Anti-windup Control of Nonlinear Cascade Systems with Particle Swarm Optimization Parameter Tuning

  • Fernando Serrano
  • Josep M. RossellEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 591)


Assuming that many physical models can be decoupled, an anti-windup control scheme for nonlinear cascade systems is proposed. Taking into account that saturation appears frequently, in order to overcome this difficulty, an efficient control approach is developed. The paper is divided into two parts; First, the design of a cascade control system with dynamic controllers in the inner and outer loops, considering the closed-loop stability in the controller design with a suitable anti-windup compensator; Secondly, a PID cascade controller design in the inner and outer loop is presented, when the parameter tuning in both control schemes is done by particle swarm optimization (PSO). However, in this case, the implementation of an anti-windup compensator is not needed. Apart from the theoretical background, two numerical examples are shown to corroborate the provided results.


  1. 1.
    Mehdi N, Rehan M, Malik FM, Bhatti AI, Tufail M (2014) A novel anti-windup framework for cascade control systems: an application to underactuated mechanical systems. ISA Trans 53(3):802–815CrossRefGoogle Scholar
  2. 2.
    Nguyen A, Dequidt A, Dambrine M (2015) Anti-windup based dynamic output feedback controller design with performance consideration for constrained Takagi Sugeno systems. Eng Appl Artif Intell 40:76–83CrossRefGoogle Scholar
  3. 3.
    Dong J, Yang G-H (2015) Reliable state feedback control of T– S fuzzy systems with sensor faults. IEEE Trans Fuzzy Syst 23(2):421–433MathSciNetCrossRefGoogle Scholar
  4. 4.
    Gao X, Komada S, Hori T (1999) A wind-up restraint control of disturbance observer system for saturation of actuator torque. In: IEEE international conference on systems, man, and cybernetics, vol 1, pp 84–88Google Scholar
  5. 5.
    Silva JD, Tarbouriech S (2003) Anti-windup design with guaranteed regions of stability: an LMI based approach. In: Proceedings of the 42nd IEEE conference on decision and control, vol 5, pp 4451–4456Google Scholar
  6. 6.
    Folcher JP (2004) LMI based anti-windup control for an underwater robot with propellers saturations. In: Proceedings of the IEEE international conference on control applications, vol 1, pp 32–37Google Scholar
  7. 7.
    Oliveira MZ, Da Silva JMG, Coutinho D, Tarbouriech S (2011) Anti-windup design for a class of multivariable nonlinear control systems: an LMI based approach. In: 50th IEEE conference on decision and control and european control conference, pp 4797–4802Google Scholar
  8. 8.
    Zhai D, An L, Li J, Zhang Q (2016) Fault detection for stochastic parameter-varying Markovian jump systems with application to networked control systems. Appl Math Model 40(3):2368–2383MathSciNetCrossRefGoogle Scholar
  9. 9.
    Zhai D, An L, Li J, Zhang Q (2016) Adaptive fuzzy fault-tolerant control with guaranteed tracking performance for nonlinear strict-feedback systems. Fuzzy Set Syst 302:80–100MathSciNetCrossRefGoogle Scholar
  10. 10.
    Zhai D, Lu A-Y, Li J-H, Zhang Q-L (2016) Simultaneous fault detection and control for switched linear systems with mode-dependent average dwell-time. Appl Math Comput 273:767–792MathSciNetzbMATHGoogle Scholar
  11. 11.
    Haddad W, Chellaboina V (2008) Nonlinear dynamical systems and control: a Lyapunov based approach. Princeton Press, PrincetonGoogle Scholar
  12. 12.
    Gao H, Xu W (2011) Particle swarm algorithm with hybrid mutation strategy. Appl Soft Comput 11(8):5129–5142CrossRefGoogle Scholar
  13. 13.
    Menhas MI, Wang L, Fei M, Pan H (2012) Comparative performance analysis of various binary coded PSO algorithms in multi-variable PID controller design. Exp Syst Appl 39(4):4390–4401CrossRefGoogle Scholar
  14. 14.
    Wang L, Fu X, Mao Y, Menhas MI, Fei M (2012) A novel modified binary differential evolution algorithm and its applications. Neurocomputing 98:55–75CrossRefGoogle Scholar
  15. 15.
    Wang Y, Li B, Weise T, Wang J, Yuan B, Tian Q (2011) Self-adaptive learning based particle swarm optimization. Inf Sci 181(20):4515–4538MathSciNetCrossRefGoogle Scholar
  16. 16.
    Kanamori M (2012) Anti-windup adaptive law for Euler-Lagrange systems with actuator saturation. IFAC Proc 45(22):875–880CrossRefGoogle Scholar
  17. 17.
    Oliveira MZ, Gomes da Silva JM, Coutinho DF, Tarbouriech S (2011) Anti-windup design for a class of nonlinear control systems. IFAC Proc Vol 44(1):13432–13437CrossRefGoogle Scholar
  18. 18.
    Serrano FE, Flores MA (2015) C ++ library for fuzzy type-2 controller design with particle swarm optimization tuning. In: IEEE CONCAPAN 2015. Tegucigalpa, HondurasGoogle Scholar
  19. 19.
    Tahoun AH (2017) Anti-windup adaptive PID control design for a class of uncertain chaotic systems with input saturation. ISA Trans 66:176–184CrossRefGoogle Scholar
  20. 20.
    Huang CQ, Peng XF, Wang JP (2008) Robust nonlinear PID controllers for anti-windup design of robot manipulators with an uncertain Jacobian matrix. Acta Autom Sin 34(9):1113–1121MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Central American Technical University (UNITEC)TegucigalpaHonduras
  2. 2.Department of MathematicsUniv. Politècnica de Catalunya (UPC)ManresaSpain

Personalised recommendations