PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Powiadomienia systemowe
  • Sesja wygasła!
Tytuł artykułu

Design of robust multi-loop PI controller for improved disturbance rejection with constraint on minimum singular value

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Disturbance rejection performance optimization with constraints on robustness for a multivariable process is commonly encountered in industrial control applications. This paper presents the tuning of a multi-loop Proportional Integral (PI) controller method to enhance the performance of load disturbance rejection using evolutionary optimization. The proposed design methodology is formulated to minimize the load disturbance rejection response and the input control energy under the constraints of robust stability. The minimum singular value of multiplicative uncertainty is considered a multi-loop system robust stability indicator. Optimization is performed to achieve the same, or higher level than the most-explored Direct Synthesis (DS) based multi-loop PI controller, which is derived from a conventional criterion. Simulation analysis clearly proved that the proposed multi-loop PI controller tuning method gives better disturbance rejection, and either, the same or a higher level of robust stability when compared to the DS-based multi-loop PI controller.
Rocznik
Strony
839--859
Opis fizyczny
Bibliogr. 25 poz., rys., tab., wzory
Twórcy
  • Department of Electrical and Electronics Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai, India
  • Department of Electrical and Electronics Engineering, P.S.R Engineering College, Sivakasi, India
  • Deparment of Electronics and Communication Engineering, Surya Engineering College, India
autor
  • Department of Electrical and Electronics Engineering, Jyothi Engineering College, Thrissur, India
  • Department of Electrical and Electronics Engineering, National Engineering College, Kovilpatti, India
Bibliografia
  • [1] K. Bingi, R. Ibrahim, M.N. Karsiti, S.M. Hassan and V.R. Harindran: A comparative study of 2dof PID and 2dof fractional order PID controllers on a class of unstable systems. Archives of Control Sciences, 28(4), (2018), 635-682. DOI: 10.24425/acs.2018.125487.
  • [2] J. Cvejn: The magnitude optimum design of the PI controller for plants with complex roots and dead time. Archives of Control Sciences, 32(1), (2022), 5-35. DOI: 10.24425/acs.2022.140862.
  • [3] B. Zhou: Multi-variable adaptive high-order sliding mode quasi-optimal control with adjustable convergence rate for unmanned helicopters subject to parametric and external uncertainties. Nonlinear Dynamics, 108(4), (2022), 3671-3692. DOI: 10.1007/s11071-022-07433-3.
  • [4] R. Dittmar, S. Gill, H. Singh and M. Darby: Robust optimization-based multiloop PID controller tuning: A new tool and its industrial application. Control Engineering Practice, 20(4), (2012) 355-370. DOI: 10.1016/j.conengprac.2011.10.011.
  • [5] S. Cha, D. Chun and J. Lee: Two-step IMC- PID method for multiloop control system design. Industrial and Engineering Chemistry Research, 41(12), (2002), 3037-3041. DOI: 10.1021/ie0102347.
  • [6] M. Lee, K. Lee, C. Kim and J. Lee: Analytical design of multiloop PID controllers for desired closed-loop responses. AIChE Journal, 50(7), (2004), 1631-1635. DOI: 10.1002/aic.10166.
  • [7] S. Khandelwal and K.P. Detroja: The optimal detuning approach based centralized control design for MIMO processes. Journal of Process Control, 96 (2020), 23-36. DOI: 10.1016/j.jprocont.2020.10.006.
  • [8] T.N.L. Vu, J.T. Lee and M.Y. Lee: Design of multi-loop PID controllers based on the generalized IMC-PID method with mp criterion. International Journal of Control, Automation, and Systems, 5(2), (2007), 212-217.
  • [9] W.D. Chang: A multi-crossover genetic approach to multivariable PID controllers tuning. Expert Systems with Applications, 33(3), (2007), 620-626. DOI: 10.1016/j.eswa.2006.06.003.
  • [10] J. Zhang, J. Zhuang, H. Du and S. Wang: Self-organizing genetic algorithm based tuning of PID controllers. Information Sciences, 179(7), (2009), 1007-1018. DOI: 10.1016/j.ins.2008.11.038.
  • [11] A. Lari, A. Khosravi and F. Rajabi: Controller design based on 𝜇 analysis and PSO algorithm. ISA Transactions, 53(2), (2014), 517-523. DOI: 10.1016/j.isatra.2013.11.006.
  • [12] M.I. Menhas, L. Wang, M. Fei and H. Pan: Comparative performance analysis of various binary coded PSO algorithms in multivariable PID controller design. Expert Systems with Applications, 39(4), (2012), 4390-4401. DOI: 10.1016/j.eswa.2011.09.152.
  • [13] A. Djoewahir, K. Tanaka and S. Nakashima: Adaptive PSO-based self-tuning PID controller for ultrasonic motor. International Journal of Innovative Computing, Information and Control, 9(10), (2013), 3903-3914.
  • [14] L. dos Santos Coelhoand and M.W. Pessoa: A tuning strategy for multiariable PI and PID controllers using differential evolution combined with chaotic Zaslavskii map. Expert Systems with Applications, 38(11), (2011), 13694-13701. DOI: 10.1016/j.eswa.2011.04.156.
  • [15] D. Davendra, I. Zelinka and R. Senkerik: Chaos driven evolutionary algorithms for the task of PID control. Computers and Mathematics with Applications, 60(4), (2010), 1088-1104. DOI: 10.1016/j.camwa.2010.03.066.
  • [16] A. Belkadi, H. Oulhadj, Y. Touati, S.A. Khan and B. Daachi: On the robust PID adaptive controller for exoskeletons: A particle swarm optimization based approach. Applied Soft Computing, 60 (2017), 87-100. DOI: 10.1016/j.asoc.2017.06.012.
  • [17] Y. Ye, C.-B. Yin, Y. Gong and J.-J. Zhou: Position control of non-linear hydraulic system using an improved PSO based PID controller. Mechanical Systems and Signal Processing, 83 (2017), 241-259. DOI: 10.1016/j.ymssp.2016.06.010.
  • [18] A. Souza and L. Souza: Design of a controller for a rigid-flexible satellite using the ℎ-infinity method considering the parametric uncertainty. Mechanical Systems and Signal Processing, 116 (2019), 641-650. DOI: 10.1016/j.ymssp.2018.07.002.
  • [19] S.J. Ho, S.Y. Ho and L.S. Shu: OSA: orthogonal simulated annealing algorithm and its application to designing mixed H2/H1 optimal controllers. IEEE Transactions on Systems, Man, and Cybernetics, 34 (2004), 588-600. DOI: 10.1109/TSMCA.2004.832834.
  • [20] K.J. Astrom, H. Panagopoulos and T. Hagglund: Design of PI controllers based on non-convex optimization. Automatica, 34(5), (1998), 585-601. DOI: 10.1016/s0005-1098(98)00011-9.
  • [21] G. Reynoso-Meza, J. Sanchis, X. Blasco and J.M. Herrero: Multiobjective evolutionary algorithms for multivariable PI controller design. Expert Systems with Applications, 39(9), (2012), 7895-7907. DOI: 10.1016/j.eswa.2012.01.111.
  • [22] G. Reynoso-Meza, J. Sanchis, X. Blasco and R.Z. Freire: Evolutionary multi objective optimization with preferences for multivariable PI controller tuning. Expert Systems with Applications, 51 (2016), 120-133. DOI: 10.1016/j.eswa.2015.11.028.
  • [23] W.L. Luyben: Simple method for tuning SISO controllers in multivariable systems. Industrial and Engineering Chemistry Process Design and Development, 25(3), (1986), 654-660. DOI: 10.1021/i200034a010.
  • [24] V.V. Kumar, V. Rao and M. Chidambaram: Centralized PI controllers for interacting multivariable processes by synthesis method. ISA Transactions, 51(3), (2012), 400-409. DOI: 10.1016/j.isatra.2012.02.001.
  • [25] T. Ngujen, T.N.L. Vu and M. Lee: Independent design of multi-loop PI/PID controllers for interacting multivariable processes. Journal of Process Control, 20(8), (2010), 922-933.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6c9e00e0-f385-44a5-949d-91d93286b263
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.