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Introducing GyroSym: a single-wheel robot

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Abstract

In this paper, GyroSym, a compact highly-stable single-wheel robot is introduced. Symmetrical flywheels have been used as its stabilization mechanism to keep the wheel upright. Thanks to the type of the flywheel used, the dynamic equations of the robot is simplified, the torque needed to tilt the spin axis is reduced, and the space is drastically minimized. Besides, a centrifugal clutch is used to smooth the motion of the robot in forward motion. Two controllers, fuzzy and fuzzy-Padé, were implemented based on their ability to stabilize the robot. Although both prevented the GyroSym from falling over, neither can entirely cancel out the effects of nonlinear behaviors that is present in the response, represented as high-frequency low domain noise. However, fuzzy-Padé demonstrated more efficient functionality, response, and mathematical simplicity which made it a perfect choice for stabilizing the GyroSym. Experimental results of a prototype have validated that of the mathematical analysis and simulations.

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References

  1. Brown HB, Xu Y (1996) A single-wheel, gyroscopically stabilized robot. In: Proceedings of IEEE international conference robotics and automation, vol 4, pp 3658–3663. https://doi.org/10.1109/robot.1996.509270

  2. Park JH, Jung S (2013) Development and control of a single-wheel robot: practical mechatronics approach. Mechatronics 23:594–606. https://doi.org/10.1016/j.mechatronics.2013.05.010

    Article  Google Scholar 

  3. Kim PK, Park JH, Jung S (2010) Experimental studies of balancing control for a disc-typed mobile robot using a neural controller: GYROBO. In: 2010 IEEE international symposium on intelligent control, pp 1499–503. https://doi.org/10.1109/isic.2010.5612929

  4. Nandy GC, Xu Y (1998) Dynamic model of a gyroscopic wheel. In: Proceedings of IEEE international conference on robotics and automation (Cat. No.98CH36146), vol 3, pp 2683–2688. https://doi.org/10.1109/robot.1998.680751

  5. Au KW, Xu Y (1999) Decoupled dynamics and stabilization of single wheel robot. In: Proceedings of IEEE/RSJ international conference intelligent robots and systems human and environment friendly robots with high intelligence emotional quotients (Cat. No.99CH36289), vol 1, pp 197–203. https://doi.org/10.1109/iros.1999.813004

  6. Alasty A, Pendar H (2005) Equations of motion of a single-wheel robot in a rough terrain. In: Proceedings of IEEE international conference robotics and automation, pp 879–884. https://doi.org/10.1109/robot.2005.1570228

  7. Lee S, Jung S (2017) Detection and control of a gyroscopically induced vibration to improve the balance of a single-wheel robot. J Low Freq Noise Vib Act Control 10:1–12. https://doi.org/10.1177/0263092317716075

    Article  Google Scholar 

  8. Tsai S-J, Ferreira ED, Paredis CJJ (1999) Control of the Gyrover. A single-wheel gyroscopically stabilized robot. In: Proceedings of IEEE/RSJ international conference intelligent robots and systems. Human and environment friendly robots with high intelligence and emotional quotients (Cat. No.99CH36289), vol 1, pp 179–184. https://doi.org/10.1109/iros.1999.813001

  9. Au SKW, Xu Y, Yu WWK (2001) Control of tilt-up motion of a single wheel robot via model-based and human-based controllers. Mechatronics 11:451–473. https://doi.org/10.1016/S0957-4158(00)00028-3

    Article  Google Scholar 

  10. Mohan S, Nandagopal JL, Amritha S (2018) Coupled dynamic control of unicycle robot using integral linear quadratic regulator and sliding mode controller. Mater Today Proc 5:1447–1454. https://doi.org/10.1016/j.matpr.2017.11.232

    Article  Google Scholar 

  11. Sun Z, Dai L, Liu K, Xia Y, Johansson KH (2018) Robust MPC for tracking constrained unicycle robots with additive disturbances. Automatica 90:172–184. https://doi.org/10.1016/j.automatica.2017.12.048

    Article  MathSciNet  MATH  Google Scholar 

  12. Xu Y, Yu W-K, Au K-W (1999) Modeling human strategy in controlling a dynamically stabilized robot. In: Proceedings of IEEE/RSJ international conference intelligent robots and systems. Human and environment friendly robotics with high intelligence and emotional quotients (Cat. No.99CH36289), vol 1, pp 507–512. https://doi.org/10.1109/iros.1999.813054

  13. Law CKH, Xu Y (2002) Shared control for navigation and balance of a dynamically stable robot. In: Proceedings of IEEE international conference robotics and automation (Cat. No.02CH37292), vol 2, pp 1985–1990. https://doi.org/10.1109/robot.2002.1014832

  14. Ou Y, Xu Y (2003) Input selection for learning human control strategy. In: Proceedings of IEEE/RSJ international conference intelligent robots and systems (IROS 2003) (Cat. No.03CH37453), vol 1, pp 668–673. https://doi.org/10.1109/iros.2003.1250706

  15. Ou Y, Xu Y (2003) Modeling human skills to control dynamic systems. In: Proceedings of IEEE international conference robotics, intelligent systems and signal process, vol 1, pp 382–387. https://doi.org/10.1109/rissp.2003.1285604

  16. Ou Y, Qian H, Wu X, Xu Y (2010) On stability region analysis for a class of human learning controllers. In: IEEE/RSJ international conference intelligent robots and systems, pp 1303–1309. https://doi.org/10.1109/iros.2010.5652242

  17. Chantarachit S, Parnichkun M (2016) Development and control of a unicycle robot with double flywheels. Mechatronics 40:28–40. https://doi.org/10.1016/j.mechatronics.2016.10.011

    Article  Google Scholar 

  18. Rashid MZA, Sidek SN (2011) Dynamic modeling and verification of unicycle mobile robot system. In: 4th international conference mechatronics, pp 1–5. https://doi.org/10.1109/icom.2011.5937199

  19. Cieslak P, Buratowski T, Uhl T, Giergiel M (2011) The mono-wheel robot with dynamic stabilisation. Rob Auton Syst 59:611–619. https://doi.org/10.1016/j.robot.2011.05.002

    Article  Google Scholar 

  20. Fujimoto Y, Uchida S (2007) Three dimensional posture control of mono-wheel robot with roll rotatable Torso. In: IEEE international conference mechatronics, pp 1–5. https://doi.org/10.1109/icmech.2007.4280051

  21. Lee JH, Shin HJ, Lee SJ, Jung S (2013) Balancing control of a single-wheel inverted pendulum system using air blowers: evolution of Mechatronics capstone design. Mechatronics 23:926–932. https://doi.org/10.1016/j.mechatronics.2012.08.006

    Article  Google Scholar 

  22. Ruan X, Xie W (2015) Lateral dynamic modelling and control of a single wheel robot based on airflow flywheel. In: IEEE international conference mechatronics and automation, pp 2192–2196. https://doi.org/10.1109/icma.2015.7237826

  23. Xiao Y (2016) Electromagnetic force balanced single-wheel robot. Chinese J Electron 25:441–447

    Article  Google Scholar 

  24. Wang Y, Wu C, Yu L, Mei Y (2018) Dynamics of a rolling robot of closed five-arc-shaped-bar linkage. Mech Mach Theory 121:75–91. https://doi.org/10.1016/j.mechmachtheory.2017.10.010

    Article  Google Scholar 

  25. Abedinnasab MH, Yoon Y-J, Saeedi-Hosseiny MS (2013) High performance fuzzy-Padé controllers: introduction and comparison to fuzzy controllers. Nonlinear Dyn 71:141–157. https://doi.org/10.1007/s11071-012-0647-0

    Article  Google Scholar 

  26. Nayfeh AH, Dean T (1995) Mook. Nonlinear Oscillations. pp 331

Download references

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Correspondence to Mohammad H. Abedin-Nasab.

Appendices

Appendix A

1.1 Lagrangian of GyroSym

The GyroSym consists of three subsystems which are the flywheels/motors, the pendulum, and the wheel encompassing all other components. Each of these three subsystems has kinetic and potential energy. The Lagrangian presented in Eq. (7) is the subtraction of the summation of kinetic energies from potential energies of the subsystems. The summation of the kinetic energies is calculated using the general equation for kinetic energy shown in Eq. (25).

$$ L = T - V $$
(23)
$$ T = \mathop \sum \limits_{j = 1}^{ num of\;subsys} \frac{1}{2}mV_{j}^{T} V_{j} + \frac{1}{2}\omega_{j}^{T} I_{j} \omega_{j} $$
(24)
$$ \begin{aligned} T &= 0.5m_{ring} \dot{X}^{2} + 0.5m_{ring} \dot{Y}^{2} + 0.5m_{ring} \dot{Z}^{2}\\ & \quad +\,0.5I_{xx} \left( {\dot{\varphi } - \dot{\psi }{\text{s}}\left( \theta \right)} \right)^{2} \\ & \quad + \;0.5I_{yy} \dot{\theta }^{2} + 0.5I_{zz} \dot{\psi }^{2} {\text{c}}\left( \theta \right)^{2} \\ & \quad+ \;0.5m_{p} \left( {\dot{X} - r_{PCG} {\text{c}}\left( \theta \right){\text{c}}\left( \psi \right)\left( {\dot{\theta }{\text{c}}\left( \beta \right) + \dot{\psi }{\text{c}}\left( \theta \right){\text{s}}\left( \beta \right)} \right)} \right. \\ & \quad - \;r_{PCG} {\text{s}}\left( \psi \right)\left( {\dot{\beta }{\text{c}}\left( \beta \right) - \dot{\psi }{\text{s}}\left( \theta \right){\text{c}}\left( \beta \right)} \right) \\ & \quad \left. { + \;r_{PCG} s\left( \theta \right)c\left( \psi \right)\left( {\dot{\beta }s\left( \beta \right) - \dot{\psi }s\left( \theta \right)s\left( \beta \right)} \right)} \right)^{2} \\ & \quad+ \;0.5m_{P} \left( {\dot{Y} - r_{PCG} c\left( \theta \right)s\left( \psi \right)\left( {\dot{\theta }c\left( \beta \right) + \dot{\psi }c\left( \beta \right)s\left( \beta \right)} \right)} \right. \\ & \quad -\,r_{PCG} c\left( \psi \right)\left( {\dot{\beta }c\left( \beta \right) + \dot{\psi }s\left( \theta \right)c\left( \beta \right)} \right) \\ & \quad \left. { +\,r_{PCG} s\left( \theta \right)s\left( \psi \right)\left( {\dot{\beta }s\left( \beta \right) - \dot{\psi }s\left( \theta \right)s\left( \beta \right)} \right)} \right)^{2} \\ & \quad + \;0.5m_{P} \left( {\dot{Z} + r_{PCG} s\left( \theta \right)\left( {\dot{\theta }c\left( \beta \right) + \dot{\psi }c\left( \theta \right)s\left( \beta \right)} \right)} \right. \\ & \quad \left. { +\,r_{PCG} c\left( \theta \right)\left( {\dot{\beta }s\left( \beta \right) - \dot{\psi }s\left( \theta \right)s\left( \beta \right)} \right)} \right)^{2} \\ & \quad + \;0.5I_{{f_{{x^{\prime } x^{\prime } }} }} \left( {\dot{\psi }\left( { - s\left( \theta \right)c\left( \alpha \right) - c\left( \theta \right)s\left( \alpha \right)c\left( \beta \right)} \right)} \right. \\ & \quad \left. { +\,\dot{\theta }s\left( \alpha \right)s\left( \beta \right) + \dot{\beta }c\left( \alpha \right) + \dot{\Omega }} \right)^{2} \\ & \quad + \;0.5I_{{f_{{y^{\prime } y^{\prime } }} }} \left( {\dot{\psi }c\left( \theta \right)s\left( \beta \right) + \dot{\theta }c\left( \beta \right) + \dot{\alpha }} \right) \\ & \quad \left( {\dot{\psi }c\left( \theta \right)s\left( \beta \right) + \dot{\theta }c\left( \beta \right) + \dot{\alpha }} \right) \\ & \quad + \;0.5I_{{f_{{z^{\prime } z^{\prime } }} }} \left( {\dot{\psi }\left( { - s\left( \theta \right)s\left( \alpha \right) + c\left( \theta \right)c\left( \alpha \right)c\left( \beta \right)} \right)} \right. \\ & \quad \left. { -\,\dot{\theta }c\left( \alpha \right)s\left( \beta \right) + \dot{\beta }s\left( \alpha \right)} \right)^{2} \\ \end{aligned} $$
(25)

And the summation of potential energies is shown in Eq. (26).

$$ \begin{aligned} V & = V_{CG of ring} + V_{CG of pendulum} \\ V_{CG of ring} & = m_{ring} gZ \\ V_{CG of pendulum} &= m_{p} g\left( {Z - r_{PCG} \cos \left( \beta \right)\cos \left( \theta \right)} \right) \\ & \quad \to V = m_{ring} gZ\\ & + m_{p} g\left( {Z - r_{PCG} \cos \left( \beta \right)\cos \left( \theta \right)} \right) \\ \end{aligned} $$
(26)

Appendix B

2.1 General constraints of GyroSym

According to the Euler–Lagrange’s equation in the system constraints, should be rewritten in the standard form as shown in Eq. (27).

$$ \mathop \sum \limits_{k = 1}^{9} a_{ik} \dot{q}_{k} + b_{j} = 0 \quad j = 1,2,3 :{\text{Standard}}\;form\;for\;the\;constraints $$
(27)
$$ \begin{aligned} & - R_{ring} \sin \left( \psi \right)\sin \left( \theta \right)\dot{\psi } + R_{ring} \cos \left( \theta \right)\cos \left( \psi \right)\dot{\theta } \\ & + R_{ring} \sin \left( \psi \right)\dot{\varphi } + 0\dot{\beta } + 0\dot{\alpha } + 0\dot{\varOmega } \\ & - \dot{X} + 0\dot{Y} + 0\dot{Z} + 0 = 0 \\ \end{aligned} $$
(28)
$$ \begin{aligned} & R_{ring} \cos \left( \psi \right)\sin \left( \theta \right)\dot{\psi } + R_{ring} \cos \left( \theta \right)\sin \left( \psi \right)\dot{\theta } \\ & - R_{ring} \cos \left( \psi \right)\dot{\varphi } + 0\dot{\beta } + 0\dot{\alpha } + 0\dot{\varOmega } + 0\dot{X} - \dot{Y} + 0\dot{Z} + 0 = 0 \\ \end{aligned} $$
(29)
$$ \begin{aligned} & 0\dot{\psi } + R_{ring} \sin \left( \theta \right)\dot{\theta } + 0\dot{\varphi } + 0\dot{\beta } + 0\dot{\alpha } + 0\dot{\varOmega } \\ & + 0\dot{X} + 0\dot{Y} + \dot{Z} + 0 = 0 \\ \end{aligned} $$
(30)

According to the standard form of constraints in Euler–Lagrange’s equations, the coefficients of Lagrange’s multipliers are shown in (31).

$$ \begin{aligned} a_{11} & = - R_{ring} \sin \left( \psi \right)\sin \left( \theta \right),a_{12} = R_{ring} \cos \left( \theta \right)\cos \left( \psi \right), \\ a_{13} & = R_{ring} \sin \left( \psi \right) \\ a_{14} & = a_{15} = a_{16} = 0,a_{17} = - 1,a_{18} = a_{19} = 0,b_{1} = 0 \\ a_{21} & = R_{ring} \cos \left( \psi \right)\sin \left( \theta \right),a_{22} = R_{ring} \cos \left( \theta \right)\sin \left( \psi \right), \\ a_{23} & = - R_{ring} \cos \left( \psi \right) \\ a_{24} & = a_{25} = a_{26} = a_{27} = 0,a_{28} = - 1,a_{29} = 0,b_{2} = 0 \\ a_{31} & = 0,a_{32} = R_{ring} \sin \left( \theta \right),a_{33} = a_{34} = a_{35} = a_{36} \\ & = a_{37} = a_{38} = 0,a_{39} = 1,b_{3} = 0 \\ \end{aligned} $$
(31)

Besides the inherent constraints of the GyroSym which are presented in (31), some extra constraints are added to the system’s dynamics to maneuver the robot in the simulation. These constraints are the movements of the robotic actuators which are applied to the generalized coordinates of the system as an angular velocity. In Simulink, for locomotion of the robot, tilting the flywheel system, and the angular velocity of the flywheels, some functions are given to the coefficients of the constraints equation in its standard format. Within the simulation, these functions will be seen in the behavior of the GyroSym.

$$ \dot{\varphi } - \dot{\beta } = C_{1} \to \dot{\varphi } - \dot{\beta } - C_{1} = 0 $$
(32)
$$ \dot{\alpha } = C_{2} \to \dot{\alpha } - C_{2} = 0 $$
(33)
$$ \dot{\Omega } = C_{3} \to \dot{\varOmega } - C_{3} = 0 $$
(34)

The matrices of the extra coefficients are presented in (35) and (36).

$$ a^{{\prime }} = \left[ {\begin{array}{*{20}c} 0 & 0 & 1 & { - 1} & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 \\ \end{array} } \right] $$
(35)
$$ b^{{\prime }} = \left[ {\begin{array}{*{20}c} { - C_{1} } & { - C_{2} } & { - C_{3} } \\ \end{array} } \right]^{T} $$
(36)

The general coefficients of the robot are the combination of the inherent constraints with the extra constraints shown in (37) and (38).

$$ a_{tot} = \left[ {\begin{array}{*{20}c} a & {a^{{\prime }} } \\ \end{array} } \right]^{T} $$
(37)
$$ b_{tot} = \left[ {\begin{array}{*{20}c} b & {b^{{\prime }} } \\ \end{array} } \right]^{T} $$
(38)

Appendix C

3.1 Definition of unknown terms for the integrated multiplier method

The differential equations of GyroSym in the form of Integrated Multiplier Method is presented in (14). The unknown elements are \( \left[ M \right]_{9 \times 9} \) and \( \left\{ F \right\}_{9 \times 1} \) while other elements are defined in the previous sections. In this appendix, these elements are presented in (39) and (40).

$$ M_{ij} = \frac{{\partial^{2} L}}{{\partial \dot{q}_{i} \partial \dot{q}_{j} }} , \quad i = 1 \ldots 9 \;{\text{and}}\; j = 1 \ldots 9 $$
(39)
$$ \begin{aligned} & I_{xx} = 2I_{yy} = 2I_{zz} = I_{r} = 0.0034 \\ & I_{{f_{{x^{\prime}x^{\prime}}} }} = 2I_{{f_{{y^{\prime}y^{\prime}}} }} = 2I_{{f_{{z^{\prime}z^{\prime}}} }} = I_{f} = 0.0006824 \\ \end{aligned} $$
$$ \begin{aligned} M_{11} & = r_{PCG}^{2} \left( {1 - c^{2} \left( \theta \right)c^{2} \left( \beta \right)} \right)m_{p} + \left( {1 - \frac{1}{2}c^{2} \left( \theta \right)} \right)I_{r} \\ & \quad +\,\left( {s\left( \theta \right)c\left( \theta \right)c\left( \beta \right)c\left( \alpha \right)s\left( \alpha \right)} \right. \\ & \quad \left. +\,\frac{1}{2}\Big( c^{2} \left( \alpha \right)\left( {1 - c^{2} \left( \theta \right) - c^{2} \left( \theta \right)c^{2} \left( \beta \right)} \right)\right.\\ &\qquad \left. + c^{2} \left( \theta \right)c^{2} \left( \beta \right) + 1 \right) \Big)I_{f} \\ \end{aligned} $$
$$ \begin{aligned} M_{12} & = M_{21} = r_{PCG}^{2} c\left( \beta \right)c\left( \theta \right)s\left( \beta \right)m_{p} \\ & \quad +\,\frac{1}{2}s\left( \beta \right)\left( {c\left( \theta \right)c^{2} \left( \alpha \right)c\left( \beta \right) - s\left( \theta \right)c\left( \alpha \right)s\left( \alpha \right) - c\left( \beta \right)c\left( \theta \right)} \right)I_{f} \\ M_{13} & = M_{31} = - s\left( \theta \right)I_{r} \\ M_{14} & = M_{41} = - r_{PCG}^{2} s\left( \theta \right)m_{p} \\ &\quad -\,\frac{1}{2}\left( {s\left( \theta \right)c^{2} \left( \alpha \right) + c\left( \alpha \right)c\left( \theta \right)s\left( \alpha \right)c\left( \beta \right) + s\left( \theta \right)} \right)I_{f} \\ M_{15} & = M_{51} = \frac{1}{2}c\left( \theta \right)s\left( \beta \right)I_{f} \\ M_{16} & = M_{61} = - \left( {s\left( \theta \right)c\left( \alpha \right) + c\left( \theta \right)s\left( \alpha \right)c\left( \beta \right)} \right)I_{f} \\ M_{17} & = M_{71} = r_{PCG} \left( {s\left( \theta \right)c\left( \beta \right)s\left( \psi \right) - s\left( \beta \right)s\left( \psi \right)} \right)m_{p} \\ M_{18} & = M_{81} = - r_{PCG} \left( {s\left( \theta \right)c\left( \beta \right)c\left( \psi \right) + s\left( \beta \right)s\left( \psi \right)} \right)m_{p} \\ M_{22} & = r_{PCG}^{2} c^{2} \left( \beta \right)m_{p} + \frac{1}{2}I_{r} \\ & \quad +\,\frac{1}{2}\left( {2 - c^{2} \left( \beta \right) - c^{2} \left( \alpha \right) + c^{2} \left( \alpha \right)c^{2} \left( \beta \right)} \right)I_{f} \\ M_{24} & = M_{42} = \frac{1}{2}s\left( \alpha \right)s\left( \beta \right)c\left( \alpha \right)I_{f} \\ M_{25} & = M_{52} = \frac{1}{2}c\left( \beta \right)I_{f} \\ M_{26} & = M_{62} = s\left( \alpha \right)s\left( \beta \right)I_{f} \\ M_{27} & = M_{72} = - r_{PCG} c\left( \beta \right)c\left( \theta \right)c\left( \psi \right)m_{p} \\ M_{28} & = M_{82} = - r_{PCG} c\left( \beta \right)c\left( \theta \right)s\left( \psi \right)m_{p} \\ M_{29} & = M_{92} = - r_{PCG} s\left( \theta \right)c\left( \beta \right)m_{p} \\ M_{33} & = I_{r} \\ M_{44} & = r_{PCG}^{2} m_{p} + \frac{1}{2}\left( {1 + c^{2} \left( \alpha \right)} \right)I_{f} \\ M_{46} & = M_{64} = c\left( \alpha \right)I_{f} \\ M_{47} & = M_{74} = r_{PCG} \left( {s\left( \theta \right)c\left( \psi \right)s\left( \beta \right) - s\left( \psi \right)c\left( \beta \right)} \right)m_{p} \\ M_{48} & = M_{84} = r_{PCG} \left( {s\left( \theta \right)s\left( \psi \right)s\left( \beta \right) + c\left( \psi \right)c\left( \beta \right)} \right)m_{p} \\ M_{49} & = M_{94} = r_{PCG} s\left( \beta \right)c\left( \theta \right)m_{p} \\ M_{55} & = \frac{1}{2}I_{f} \\ M_{66} & = I_{f} \\ M_{77} & = m_{ring} + m_{p} \\ M_{88} & = m_{ring} + m_{p} \\ M_{99} & = m_{ring} + m_{p} \\ \end{aligned} $$
$$ \begin{aligned} M_{19} & = M_{91} = M_{23} = M_{32} = M_{34} = M_{43} = M_{35} = M_{53} \\ & = M_{36} = M_{63} = M_{37} = M_{73} = M_{38} \\ & = M_{83} = M_{39} = M_{93} = M_{45} = M_{54} = M_{56} = M_{65} \\ & = M_{57} = M_{75} = M_{58} = M_{85} = M_{59} = M_{95} \\ & = M_{67} = M_{76} = M_{68} = M_{86} = M_{69} = M_{96} = M_{78} \\ & = M_{87} = M_{79} = M_{97} = M_{89} = M_{98} = 0 \\ \end{aligned} $$
$$ \begin{aligned} \left[ M \right]_{9 \times 9} \left\{ {\ddot{q}} \right\}_{9 \times 1} & = \left\{ F \right\}_{9 \times 1} + \left[ a \right]_{9 \times 6}^{T} \left\{ \lambda \right\}_{6 \times 1} \to \left\{ F \right\}_{9 \times 1} \\ & = \left[ M \right]_{9 \times 9} \left\{ {\ddot{q}} \right\}_{9 \times 1} - \left[ a \right]_{9 \times 6}^{T} \left\{ \lambda \right\}_{6 \times 1} \\ & the\; equations\;of\;\left\{ F \right\}_{9 \times 1} \;are \\ & too\;big\;for\; this\;space. \\ \end{aligned} $$
(40)

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Forouhar, M., Abedin-Nasab, M.H. & Liu, G. Introducing GyroSym: a single-wheel robot. Int. J. Dynam. Control 8, 404–417 (2020). https://doi.org/10.1007/s40435-019-00565-2

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