Skip to main content
Log in

Flatness of musculoskeletal systems under functional electrical stimulation

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

Control of musculoskeletal yy system through functional electrical stimulation (FES) still remains a complex and a challenging process. Indeed, the used musculoskeletal models are often complex and highly nonlinear, which makes their control and inversion (getting appropriate inputs from a desired outputs) very difficult. On the other hand, the system flatness has been proved to be an efficient method for nonlinear system control, since in this technique, the nonlinear system can be controlled more easily through its flat outputs. Therefore, it is very promising to apply this control technique on the musculoskeletal system, to overcome its problems, which has never been explored so far. The aim of this work is to explore the flatness technique and its feasibility on the knee joint musculoskeletal system in dynamic condition, controlled by electrically stimulated quadriceps muscle. A mathematical proof developed in the current work highlights that the two-input musculoskeletal system is flat, where two flat outputs are the muscle stiffness and the knee joint angle. It also shows that the single-input musculoskeletal system is not flat. These results are crucial for flatness-based control of musculoskeletal systems, since this model in literature deals with a single input. Simulation results in open-loop control of two-input system highlight the consistency of the mathematical proof, and the applicability of this technique on the musculoskeletal system, where its simulated outputs fit perfectly with the desired ones if the model is considered perfect. When, one parameter of the system is not well estimated (10% of error), simulations show limits of open-loop control, with a joint angle rms deviation of 4%; hence, the closed-loop control should be considered.

Flatness Study and control of Musculoskeletal systems.

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

Similar content being viewed by others

References

  1. Abdallah MB, Ayadi M, Rotella F, Benrejeb M (2014) Ltv controller flatness-based design for mimo systems. Int J Dynamics Control 2:335–345

    Article  Google Scholar 

  2. Benoussaad M, Mombaur K, Azevedo-Coste C (2013) Nonlinear model predictive control of joint ankle by electrical stimulation for drop foot correction. In: 2013 IEEE/RSJ International conference on intelligent robots and systems. IEEE, pp 983–989. https://doi.org/10.1109/IROS.2013.6696470

  3. Benoussaad M, Poignet P, Guiraud D (2008) Optimal functional electrical stimulation patterns synthesis for knee joint control. In: 2008 IEEE/RSJ International conference on intelligent robots and systems. pp 2386–2391. https://doi.org/10.1109/IROS.2008.4651112

  4. Benoussaad M, Poignet P, Hayashibe M, Azevedo-Coste C, Fattal C, Guiraud D (2013) Experimental parameter identification of a multi-scale musculoskeletal model controlled by electrical stimulation: application to patients with spinal cord injury. Med Biol Eng Comp 51:617–31. https://doi.org/10.1007/s11517-013-1032-y

    Article  Google Scholar 

  5. Benoussaad M, Poignet P, Hayashibe M, Azevedo-Coste C, Fattal C, Guiraud D (2014) Synthesis of optimal electrical stimulation patterns for functional motion restoration: applied to spinal cord-injured patients. Med Biol Eng Comp 53:227–240. https://doi.org/10.1007/s11517-014-1227-x

    Article  Google Scholar 

  6. Chen G, Ma L, Song R, Li L, Wang X, Tong K (2018) Speed-adaptive control of functional electrical stimulation for dropfoot correction. J Neuroeng Rehab 15:98. https://doi.org/10.1186/s12984-018-0448-x

    Article  Google Scholar 

  7. Cheng T, Wang Q, Kamalapurkar R, Dinh HT, Bellman M, Dixon WE (2016) Identification-based closed-loop NMES limb tracking amplitude-modulated control input. IEEE Trans Cybern 46:1679–1690. https://doi.org/10.1109/TCYB.2015.2453402

    Article  PubMed  Google Scholar 

  8. El Makssoud H, Guiraud D, Poignet P, Hayashibe M, Wieber PB, Yoshida K, Azevedo-Coste C (2011) Multiscale modeling of skeletal muscle properties and experimental validations in isometric conditions. Biol Cyber 105:121. https://doi.org/10.1007/s00422-011-0445-7

    Article  Google Scholar 

  9. Fliess M, Lévine J, Martin P, Rouchon P (1995) Flatness and defect of non-linear systems: introductory theory and examples. Int J Cont 61:1327–1361. https://doi.org/10.1080/00207179508921959

    Article  Google Scholar 

  10. Guiraud D, Stieglitz T, Koch KP, Divoux JL, Rabischong P (2006) An implantable neuroprosthesis for standing and walking in paraplegia: 5-year patient follow-up. J Neural Eng 3:268– 275

    Article  Google Scholar 

  11. Hatze H (1981) Myocybernetic control models of skeletal muscle: characteristics and applications. Studia Mathematica University of South Africa

  12. Hill A (1938) The heat of shortening and the dynamic constants of muscle. Proc R Soc Lon B:Biol Sci 126:136–195

    Article  Google Scholar 

  13. Howlett OA, Lannin NA, Ada L, McKinstry C (2015) Functional electrical stimulation improves activity after stroke: a systematic review with meta-analysis. Arch Phys Med Rehab 96:934– 943

    Article  Google Scholar 

  14. Huxley A (1957) Muscle structure and theories of contraction. Prog Biophys Biophys Chem 7:255–318

    Article  CAS  Google Scholar 

  15. Isidori A (1995) Nonlinear control systems, 3rd edn. Springer, Berlin

    Book  Google Scholar 

  16. Khalil W, Dombre E (2002). In: khalil W, dombre E (eds). Modeling, Identification and Control of Robots Butterworth-Heinemann, Oxford, pp 313–345

  17. Kirsch N, Alibeji N, Sharma N (2017) Nonlinear model predictive control of functional electrical stimulation. Control Eng Pract 58:319–331. https://doi.org/10.1016/j.conengprac.2016.03.005

    Article  Google Scholar 

  18. Kralj A, Bajd T (1989) Functional electrical stimulation: standing and walking after spinal cord injury

  19. Lévine J (2011) On necessary and sufficient conditions for differential flatness applicable algebra in engineering. Commun Comp 22:47–90

    Google Scholar 

  20. Li Y, Chen W, Chen J, Chen X, Liang J, Du M (2019) Neural network based modeling and control of elbow joint motion under functional electrical stimulation. Neurocomputing 340:171–179. https://doi.org/10.1016/j.neucom.2019.03.003

    Article  Google Scholar 

  21. Li Z, Guiraud D, Andreu D, Benoussaad M, Fattal C, Hayashibe M (2016) Real-time estimation of fes-induced joint torque with evoked emg. J Neuroeng Rehab 13:60. https://doi.org/10.1186/s12984-016-0169-y

    Article  Google Scholar 

  22. Li Z, Hayashibe M, Andreu D, Guiraud D (2015) Real-time closed-loop fes control of muscle activation with evoked emg feedback. In: 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)623–626 https://doi.org/10.1109/NER.2015.7146700

  23. Martin P, Murray RM, Rouchon P (1997) Flat systems. In: Gevers M, Bastin G (eds) Plenary Lectures and Mini-Courses 4th European Control Conference (1997), pp 211–264

  24. Mohammed S, Fraisse P, Guiraud D, Poignet P, Makssoud HE (2005) Robust control law strategy based on high order sliding mode: towards a muscle control. In: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp 2644–2649 https://doi.org/10.1109/IROS.2005.1545413

  25. Mohammed S, Poignet P, Fraisse P, Guiraud D (2012) Toward lower limbs movement restoration with input - output feedback linearization and model predictive control through functional electrical stimulation. Control Eng Pract 20:182–195. https://doi.org/10.1016/j.conengprac.2011.10.010

    Article  Google Scholar 

  26. Mohammed S, Poignet P, Guiraud D (2006) Closed loop nonlinear model predictive control applied on paralyzed muscles to restore lower limbs functions. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems pp. 259–264 https://doi.org/10.1109/IROS.2006.281945

  27. Müller P, Balligand C, Seel T, Schauer T (2017) Iterative learning control and system identification of the antagonistic knee muscle complex during gait using functional electrical stimulation. IFAC-PapersOnLine 50:8786–8791. https://doi.org/10.1016/j.ifacol.2017.08.1738

    Article  Google Scholar 

  28. Nijmeijer H, Van der Schaft A (1990) Nonlinear dynamical control systems. Springer, Berlin

    Book  Google Scholar 

  29. Riener R, Fuhr T (1998) Patient-driven control of fes-supported standing up: a simulation study. IEEE Trans Rehab Eng 6:113–124

    Article  CAS  Google Scholar 

  30. Rotella F, Ayadi M, Carrillo F (2002) Control and trajectory tracking by flatness of a time-variant stator flux motor. In: Control Applications, 2002, Proceedings of the 2002 International Conference on, vol. 1 IEEE pp. 102–107

  31. Rotella F, Carillo FJ, Ayadi M (2002) Polynomial controller design based on flatness. Kybernetika 38:571–584

    Google Scholar 

  32. Sharma N, Kirsch NA, Alibeji NA, Dixon WE (2017) A non-linear control method to compensate for muscle fatigue during neuromuscular electrical stimulation frontiers. In: Robotics and AI 4:68 https://doi.org/10.3389/frobt.2017.00068

  33. Sira-Ramirez H, Agrawal SK (2004) Differentially flat systems, Marcel Dekker, New York

  34. Stein RB, Zehr EP, Lebiedowska MK, Popović DB, Scheiner A, Chizeck HJ (1996) Estimating mechanical parameters of leg segments in individuals with and without physical disabilities. IEEE Trans Rehab Eng 4:201–211

    Article  CAS  Google Scholar 

  35. Yang R, de Queiroz M (2016) Adaptive control of the nonlinearly parameterized limb dynamics with application to neuromuscular electrical stimulation. In: 2016 American Control Conference (ACC), pp 4883–4888 https://doi.org/10.1109/ACC.2016.7526126

  36. Yang R, de Queiroz M, Li M (2019) Neural network-based control of neuromuscular electrical stimulation with input saturation. IFAC-PapersOnLine 51:170–175. https://doi.org/10.1016/j.ifacol.2019.01.061

    Article  Google Scholar 

  37. Zajac FE (1989) Muscle and tendon: properties, models, scaling, and application to biomechanics and motor control. Crit Rev Bio Eng 17:359–411

    CAS  Google Scholar 

Download references

Funding

This study was financially supported by the IDEX program of University of Toulouse (France).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mourad Benoussaad.

Additional information

Publisher’s note

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

Appendix: Ruled manifold criterion of one-input musculoskeletal model

Appendix: Ruled manifold criterion of one-input musculoskeletal model

Considering only one input (α) and uch as a constant, we can define the input (10)

$$v = \alpha u_{ch}$$

and parameters

$$u_{c} = u_{ch}; ~c_{1}=\frac{r_{1}}{L_{c0}}; ~c_{2}=-\frac{r_{1}}{J}; ~ c_{3}=\frac{m g d}{J}; ~c_{4}=-\frac{F_{v}}{J}$$

Therefore, the one-input musculoskeletal model can described by Eq. 11, by considering uc as a constant:

The first stage of ruled manifold criterion consists in expressing the implicit form (21) of the model by eliminating the input control v from Eq. 34b, c. Let us extract the input v from Eq. 34c as follows:

$$ \begin{array}{@{}rcl@{}} && (1+p x_{1} + q x_{2})~\dot x_{2} = F_{0}(x_{3})~v - x_{2}~u_{c} \\ && \qquad\qquad\qquad\qquad\quad \!+ (b x_{1} + a x_{2})~c_{1}~x_{4} \end{array} $$
(35)
$$ \begin{array}{@{}rcl@{}} &&\Leftrightarrow v = \frac{(1+p x_{1} + q x_{2})~\dot x_{2} + x_{2}~u_{c} - (b x_{1} + a x_{2})~c_{1}~x_{4}}{F_{0}(x_{3})}\\ \end{array} $$
(36)

From Eq. 36b, we get

$$ \begin{array}{@{}rcl@{}} (1+p x_{1} + q x_{2})~\dot x_{1} &=& a c_{1} x_{1} x_{4} - \left( x_{1}~(1+p x_{1} + q x_{2}) \right. \\ &&\left. +q x_{2} x_{1}\right)u_{c}+ \left( K_{0}(x_{3})(1+p x_{1} \right.\\ &&\left.+ q x_{2}) - q F_{0}(x_{3}) x_{1}\right)~v \end{array} $$
(37)

Then, by replacing v in this equation, we obtain an equation without control input:

$$ \begin{array}{@{}rcl@{}} &&(1 +p x_{1} + q x_{2})~F_{0}(x_{3})\dot x_{1}\\ &=& a F_{0}(x_{3}) c_{1} x_{1} x_{4} - (x_{1}~(1+p x_{1} + q x_{2}) + q x_{2} x_{1})\\ &&\times F_{0}(x_{3}) u_{c}+ (K_{0}(x_{3})(1+p x_{1} + q x_{2}) - q F_{0}(x_{3}) x_{1})\\ &&\times ((1+p x_{1} + q x_{2})~\dot x_{2}+ x_{2}~u_{c} - (b x_{1} + a x_{2}) c_{1} x_{4}) \end{array} $$

Based on this input elimination and Eq. 34d, e, we define the implicit form of the one-input model as \(F(\mathbf {x},\dot {\mathbf { x}})=0\):

$$ \left\{\begin{array}{l} (1 + p x_{1} + q x_{2})~F_{0}(x_{3}) \dot x_{1} + \left( x_{1}~(1+p x_{1} + q x_{2}) + q x_{2} x_{1}\right) F_{0}(x_{3}) u_{c} \\ ~~~- \left( K_{0}(x_{3})(1+p x_{1} + q x_{2}) - q F_{0}(x_{3}) x_{1} \right)~\left( (1 + p x_{1} + q x_{2})~\dot x_{2} + x_{2}~u_{c} - (b x_{1} + a x_{2})~c_{1}~x_{4}\right) \\ ~~~ - a F_{0}(x_{3}) c_{1} x_{1} x_{4} = 0\\ \dot x_{3} - x_{4} = 0\\ \dot x_{4} + c_{2}~x_{2} + c_{3}~\cos(x_{3}) + c_{4}~x_{4} = 0 \end{array}\right. $$
(38)

Let us then explore a necessary condition of flatness by using the negation of ruled manifold criterion (24). Then we have

$$\forall (\lambda,\mu) \in \mathbb{R}^{2n} $$

where λ = (λ1,λ2,λ3,λ4) and μ = (μ1,μ2,μ3,μ4).

Considering, from the implicit form of the model (38), the following set of equation, which corresponds to F(λ,μ) = 0:

$$ \left\{\begin{array}{l} (1 + p \lambda_{1} + q \lambda_{2})~F_{0}(\lambda_{3}) \mu_{1} + (\lambda_{1}~(1 + p \lambda_{1} + q \lambda_{2}) + q \lambda_{2} \lambda_{1}) F_{0}(\lambda_{3}) u_{c} \\ ~~~- \left( K_{0}(\lambda_{3})(1+p \lambda_{1} + q \lambda_{2}) - q F_{0}(\lambda_{3}) \lambda_{1} \right) \left( (1 + p \lambda_{1} + q \lambda_{2})~\mu_{2} + \lambda_{2}~u_{c} - (b \lambda_{1} + a \lambda_{2})~c_{1}~\lambda_{4}\right) \\ ~~~- a F_{0}(\lambda_{3}) c_{1} \lambda_{1} \lambda_{4} = 0\\ \mu_{3} - \lambda_{4} = 0\\ \mu_{4} + c_{2}~\lambda_{2} + c_{3}~\cos(\lambda_{3}) + c_{4}~\lambda_{4} = 0 \end{array}\right. $$
(39)

Then, \(\forall e \in \mathbb {R}\), let us consider the statement F(λ,μ + ew) = 0, where w = (w1,w2,w3,w4).

$$ \Rightarrow \left\{\begin{array}{l} (1 + p \lambda_{1} + q \lambda_{2})~F_{0}(\lambda_{3}) (\mu_{1} + e w_{1}) + (\lambda_{1}~(1 + p \lambda_{1} + q \lambda_{2}) + q \lambda_{2} \lambda_{1}) F_{0}(\lambda_{3}) u_{c} \\ ~~~ -\left( K_{0}(\lambda_{3})(1+p \lambda_{1} + q \lambda_{2}) - q F_{0}(\lambda_{3}) \lambda_{1} \right) \left( (1 + p \lambda_{1} + q \lambda_{2})~(\mu_{2} + e w_{2}) + \lambda_{2}~u_{c} - (b \lambda_{1} + a \lambda_{2})~c_{1}~\lambda_{4}\right) \\ ~~~ - a F_{0}(\lambda_{3}) c_{1} \lambda_{1} \lambda_{4} = 0\\ \mu_{3} + e w_{3} - \lambda_{4} = 0\\ \mu_{4} + e w_{4} + c_{2}~\lambda_{2} + c_{3}~\cos(\lambda_{3}) + c_{4}~\lambda_{4} = 0 \end{array}\right. $$
(40)

By taking into account that F(λ,μ) = 0 (39), we obtain

$$ \left\{\begin{array}{l} (1 + p \lambda_{1} + q \lambda_{2})~F_{0}(\lambda_{3}) e w_{1} - (K_{0}(\lambda_{3})(1+p \lambda_{1} + q \lambda_{2}) - q F_{0}(\lambda_{3}) \lambda_{1}) (1 + p \lambda_{1} + q \lambda_{2})~e w_{2} = 0\\ e w_{3} = 0\\ e w_{4} = 0 \end{array}\right. $$
(41)
$$ \Rightarrow \left\{\begin{array}{l} e F_{0}(\lambda_{3}) w_{1} = e w_{2} \frac{(K_{0}(\lambda_{3})(1+p \lambda_{1} + q \lambda_{2}) - q F_{0}(\lambda_{3}) \lambda_{1}) (1 + p \lambda_{1} + q \lambda_{2})}{1 + p \lambda_{1} + q \lambda_{2}} = e w_{2} K(\lambda_{1}, \lambda_{2}) \\ e w_{3} = 0\\ e w_{4} = 0 \end{array}\right. $$
(42)

Thus, for all λ1,λ2,λ3, and λ4, the only possible values of w that satisfy this last statement are (w1,w2,w3,w4) = (0, 0, 0, 0).

Since w = (w1,w2,w3,w4) are proved to be equal to zero in order to respect the ruled manifold criterion negation (24), the musculoskeletal system with a single input is thus proved to be not flat.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Benoussaad, M., Rotella, F. & Chaibi, I. Flatness of musculoskeletal systems under functional electrical stimulation. Med Biol Eng Comput 58, 1113–1126 (2020). https://doi.org/10.1007/s11517-020-02139-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-020-02139-3

Keywords

Navigation