A case study of optimal input-output system with sampled-data control: Ding et al. force and fatigue muscular control model

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March 2019, 14(1): 79-100. doi: 10.3934/nhm.2019005

A case study of optimal input-output system with sampled-data control: Ding et al. force and fatigue muscular control model

Toufik Bakir ^1, , Bernard Bonnard ^2, and Jérémy Rouot ^3,,

1.	Le2i Laboratory EA 7508, Dijon, France
2.	Univ. Bourgogne Franche-Comté and INRIA Sophia Antipolis, Dijon, France
3.	Univ. Bourgogne Franche-Comté and EPF École Ingénieur-e-s, Troyes, France

^* Corresponding author: Jérémy Rouot

Received April 2018 Published January 2019

The objective of this article is to make the analysis of the muscular force response to optimize electrical pulses train using Ding et al. force-fatigue model. A geometric analysis of the dynamics is provided and very preliminary results are presented in the frame of optimal control using a simplified input-output model. In parallel, to take into account the physical constraints of the problem, partial state observation and input restrictions, an optimized pulses train is computed with a model predictive control, where a non-linear observer is used to estimate the state-variables.

Keywords: Muscular force and fatigue model, optimization of sampled-data control, non-linear observer, model predictive control.

Mathematics Subject Classification: 49K15, 93B07, 92B05.

Citation: Toufik Bakir, Bernard Bonnard, Jérémy Rouot. A case study of optimal input-output system with sampled-data control: Ding et al. force and fatigue muscular control model. Networks & Heterogeneous Media, 2019, 14 (1) : 79-100. doi: 10.3934/nhm.2019005

References:

[1]	T. Bakir, B. Bonnard and S. Othman, Predictive control based on non-linear observer for muscular force and fatigue model, Annual American Control Conference (ACC), Milwaukee (2018) 2157-2162. doi: 10.23919/ACC.2018.8430962. Google Scholar
[2]	J. Bobet and R. B. Stein, A simple model of force generation by skeletal muscle during dynamic isometric contractions, IEEE Transactions on Biomedical Engineering, 45 (1998), 1010-1016. doi: 10.1109/10.704869. Google Scholar
[3]	L. Bourdin and E. Trélat, Optimal sampled-data control, and generalizations on time scales, Math. Cont. Related Fields, 6 (2016), 53-94. doi: 10.3934/mcrf.2016.6.53. Google Scholar
[4]	S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004. doi: 10.1017/CBO9780511804441. Google Scholar
[5]	C. R. Cutler and B. L. Ramaker, Dynamic Matrix Control: A Computer Control Algorithm, In Joint automatic control conference, San Francisco, 1981. Google Scholar
[6]	J. Ding, S. A. Binder-Macleod and A. S. Wexler, Two-step, predictive, isometric force model tested on data from human and rat muscles, J. Appl. Physiol., 85 (1998), 2176-2189. doi: 10.1152/jappl.1998.85.6.2176. Google Scholar
[7]	J. Ding, A. S. Wexler and S. A. Binder-Macleod, Development of a mathematical model that predicts optimal muscle activation patterns by using brief trains, J. Appl. Physiol., 88 (2000), 917-925. doi: 10.1152/jappl.2000.88.3.917. Google Scholar
[8]	J. Ding, A. S. Wexler and S. A. Binder-Macleod, A predictive model of fatigue in human skeletal muscles, J. Appl. Physiol., 89 (2000), 1322-1332. doi: 10.1152/jappl.2000.89.4.1322. Google Scholar
[9]	J. Ding, A. S. Wexler and S. A. Binder-Macleod, Mathematical models for fatigue minimization during functional electrical stimulation, J. Electromyogr. Kinesiol., 13 (2003), 575-588. doi: 10.1016/S1050-6411(03)00102-0. Google Scholar
[10]	R. Fletcher, Practical Methods of Optimization, A Wiley-Interscience Publication. John Wiley & Sons, Second edition., Ltd., Chichester, 1987. Google Scholar
[11]	J. P. Gauthier, H. Hammouri and S. Othman, A simple observer for non-linear systems Application to bioreactors, IEEE Trans. Automat. Control, 37 (1992), 875-880. doi: 10.1109/9.256352. Google Scholar
[12]	R. Gesztelyi, J. Zsuga, A. Kemeny-Beke, B. Varga, B. Juhasz and A. Tosaki, The Hill equation and the origin of quantitative pharmacology, Arch. Hist. Exact Sci., 66 (2012), 427-438. doi: 10.1007/s00407-012-0098-5. Google Scholar
[13]	R. Hermann and J. Krener, Non-linear controllability and observability, IEEE Transactions on Automatic Control, AC-22 (1977), 728-740. doi: 10.1109/tac.1977.1101601. Google Scholar
[14]	A. Isidori, Non-linear Control Systems, 3rd ed. Berlin, Germany: Springer-Verlag, 1995. doi: 10.1007/978-1-84628-615-5. Google Scholar
[15]	L. F. Law and R. Shields, Mathematical models of human paralyzed muscle after long-term training, Journal of Biomechanics, 40 (2007), 2587-2595. Google Scholar
[16]	S. Li, K. Y. Lim and D. G. Fisher, A state space formulation for model predictive control, Springer, New York, 35 (1989), 241-249. doi: 10.1002/aic.690350208. Google Scholar
[17]	J. Richalet, A. Rault, J. L. Testud and J. Papon, Model algorithmic control of industrial processes, In IFAC Proceedings, 10 (1977), 103–120. doi: 10.1016/S1474-6670(17)69513-2. Google Scholar
[18]	H. J. Sussmann and V. Jurdjevic, Controllability of non-linear systems, J. Differential Equations, 12 (1972), 95-116. doi: 10.1016/0022-0396(72)90007-1. Google Scholar
[19]	L. Wang, Model Predictive Control System Design and Implementation Using MATLAB, Springer, London, 2009. Google Scholar
[20]	E. Wilson, Force Response of Locust Skeletal Muscle, Southampton University, Ph.D. thesis, 2011. Google Scholar

show all references

References:

[1]	T. Bakir, B. Bonnard and S. Othman, Predictive control based on non-linear observer for muscular force and fatigue model, Annual American Control Conference (ACC), Milwaukee (2018) 2157-2162. doi: 10.23919/ACC.2018.8430962. Google Scholar
[2]	J. Bobet and R. B. Stein, A simple model of force generation by skeletal muscle during dynamic isometric contractions, IEEE Transactions on Biomedical Engineering, 45 (1998), 1010-1016. doi: 10.1109/10.704869. Google Scholar
[3]	L. Bourdin and E. Trélat, Optimal sampled-data control, and generalizations on time scales, Math. Cont. Related Fields, 6 (2016), 53-94. doi: 10.3934/mcrf.2016.6.53. Google Scholar
[4]	S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004. doi: 10.1017/CBO9780511804441. Google Scholar
[5]	C. R. Cutler and B. L. Ramaker, Dynamic Matrix Control: A Computer Control Algorithm, In Joint automatic control conference, San Francisco, 1981. Google Scholar
[6]	J. Ding, S. A. Binder-Macleod and A. S. Wexler, Two-step, predictive, isometric force model tested on data from human and rat muscles, J. Appl. Physiol., 85 (1998), 2176-2189. doi: 10.1152/jappl.1998.85.6.2176. Google Scholar
[7]	J. Ding, A. S. Wexler and S. A. Binder-Macleod, Development of a mathematical model that predicts optimal muscle activation patterns by using brief trains, J. Appl. Physiol., 88 (2000), 917-925. doi: 10.1152/jappl.2000.88.3.917. Google Scholar
[8]	J. Ding, A. S. Wexler and S. A. Binder-Macleod, A predictive model of fatigue in human skeletal muscles, J. Appl. Physiol., 89 (2000), 1322-1332. doi: 10.1152/jappl.2000.89.4.1322. Google Scholar
[9]	J. Ding, A. S. Wexler and S. A. Binder-Macleod, Mathematical models for fatigue minimization during functional electrical stimulation, J. Electromyogr. Kinesiol., 13 (2003), 575-588. doi: 10.1016/S1050-6411(03)00102-0. Google Scholar
[10]	R. Fletcher, Practical Methods of Optimization, A Wiley-Interscience Publication. John Wiley & Sons, Second edition., Ltd., Chichester, 1987. Google Scholar
[11]	J. P. Gauthier, H. Hammouri and S. Othman, A simple observer for non-linear systems Application to bioreactors, IEEE Trans. Automat. Control, 37 (1992), 875-880. doi: 10.1109/9.256352. Google Scholar
[12]	R. Gesztelyi, J. Zsuga, A. Kemeny-Beke, B. Varga, B. Juhasz and A. Tosaki, The Hill equation and the origin of quantitative pharmacology, Arch. Hist. Exact Sci., 66 (2012), 427-438. doi: 10.1007/s00407-012-0098-5. Google Scholar
[13]	R. Hermann and J. Krener, Non-linear controllability and observability, IEEE Transactions on Automatic Control, AC-22 (1977), 728-740. doi: 10.1109/tac.1977.1101601. Google Scholar
[14]	A. Isidori, Non-linear Control Systems, 3rd ed. Berlin, Germany: Springer-Verlag, 1995. doi: 10.1007/978-1-84628-615-5. Google Scholar
[15]	L. F. Law and R. Shields, Mathematical models of human paralyzed muscle after long-term training, Journal of Biomechanics, 40 (2007), 2587-2595. Google Scholar
[16]	S. Li, K. Y. Lim and D. G. Fisher, A state space formulation for model predictive control, Springer, New York, 35 (1989), 241-249. doi: 10.1002/aic.690350208. Google Scholar
[17]	J. Richalet, A. Rault, J. L. Testud and J. Papon, Model algorithmic control of industrial processes, In IFAC Proceedings, 10 (1977), 103–120. doi: 10.1016/S1474-6670(17)69513-2. Google Scholar
[18]	H. J. Sussmann and V. Jurdjevic, Controllability of non-linear systems, J. Differential Equations, 12 (1972), 95-116. doi: 10.1016/0022-0396(72)90007-1. Google Scholar
[19]	L. Wang, Model Predictive Control System Design and Implementation Using MATLAB, Springer, London, 2009. Google Scholar
[20]	E. Wilson, Force Response of Locust Skeletal Muscle, Southampton University, Ph.D. thesis, 2011. Google Scholar

Figure 1. Time evolution of the permanent control (thin continuous line) and sampled-data control for several values of the sampling period $ T_s\in \{T/20, T/40, T/200\} $

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Figure 2. Evolution of $ K_m $ for different initial conditions (case of $ I = 10ms $)

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Figure 3. Relative error of the force for a well known and erroneous $ K_m $ initial condition (case of $ I = 10ms $)

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Figure 4. General MPC strategy diagram

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Figure 5. (left half plane) $ E_s $ and force profile for applied amplitude and interpulse stimulation, (right half plane) Predicted $ E_s $ and force using single move strategy to be optimized

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Figure 6. Evolution of $ A $ and $ \hat{A} $ for $ I = 10 $, $ 30\% $ error of $ K_m $

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Figure 7. Evolution of $ A $ and $ \hat{A} $ for $ I = 25 $, $ 30\% $ error of $ K_m $

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Figure 8. Evolution of $ \tau_1 $ and $ \hat{\tau_1} $ for $ I = 10 $, $ 30\% $ error of $ K_m $

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Figure 9. Evolution of $ \tau_1 $ and $ \hat{\tau_1} $ for $ I = 25 $, $ 30\% $ error of $ K_m $

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Figure 10. Evolution of $ F $, $ \hat{F} $ and $ F $ mean value over $ I $ for $ I = 25 $, $ 30\% $ error of $ K_m $, $ Fref = 250N $

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Figure 11. Evolution of the force for a reference force of $ 425N $ and different receding horizons $ (3, 5 $ and $ 10) $

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Figure 12. Evolution of the interpulse (control) for a reference force of $ 425N $ and a preditive horizon of $ 10 $

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Figure 13. Evolution of the amplitude (control) for a reference force of $ 425N $ and a preditive horizon of $ 10 $

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Figure 14. Evolution of the interpulse (control) for a reference force of 425N and a preditive horizon of 10

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Figure 15. Evolution of the amplitude (control) for a reference force of 425N and a preditive horizon of 3

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Table 1. Margin settings

Symbol	Unit	Value	description
$ C_{N} $	—	—	Normalized amount of
			$ Ca^{2+} $-troponin complex
$ F $	$ N $	—	Force generated by muscle
$ t_{i} $	$ ms $	—	Time of the $ i^{th} $ pulse
$ n $	—	—	Total number of
			the pulses before time $ t $
$ i $	—	—	Stimulation pulse index
$ \tau_{c} $	$ ms $	$ 20 $	Time constant that commands
			the rise and the decay of $ C_{N} $
$ R_{0} $	—	$ 1.143 $	Term of the enhancement
			in $ C_{N} $ from successive stimuli
$ A $	$ \frac{N}{ms} $	—	Scaling factor for the force and
			the shortening velocity
			of muscle
$ \tau_{1} $	$ ms $	—	Force decline time constant
			when strongly bound
			cross-bridges absent
$ \tau_{2} $	$ ms $	$ 124.4 $	Force decline time constant
			due to friction between actin
			and myosin
$ K_{m} $	—	—	Sensitivity of strongly bound
			cross-bridges to $ C_{N} $
$ A_{rest} $	$ \frac{N}{ms} $	$ 3.009 $	Value of the variable $ A $
			when muscle is not fatigued
$ K_{m, rest} $	—	$ 0.103 $	Value of the variable $ K_{m} $
			when muscle is not fatigued
$ \tau_{1, rest} $	$ ms $	$ 50.95 $	The value of the variable $ \tau_{1} $
			when muscle is not fatigued
$ \alpha_{A} $	$ \frac{1}{ms^{2}} $	$ -4.0 10^{-7} $	Coefficient for the force-model
			variable $ A $ in the fatigue
			model
$ \alpha_{K_{m}} $	$ \frac{1}{msN} $	$ 1.9 10 ^{-8} $	Coefficient for the force-model
			variable $ K_{m} $ in the fatigue
			model
$ \alpha_{\tau_{1}} $	$ \frac{1}{N} $	$ 2.1 10^{-5} $	Coefficient for force-model
			variable $ \tau_{1} $ in the fatigue
			model
$ \tau_{fat} $	$ s $	$ 127 $	Time constant controlling the
			recovery of $ (A, K_{m}, \tau_{1}) $

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Symbol	Unit	Value	description
$ C_{N} $	—	—	Normalized amount of
			$ Ca^{2+} $-troponin complex
$ F $	$ N $	—	Force generated by muscle
$ t_{i} $	$ ms $	—	Time of the $ i^{th} $ pulse
$ n $	—	—	Total number of
			the pulses before time $ t $
$ i $	—	—	Stimulation pulse index
$ \tau_{c} $	$ ms $	$ 20 $	Time constant that commands
			the rise and the decay of $ C_{N} $
$ R_{0} $	—	$ 1.143 $	Term of the enhancement
			in $ C_{N} $ from successive stimuli
$ A $	$ \frac{N}{ms} $	—	Scaling factor for the force and
			the shortening velocity
			of muscle
$ \tau_{1} $	$ ms $	—	Force decline time constant
			when strongly bound
			cross-bridges absent
$ \tau_{2} $	$ ms $	$ 124.4 $	Force decline time constant
			due to friction between actin
			and myosin
$ K_{m} $	—	—	Sensitivity of strongly bound
			cross-bridges to $ C_{N} $
$ A_{rest} $	$ \frac{N}{ms} $	$ 3.009 $	Value of the variable $ A $
			when muscle is not fatigued
$ K_{m, rest} $	—	$ 0.103 $	Value of the variable $ K_{m} $
			when muscle is not fatigued
$ \tau_{1, rest} $	$ ms $	$ 50.95 $	The value of the variable $ \tau_{1} $
			when muscle is not fatigued
$ \alpha_{A} $	$ \frac{1}{ms^{2}} $	$ -4.0 10^{-7} $	Coefficient for the force-model
			variable $ A $ in the fatigue
			model
$ \alpha_{K_{m}} $	$ \frac{1}{msN} $	$ 1.9 10 ^{-8} $	Coefficient for the force-model
			variable $ K_{m} $ in the fatigue
			model
$ \alpha_{\tau_{1}} $	$ \frac{1}{N} $	$ 2.1 10^{-5} $	Coefficient for force-model
			variable $ \tau_{1} $ in the fatigue
			model
$ \tau_{fat} $	$ s $	$ 127 $	Time constant controlling the
			recovery of $ (A, K_{m}, \tau_{1}) $

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