoplamento de sistemas multivariáveis não lineares com mpc usando função custo dinâmica. In: Congresso Brasileiro de Automática-CBA. [S.l.: s.n.], 2020. v. 2, n. 1.
PEREIRA, G. F.; FLESCH, R. Desacoplamento de sistemas multivariáveis não lineares com MPC usando função custo dinâmica. In: Anais do Congresso Brasileiro de Automática. [S.l.]: SBA, 2020.
QIN, J.; BADGWELL, T. An overview of industrial model predictive control technology. AIChE Symposium Series, v. 93, 01 1997.
QIN, X.; ZHU, K.; CHAI, T. Robust adaptive decoupling design for generalized predictive control with neural network. In: Proceedings of 35th IEEE Conference on Decision and Control. [S.l.: s.n.], 1996. v. 3, p. 2426–2431.
RAKOVIć, S. V. et al. Homothetic tube model predictive control. Automatica, v. 48, n. 8, p. 1631–1638, 2012. ISSN 0005-1098. Disponível em: <https: //www.sciencedirect.com/science/article/pii/S0005109812001768>.
RICHALET, J. et al. Algorithmic control of industrial processes. p. 1119–1167, 1976.
SANIYE, A.; SULEYMAN, K. Decoupling constrained model predictive control of multi-component packed distillation column. World Applied Sciences Journal, v. 13, n. 3, p. 517–530, 2011.
SANTOS, T. Contribuições para o Controle Preditivo com Compensação de Atraso Robusta. Tese (Doutorado) — Universidade Federal de Santa Catarina, 2011.
SANTOS, T.; CUNHA, V. Robust mpc for linear systems with bounded disturbances based on admissible equilibria sets. International Journal of Robust and Nonlinear Control, v. 31, n. 7, p. 2690–2711, 2021.
SANTOS, T. L.; CUNHA, V. M. Tractable robust mpc for tracking piecewise constant references based on nominal predictions. Journal of Process Control, v. 129, p. 103030, 2023. ISSN 0959-1524. Disponível em: <https://www.sciencedirect.com/science/article/pii/S0959152423001178>.
SANTOS, T. L. M. Modified reference tracking mpc for constrained linear systems. International Journal of Systems Science, Taylor & Francis, v. 49, n. 8, p. 1674–1684, 2018. Disponível em: <https://doi.org/10.1080/00207721.2018.1478014>.
SCHMITZ U., H. R. A. F.; BARS, R. Decoupling predictive control by error dependent tuning of the weighting factors. ATP Journal PLUS, 2007.
SHINSKEY, F. Process Control System: application, design and tuning. 4. ed. New York: McGraw-Hill, 1996.
SKOGESTAD, S.; POSTLETHWAITE, I. Multivariable Feedback Control: Analysis and Design. 2. ed. [S.l.]: Wiley, 2005.
STRASSBERGER D., M. P.; SERGIYENKO, O. A decoupled MPC for motion control in robotino using a geometric approach. Journal of Physics: Conference Series, v. 659, 2015.
TôRRES, L. A. B. Teoria de Estabilidade de Lyapunov. 2019. Março, 2019. Disponível em: <http://www.cpdee.ufmg.br/~torres/wp-content/uploads/2018/02/Estabilidade_Lyapunov.pdf>. Acesso em 5 de Maio de 2024.
WANG, Q.-G. Decoupling Control. 1st. ed. German: Springer, 2003.
WANG, Q.-G.; HUANG, B.; GUO, X. Auto-tuning of tito decoupling controllers from step tests. ISA Transactions, v. 39, n. 4, p. 407–418, 2000. ISSN 0019-0578. Disponível em: <https://www.sciencedirect.com/science/article/pii/S0019057800000288>.
ZABET, K.; HABER, R. Improvement of the decoupling effect of the predictive controllers GPC and PFC by parameter adaptation. In: 18th International Conference on Process Control. [S.l.]: Slovak University of Technology in Bratislava, 2011. p. 419–426.
ZERMANI M.A., F. E.; MAMI, A. Decoupling multivariable GPC with reference observation and feedforward compensation method. case study: Neonate incubator. International Journal of Computer Science, v. 9, n. 3, p. 508–515, 2012. ZERMANI M.A., F. E.; MAMI, A. Self-tuning weighting factor to decoupling control for incubator system. International Journal of Information Technology, Control and Automation, v. 2, n. 3, p. 67–83, 2012.
ZHANG, J. Improved nonminimal state space model predictive control for multivariable processes using a non-zero-pole decoupling formulation. Industrial Engineering & Chemistry Research, v. 52, n. 3, p. 4874?4880, 2013.
ZHANG, R.; GAO, F. An improved decoupling structure based state space MPC design with improved performance. Systems & Control Letters, v. 75, p. 77–83, 2015. ISSN 0167-6911.
ZHU, K.; QIN, X.; CHAI, T. A new decoupling design of self-tuning multivariable generalized predictive control. International Journal of Adaptive Control and Signal Processing, v. 13, n. 3, p. 183–196, 1999. | pt_BR |