The conventional limitations of the robotic actuation mechanisms have led to many researchers needing to explore novel biomimetic motor mechanisms as the antagonistic human motor system. In this way, it is of interest to understand the inherent adaptive stiffness, or compliance, and modulation, in different alternative actuation architectures such as the antagonistic bi-articular (AbA) system. These novel AbA actuation mechanisms are characterized by resembling the efficient tendon and muscle build-up over our skeletal structure. In this paper, we propose a Cartesian neuro-controller for a robot manipulator actuated by a simplified adaptive viscoelastic linear AbA system. It is shown that the adaptive closed-loop system enforces terminal attractors, induced by a continuous model-free sliding mode control, simultaneously with a learning algorithm to compensate parametric uncertainties of AbA system through a low dimensional neural network. Numerical simulation results exhibit the feasibility of this approach.
BalaghiMHVatankhahRBroushakiM (2016) Adaptive optimal multi-critic based neuro-fuzzy control of MIMO human musculoskeletal arm model. Neurocomputing173(3): 1529–1537.
2.
BicchiAToniettiG (2004) Fast and soft-arm tactics. IEEE Robotics & Automation Magazine11(2): 22–33.
3.
ChoiTYLeeJJ (2010) Control of manipulator using pneumatic muscles for enhanced safety. IEEE Transactions on Industrial Electronics57(8): 2815–2825.
4.
ChoiHOhSKongK (2014) Design of a biarticular robotic manipulator and its control in the rotating coordinate system. In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics, (ICAM, 2014), Besançon, France, 8–11 July 2014, pp. 888–891. Piscataway, New Jersey, USA: IEEE.
5.
DeckerM (2017) The next generation of robots for the next generation of humans?Robotics and Autonomous Systems88: 154–156.
6.
FredericMOberWCGarrisonCW, et al. (2001) Fundamentals of Anatomy and Physiology, 5th edn.USA: Prentice Hall College Div.
7.
FukushoHSugimotoTKosekiT (2010) Control of a straight line motion for a two-link robot arm using coordinate transform of bi-articular simultaneous drive. In: The 11th IEEE International Workshop on Advanced Motion Control (AMC2010), Nagaoka, Japan, 21–24 March. Piscataway, New Jersey, USA: IEEE.
8.
Garcia-RodriguezRParra-VegaV (2009) A neuro-sliding mode control scheme for constrained robots with uncertain Jacobian. Journal of Intelligent and Robotic Systems54(5): 689–708.
9.
Garcia-RodriguezRParra-VegaV (2011) Task-space neuro-sliding mode control of robot manipulators under Jacobian uncertainties. International Journal of Control, Automation, and Systems9(5): 895–904.
10.
HoganN (1984) Adaptive control of mechanical impedance by coactivation of antagonist muscles. IEEE Transactions on Automatic Control AC-29(8): 681–690.
11.
HuijingP (1999) Muscular force transmission: A unified, dual or multiple system. A review and some explorative experimental results. Arch Physiol Biochem107(4): 292–311.
12.
KawaiHMuraoTSatoRFujitaM (2011) Passivity-based control for 2DOF robot manipulators with antagonistic bi-articular muscles. In: IEEE International Conference on Control Applications, (CCA, 2011), Denver, CO, USA, 28–30 September 2011, pp. 1451–1456. Piscataway, New Jersey, USA: IEEE.
13.
KawaiYDowneyRJKawaiHDixonWE (2014) Co-contraction of antagonist bi-articular muscles for tracking control of human limb. In: American Control Conference, (ACC, 2014), Portland, OR, USA, 4–6 June 2014, pp. 3316–3321. Piscataway, New Jersey, USA: IEEE.
14.
KimYSalvucciVHoriY (2013) Design and control of an under-actuated robot leg, using state feedback and impulse shaping. In: 16th International Conference on Advanced Robotics (ICAR), Montevideo, USA, 25–29 November 2013, pp. 1–6. Piscataway, New Jersey, USA: IEEE.
15.
KühnelDTHelpsTRossiterJ (2016) Kinematic analysis of vibrobot: A soft, hopping robot with stiffness- and shape-changing abilities. Frontiers in Robotics and AI3: 60.
16.
KumamotoMOshimaTYamamotoT (1994) Control properties induced by the existence of antagonistic pairs of bi-articular muscles — Mechanical engineering model analyses. Human Movement Science13(15): 611–634.
17.
OhSHoriY (2009) Development of two-degree-of-freedom control for robot manipulator with biarticular muscle torque. In: American Control Conference, (ACC, 2009)St. Louis, Missouri, USA, 10–12 June 2009, pp. 325–330. Piscataway, New Jersey, USA: IEEE.
18.
OhSKongK (2014) Realization of spring loaded inverted pendulum dynamics with a two-link manipulator based on the bio-inspired coordinate system. In: IEEE International Conference on Robotics and Automation, (ICRA, 2014), Hong Kong, China, 31 May–5 June 2014, pp. 310–315. Piscataway, New Jersey, USA: IEEE.
19.
OhSKoyanagiTHoriY (2009) Stiffness direction stabilization and inertia matrix diagonalization of robot manipulator by biarticular muscle. In: IEEE International Symposium on Industrial Electronics, (ISIE, 2009), Seoul, Korea, 5–8 July 2009, pp. 989–994. Piscataway, New Jersey, USA: IEEE.
20.
Parra-VegaVArimotoSLiuYH, et al. (2003) Dynamic sliding PID control for tracking of robot manipulators: theory and experiments. IEEE Transactions on Robotics and Automation19(6): 967–976.
21.
PfeiferRLungarellaMIidaF (2012) The challenges ahead for bio-inspired soft robotics. Communications of the ACM55(11): 76–87.
22.
RobinsonRMKotheraCSWereleyNM (2015) Variable recruitment testing of pneumatic artificial muscles for robotic manipulators. IEEE/ASME Transactions on Mechatronics20(4): 1642–1652.
23.
SalvucciVKimuraYOhS, et al. (2014) Comparing approaches for actuator redundancy resolution in biarticularly-actuated robot arms. IEEE/ASME Transactions on Mechatronics19(2): 765–776.
24.
SandercockTGMaasH (2009) Force summation between muscles: are muscles independent actuators?Med Sci Sports Exerc41(1): 184–190.
25.
SanoKKawaiHMuraoTM.Fujita (2012) Open-loop control for 2DOF robot manipulators with antagonistic bi-articular muscles. IEEE International Conference on Control Applications, (CCA, 2012), Dubrovnik, Croatia, 3–5 October 2012, pp. 1346–1351.
26.
SchindlbeckCHaddadinS (2015) Unified passivity-based Cartesian force/impedance control for rigid and flexible joint robots via task-energy tanks. In: IEEE International Conference on Robotics and Automation. Seattle, Washington, USA, 26–30 May 2015, pp. 440–447. Piscataway, New Jersey, USA: IEEE.
27.
Sira-RamirezHColina-MoralesE (1993) Adaptive learning in perceptrons: A sliding mode control approach. Pure Mathematics and Applications, Department of Mathematics, Corvinus University of Budapest4(1): 99–133.
28.
Sira-RamirezHColina-MoralesE (1995) A sliding mode strategy for adaptive learning in adalines. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications42(12): 1001–1012.
29.
Sira-RamirezHZakSH (1991) The adaptation of perceptrons with applications to inverse dynamics identification of unknown dynamic systems. IEEE Transactions on Systems, Man, and Cybernetics21(3): 634–643.
30.
SmithCKarayiannidisYNalpantidisL, et al. (2012) Dual arm manipulation—A survey. Robotics and Autonomous Systems60(10): 1340–1353.
31.
TrivediDRahnCDKierWMWalkerID (2008) Soft robotics: Biological inspiration, state of the art, and future research. Applied Bionics and Biomechanics5(3): 99–117.
32.
TsujiT (2010) A model of antagonistic Tri-articular muscle mechanism for lancelet robot. In: Proceedings of 11th IEEE International Workshop on Advanced Motion Control, Nagaoka, Japan, 21–24 March 2010, pp. 496–501. Piscataway, New Jersey, USA: IEEE.
33.
VollmerALWredeBRohlfingKJOudeyerP (2016) Pragmatic frames for teaching and learning in human–Robot interaction: Review and challenges. Frontiers in Neurorobotics10(10): 1–20.
34.
ZakSHSira-RamirezH (1990) On the adaptation algorithms for feedforward neural networks. In: FieldDAKomkovV (eds) Theoretical Aspects of Industrial Design. New York: SIAM, pp. 116–133.
35.
ZhangJCheahCC (2015) Passivity and stability of human–robot interaction control for upper-limb rehabilitation robots. IEEE Transactions on Robotics31(2): 233–245.
