Skip to main content

Advertisement

Log in

Robust nonlinear fractional order fuzzy PD plus fuzzy I controller applied to robotic manipulator

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The aim of this article is to utilize fractional calculus for performance enhancement of nonlinear fuzzy PD + I controller. A fractional order fuzzy PD + I controller (FOFPD + I) is designed and implemented to control complex, uncertain and nonlinear robotic manipulator. FOFPD + I controller is derived from fractional order PD and fractional order I controller. The proposed control strategy has an adaptive capability due to its nonlinear gains and preserves the linear structure of fractional order PD + I controller. Further, integer-order fuzzy PD + I controller (FPD + I) and conventional PID controllers are also designed for comparative analysis. The optimum parameter values of FOFPD + I, FPD + I and PID controllers are obtained using non-dominated sorting genetic algorithm-II. The effectiveness of proposed controller is examined for reference tracking and disturbance rejection problems of robotic manipulator. The designed controllers are also validated experimentally on DC servomotor. Simulation and experimental results prove the superiority of FOFPD + I controller as compared to its integer-order equivalent and conventional PID controllers for control of robotic manipulator.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  1. Åström KJ, Hang CC, Persson P, Ho WK (1992) Towards intelligent PID control. Automatica 28:1–9

    Article  Google Scholar 

  2. Bennett S (1993) Development of the PID controller. IEEE Control Syst 13:58–62

    Google Scholar 

  3. Åström KJ, Hägglund T (2001) The future of PID control. Control Eng Pract 9:1163–1175

    Article  Google Scholar 

  4. Åström KJ, Hägglund T (2006) Advanced PID control. ISA—the instrumentation, systems, and automation society research Triangle park

  5. Mohan V, Chhabra H, Rani A, Singh V (2018) Robust self-tuning fractional order PID controller dedicated to non-linear dynamic system. J Intell Fuzzy Syst 34:1467–1478

    Article  Google Scholar 

  6. Chen G (1996) Conventional and fuzzy PID controllers: an overview. Int J Intell Control Syst 1:235–246

    Article  MathSciNet  Google Scholar 

  7. Mohan V, Rani A, Singh V (2017) Robust adaptive fuzzy controller applied to double inverted pendulum. J Intell Fuzzy Syst 32:3669–3687

    Article  Google Scholar 

  8. Lim CM, Hiyama T (1991) Application of fuzzy logic control to a manipulator. IEEE Trans Robot Autom 7:688–691

    Article  Google Scholar 

  9. Yoo BK, Ham WC (2000) Adaptive control of robot manipulator using fuzzy compensator. IEEE Trans Fuzzy Syst 8:186–199

    Article  Google Scholar 

  10. Sooraksa P, Chen G (1998) Mathematical modeling and fuzzy control of a flexible-link robot arm. Math Comput Model 27:73–93

    Article  MATH  Google Scholar 

  11. Li W, Chang X, Wahl FM, Farrell J (2001) Tracking control of a manipulator under uncertainty by FUZZY P + ID controller. Fuzzy Sets Syst 122:125–137

    Article  MathSciNet  MATH  Google Scholar 

  12. Tang W, Chen G, Lu R (2001) A modified fuzzy PI controller for a flexible-joint robot arm with uncertainties. Fuzzy Sets Syst 118:109–119

    Article  MathSciNet  Google Scholar 

  13. Malki HA, Li H, Chen G (1994) New design and stability analysis of fuzzy proportional-derivative control systems. IEEE Trans Fuzzy Syst 2:245–254

    Article  Google Scholar 

  14. Malki HA, Misir D, Feigenspan D, Chen G (1997) Fuzzy PID control of a flexible-joint robot arm with uncertainties from time-varying loads. IEEE Trans Control Syst Technol 5:371–378

    Article  Google Scholar 

  15. Misir D, Malki HA, Chen G (1996) Design and analysis of a fuzzy proportional-integral-derivative controller. Fuzzy Sets Syst 79:297–314

    Article  MathSciNet  MATH  Google Scholar 

  16. Dumlu A, Erenturk K (2014) Trajectory tracking control for a 3-DOF parallel manipulator using fractional-order control. IEEE Trans Ind Electron 61:3417–3426

    Article  Google Scholar 

  17. Valerio D, da Costa JS (2012) An introduction to fractional control, vol 91. Institution of Engineering and Technology (IET), England

  18. Pan I, Das S (2013) Frequency domain design of fractional order PID controller for AVR system using chaotic multi-objective optimization. Int J Electr Power Energy Syst 51:106–118

    Article  Google Scholar 

  19. Das S, Pan I, Das S, Gupta A (2012) A novel fractional order fuzzy PID controller and its optimal time domain tuning based on integral performance indices. Eng Appl Artif Intell 25:430–442

    Article  Google Scholar 

  20. Chhabra H, Mohan V, Rani A, Singh V (2016) Multi Objective PSO tuned fractional order PID control of robotic manipulator. In: The international symposium on intelligent systems technologies and applications, pp 567–572

  21. Bingül Z, Karahan O (2012) Fractional PID controllers tuned by evolutionary algorithms for robot trajectory control. Turk J Electr Eng Comput Sci 20:1123–1136

    Google Scholar 

  22. Monje CA, Vinagre BM, Feliu V, Chen Y (2008) Tuning and auto-tuning of fractional order controllers for industry applications. Control Eng Pract 16:798–812

    Article  Google Scholar 

  23. Gaing Z-L (2004) A particle swarm optimization approach for optimum design of PID controller in AVR system. IEEE Trans Energy Convers 19:384–391

    Article  Google Scholar 

  24. Wang P, Kwok D (1994) Optimal design of PID process controllers based on genetic algorithms. Control Eng Pract 2:641–648

    Article  Google Scholar 

  25. Vasan A, Raju KS (2009) Comparative analysis of simulated annealing, simulated quenching and genetic algorithms for optimal reservoir operation. Appl Soft Comput 9:274–281

    Article  Google Scholar 

  26. Pan I, Das S (2016) Fractional order fuzzy control of hybrid power system with renewable generation using chaotic PSO. ISA Trans 62:19–29

    Article  Google Scholar 

  27. Mishra P, Kumar V, Rana K (2015) A fractional order fuzzy PID controller for binary distillation column control. Expert Syst Appl 42:8533–8549

    Article  Google Scholar 

  28. Jesus IS, Barbosa RS (2015) Genetic optimization of fuzzy fractional PD + I controllers. ISA Trans 57:220–230

    Article  Google Scholar 

  29. Pan I, Korre A, Das S, Durucan S (2012) Chaos suppression in a fractional order financial system using intelligent regrouping PSO based fractional fuzzy control policy in the presence of fractional Gaussian noise. Nonlinear Dyn 70:2445–2461

    Article  MathSciNet  Google Scholar 

  30. Das S, Pan I, Das S (2013) Performance comparison of optimal fractional order hybrid fuzzy PID controllers for handling oscillatory fractional order processes with dead time. ISA Trans 52:550–566

    Article  Google Scholar 

  31. Mohan V, Chhabra H, Rani A, Singh V (2018) An expert 2DOF fractional order fuzzy PID controller for nonlinear systems. Neural Comput Appl. https://doi.org/10.1007/s00521-017-3330-z

    Article  Google Scholar 

  32. Ying H, Siler W, Buckley JJ (1990) Fuzzy control theory: a nonlinear case. Automatica 26:513–520

    Article  MathSciNet  MATH  Google Scholar 

  33. Craig JJ (2005) Introduction to robotics: mechanics and control, vol 3. Pearson Prentice Hall, Upper Saddle River

    Google Scholar 

  34. Ayala HVH, dos Santos Coelho L (2012) Tuning of PID controller based on a multiobjective genetic algorithm applied to a robotic manipulator. Expert Syst Appl 39:8968–8974

    Article  Google Scholar 

  35. Goodrich C, Peterson AC (2015) Discrete fractional calculus. Springer, Berlin

    Book  MATH  Google Scholar 

  36. Kumar V, Rana K (2017) Nonlinear adaptive fractional order fuzzy PID control of a 2-link planar rigid manipulator with payload. J Frankl Inst 354:993–1022

    Article  MathSciNet  MATH  Google Scholar 

  37. Lubich C (1986) Discretized fractional calculus. SIAM J Math Anal 17:704–719

    Article  MathSciNet  MATH  Google Scholar 

  38. Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353

    Article  MATH  Google Scholar 

  39. Podlubny I (1998) Fractional differential equations: an introduction to fractional derivatives, fractional differential equations, to methods of their solution and some of their applications, vol 198. Academic Press, New York

    MATH  Google Scholar 

  40. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6:182–197

    Article  Google Scholar 

  41. Pachauri N, Singh V, Rani A (2017) Two degree of freedom PID based inferential control of continuous bioreactor for ethanol production. ISA Trans 68:235–250

    Article  Google Scholar 

  42. Mirjalili S, Jangir P, Saremi S (2017) Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems. Appl Intell 46:79–95

    Article  Google Scholar 

  43. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1:67–82

    Article  Google Scholar 

  44. Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27:495–513

    Article  Google Scholar 

  45. Jain M, Singh V, Rani A (2018) A novel nature-inspired algorithm for optimization: Squirrel search algorithm. Swarm Evol Comput 44:148–175

    Article  Google Scholar 

  46. Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27:1053–1073

    Article  Google Scholar 

  47. Mirjalili S, Jangir P, Mirjalili SZ, Saremi S, Trivedi IN (2017) Optimization of problems with multiple objectives using the multi-verse optimization algorithm. Knowl-Based Syst 134:50–71

    Article  Google Scholar 

  48. Russell RW, May ML, Soltesz KL, Fitzpatrick JW (1998) Massive swarm migrations of dragonflies (Odonata) in eastern North America. Am Midl Nat 140:325–342

    Article  Google Scholar 

  49. Wikelski M, Moskowitz D, Adelman JS, Cochran J, Wilcove DS, May ML (2006) Simple rules guide dragonfly migration. Biol Lett 2:325–329

    Article  Google Scholar 

  50. De Wit CC, Praly L (2000) Adaptive eccentricity compensation. IEEE Trans Control Syst Technol 8:757–766

    Article  Google Scholar 

  51. Åström KJ, Hägglund T (1995) PID controllers: theory, design, and tuning, vol 2. Instrument society of America Research, Triangle Park

    Google Scholar 

  52. Saleem O, Omer U (2017) EKF-based self-regulation of an adaptive nonlinear PI speed controller for a DC motor. Turk J Electr Eng Comput Sci 25:4131–4141

    Article  Google Scholar 

  53. Quanser Engineering Trainer for NI-ELVIS (2009) QNET Interactive learning Guide, Quanser Inc.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Vijay Mohan.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

\(\tau_{1}\)

Torque for link-1

\(T\)

Sampling time

\(o.p\)

Output positive

\(\tau_{2}\)

Torque for link-2

\(K_{\text{P}}\)

Proportional gain for FOFPD controller

\(o.n\)

Output negative

\(\theta_{1}\)

Actuator angle measured from \(X - a\)x is to AB

\(K_{\text{D}}\)

Derivative gain for FOFPD controller

\(o.z\)

Output zero

\(\theta_{2}\)

Actuator angle measured from extended line of \(AB\) to the line \(BC\)

\(K_{\text{I}}\)

Integral gain for FOFI controller

\(w_{1}\)

Objective-1

\(l_{1}\)

Length of link-1, \(AB\)

\(\grave{u}_{\text{PD}} \left( {nT} \right)\)

FOFPD controller output

\(w_{2}\)

Objective-2

\(l_{2}\)

Length of link-2, \(BC\)

\(\Delta u_{\text{I}} \left( {nT} \right)\)

FOFI controller output

\(R_{\text{T}}\)

Reference trajectory

\(m_{1}\)

Mass of link-1

\(D^{\lambda }\)

Fractional operator

\(K_{{u{\text{PD}}}}\)

Output gain for FOFPD controller

\(m_{2}\)

Mass of link-2

\(r.p\)

Fractional rate of error positive

\(K_{\text{uI}}\)

Output gain for FOFI controller

\(K_{\text{p}}^{\text{PD}}\)

Proportional gain for conventional FOPD controller

\(r.n\)

Fractional rate of error negative

\(\lambda\)

Fractional order

\(K_{\text{D}}^{\text{PD}}\)

Derivative gain for conventional FOPD controller

\(e.p\)

Error positive

\(g\)

Acceleration due to gravity

\(K_{\text{I}}^{\text{I}}\)

Integral gain for conventional FOI controller

\(e.n\)

Error negative

  

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chhabra, H., Mohan, V., Rani, A. et al. Robust nonlinear fractional order fuzzy PD plus fuzzy I controller applied to robotic manipulator. Neural Comput & Applic 32, 2055–2079 (2020). https://doi.org/10.1007/s00521-019-04074-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-019-04074-3

Keywords

Navigation