Soft Computing

, Volume 21, Issue 3, pp 571–584 | Cite as

ANFIS and MPC controllers for a reconfigurable lower limb exoskeleton



The exoskeletons are robotic active orthoses intended to enhance power or in medical applications as rehabilitation and assistive walking. In the context of designing a controller for the reconfigurable exoskeleton for lower limb, it is critical to define the hardware as well as the control. The scope of this work was to define the controller for reconfigurable exoskeleton using three types of controllers: PD, ANFIS, and MPC. The PD controllers are the typical approach for torque/tracking control while artificial intelligence controller, as ANFIS, and optimal controller, as MPC, are recently entering to this field. The ANFIS and MPC controllers may bring more precision and capability to distribute the processing operations. Afterward, this work contrasts the performance evaluation using objective indices to evaluate the error, ISE, IAE, ITSE, ITAE, ISTSE, and ISTAE. The results suggest that ANFIS and MPC controllers have the potential to drive the torque/traction reducing the error while having the capability to learn from the disturbances from the surroundings.


ANFIS Model predictive controller Exoskeleton Integral errors Artificial intelligence 


  1. Al Mashhadany YI (2012) Design and simulation of anfis controller for virtual-reality-built manipulator. In: Iqbal SD (ed) Fuzzy controllers: recent advances in theory and applications. InTech Open Access Publisher, Crotia, pp 315–334. doi:10.5772/48383 Google Scholar
  2. Anam K, Al-Jumaily AA (2012) Active exoskeleton control systems: state of the art. Proc Eng 41(Iris):988–994. doi:10.1016/j.proeng.2012.07.273 CrossRefGoogle Scholar
  3. Boscariol P, Gasparetto A, Zanotto V (2010) Model predictive control of a flexible links mechanism. J Intell Robot Syst 58(2):125–147. doi:10.1007/s10846-009-9347-5 CrossRefMATHGoogle Scholar
  4. Campo AB (2012) PID control design. In: Katsikis VN (ed) MATLAB: a fundamental tool for scientific computing and engineering applications, vol 1. doi:10.5772/48497
  5. Cardenas S, Castillo O, Aguilar LT, Rodriguez A (2009) Genetic design of biped walking fuzzy logic controller. In: 2009 IEEE workshop on hybrid intelligent models and applications, HIMA 2009—Proceedings vol 1, pp 7–12. doi:10.1109/HIMA.2009.4937818
  6. Cardenas-Maciel SL, Castillo O, Aguilar LT, Castro JR (2010) A T–S fuzzy logic controller for biped robot walking based on adaptive network fuzzy inference system. In: The 2010 international joint conference on neural networks, pp 1–8. doi:10.1109/IJCNN.2010.5596653
  7. Cardenas-Maciel SL, Castillo O, Aguilar LT (2011) Generation of walking periodic motions for a biped robot via genetic algorithms. Appl Soft Comput J 11(8):5306–5314. doi:10.1016/j.asoc.2011.05.030 CrossRefGoogle Scholar
  8. Castano JA, Hernandez A, Li Z, Tsagarakis NG, Caldwell DG, De Keyser R (2015) Enhancing the robustness of the EPSAC predictive control using a Singular Value Decomposition approach. Robot Auton Syst 74:283–295. doi:10.1016/j.robot.2015.09.001 CrossRefGoogle Scholar
  9. Castro SDJ, Lugo E, Cruz PP, Molina A (2013) Assistive robotic exoskeleton for helping limb girdle muscular dystrophy. In: 2013 international conference on mechatronics, electronics and automotive engineering, pp 27–32. doi:10.1109/ICMEAE.2013.9
  10. Dahari M, Bhuiyan MSH, Choudhury IA (2015) Development of a control system for artificially rehabilitated limbs? A review. Biol Cybern 109(2):141–162. doi:10.1007/s00422-014-0635-1 CrossRefGoogle Scholar
  11. Duarte-Mermoud MA, Prieto RA (2004) Performance index for quality response of dynamical systems. ISA Trans 43(1):133–151. doi:10.1016/s0019-0578(07)60026-3 CrossRefGoogle Scholar
  12. Grizzle JW, Chevallereau C, Sinnet RW, Ames AD (2014) Models, feedback control, and open problems of 3D bipedal robotic walking. Automatica 50(8):1955–1988. doi:10.1016/j.automatica.2014.04.021 MathSciNetCrossRefMATHGoogle Scholar
  13. Jiménez-Fabián R, Verlinden O (2012) Review of control algorithms for robotic ankle systems in lower-limb orthoses, prostheses, and exoskeletons. Med Eng Phys 34(4):397–408. doi:10.1016/j.medengphy.2011.11.018 CrossRefGoogle Scholar
  14. John CT, Anderson FC, Higginson JS, Delp SL (2012) Stabilisation of walking by intrinsic muscle properties revealed in a three-dimensional muscle-driven simulation. Comput Methods Biomech Biomed Eng. doi:10.1080/10255842.2011.627560 Google Scholar
  15. Koceska N, Koceski S, Durante F, Beomonte P, Raparelli T (2013) Control architecture of a 10 DOF lower limbs exoskeleton for gait rehabilitation. Int J Adv Robot Syst. doi:10.5772/55032 Google Scholar
  16. Kusagur A, Kodad S, Sankar Ram B (2010) Modeling, design simulation of an adaptive neuro-fuzzy inference system (ANFIS) for speed control of induction motor. Int J Comput Appl (0975–8887) 6(12):29–44Google Scholar
  17. Pan D, Gao F, Miao Y, Cao R (2015) Co-simulation research of a novel exoskeleton–human robot system on humanoid gaits with fuzzy-PID/PID algorithms. Adv Eng Softw 79:36–46. doi:10.1016/j.advengsoft.2014.09.005 CrossRefGoogle Scholar
  18. Premkumar K, Manikandan BV (2015) Fuzzy PID supervised online ANFIS based speed controller for brushless dc motor. Neurocomputing 157:76–90. doi:10.1016/j.neucom.2015.01.032 CrossRefGoogle Scholar
  19. Rodriguez CA, Lugo E, Ponce P, Molina A (2015) Towards a reconfigurable inferior limbs exoskeleton for assistive, rehabilitation, and empowering application. In: 15th IFAC symposium on information control problems in manufacturing, vol 15, pp 1541–1546Google Scholar
  20. Sinnet RW, Ames AD (2012) Bio-inspired feedback control of three-dimensional humanlike bipedal robots. J Robot Mechatron 24(4):595–601CrossRefGoogle Scholar
  21. Tucker MR, Olivier J, Pagel A, Bleuler H, Bouri M, Lambercy O, Gassert R (2015) Control strategies for active lower extremity prosthetics and orthotics: a review. J Neuroeng Rehabil 12:1. doi:10.1186/1743-0003-12-1 CrossRefGoogle Scholar
  22. Wang M, Luo J, Walter U (2015) A non-linear model predictive controller with obstacle avoidance for a space robot. Adv Space Res 57(8):1737–1746. doi:10.1016/j.asr.2015.06.012 CrossRefGoogle Scholar
  23. Wang X, Li X, Wang J, Fang X, Zhu X (2016) Data-driven model-free adaptive sliding mode control for the multi degree-of-freedom robotic exoskeleton. Inf Sci 327:246–257. doi:10.1016/j.ins.2015.08.025 MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Carlos A. Rodriguez
    • 1
  • Pedro Ponce
    • 1
  • Arturo Molina
    • 1
  1. 1.Tecnologico de MonterreyMexicoMexico

Personalised recommendations