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The Art and Science of Bicycling
Arend L. Schwab
Laboratory for Engineering Mechanics (3mE/PME)
Delft University of Technology, The Netherlands




            Delft
            University of
            Technology

            Challenge the future
Acknowledgement

  TUDelft:                      Cornell University:                        UCDavis:
  Jaap Meijaard                 Andy Ruina                                 Mont Hubbard
  Jodi Kooijman                 Jim Papadopoulos                           Jason Moore
  Nick Appelman                 Andrew Dressel                             Luke Peterson




J. P. Meijaard, Jim M. Papadopoulos, Andy Ruina, A. L. Schwab (2007) Linearized dynamics equations for
       the balance and steer of a bicycle: a benchmark and review. Proc. R. Soc. A. 463, 1955-1982.

J. D. G. Kooijman, J. P. Meijaard, Jim M. Papadopoulos, Andy Ruina, and A. L. Schwab (2011) A bicycle
      can be self-stable without gyroscopic or caster effects, Science 15 April: 332(6027), 339-342.

                                        www.bicycle.tudelft.nl

                                                                                              3
1.
Bicycle Dynamics, some observations




                                 4
Movie




        Jour de Fête van Jacques Tati, 1949

                                              5
Experiment




     Yellow Bike in the Car Park, Cornell University, Ithaca, NY.

                                                                    6
Experiment




    Yellow Bike in the Car Park, Cornell University, Ithaca, NY.

                                                                   7
Bicycle Evolution




                    8
The Whipple/Carvallo Model (1899)




                          1899




        Francis Whipple
          1876-1943

   Delft
   University of
   Technology

   Challenge the future
The Whipple/Carvallo Model (1899)
                Modelling Assumptions:
                • rigid bodies
                • fixed rigid rider
                • hands-free
                • symmetric about vertical plane
                • knife-edge wheels
                • point contact, no side slip
                • flat level road
                • no friction or propulsion


                       3 velocity degrees of freedom


                Note: Energy Conservative




                                                       10
Linearized Eqn’s of Motion
 For the straight ahead upright motion with lean angle ϕ, steering angle δ and forward
 speed v :


     ϕ 
                     ϕ 
                                     2 ϕ 
[M]   + [C1v ]    + [K 0 + K 2v ]   =  0
     δ               δ               δ 
Standard bicycle + rider :                 v = 0
    130 −3        0 −40         −1003 27         0 −96 
M    = = 
     −3 0.3 , C1           , K0      =        , K2
                 0.6 1.8        27    −8.8 
                                                
                                                              
                                                       0 2.7 


Assume motions: ϕ
          =                 ϕ0e λt , δ δ 0e λt
                             =
Characteristic equation :        det(λ 2 [M] + λ [C1v ] + [K 0 + K 2v 2 ]) =
                                                                           0
leads to a fourth order characteristic polynomial in eigenvalues λ:
                         λ 4 + a3 (v )λ 3 + a2 (v )λ 2 + a1 (v )λ + a 0 (v ) =
                                                                             0

                                                                              12
The Whipple/Carvallo Model (1899)
                                 det(λ 2 [M] + λ [C1v ] + [K 0 + K 2v 2 ]) =
                                                                           0




3 velocity degrees of freedom:
- lean rate ϕ
            
- steer rate δ
- forward speed v
                                      selfstable: 4.6 < v < 7.9 m/s

                                 = ϕ0e λt , δ δ 0e λt
                                  ϕ =

                                                                      13
2.
Experimental validation




                          18
Experimental Validation
Instrumented Bicycle, uncontrolled

                                     2 rate gyros:
                                     -lean rate ϕ
                                                
                                     -yaw rate ψ
                                     1 speedometer:
                                     -forward speed v
                                     1 potentiometer
                                     -steering angle δ
                                     Laptop Computer
                                     running LabVIEW




                                                        19
An Experiment




                20
An Experiment




                21
Measured Data




                                 c 1 + e λ1t [c 2 cos(
Fit function for the lean rate: ϕ = λ2t ) + c 3 sin(λ2t )]


                                                             22
Compare with Linearized Results


                           λ2 = 5.52 [rad/s]




                           λ1 = -1.22 [rad/s]




                           forward speed:
                           4.9 < v <5.4 [m/s]




                                       23
Compare in a broad speed range




Conclusion:
Experimental data in good agreement with linearized analysis on 3 dof model.

                                                                       24
3.
Bicycle Selfstability




                        25
Selfstable: automagic control?




 How do we balance   We balance an inverted pendulum by
     a bicycle?        accelerating the support in the
                             direction of the fall.



                                                          26
Selfstable: automagic control?


                                                                     δ




                                                                          a

                                                              v



We balance an inverted pendulum by      Balance the bicycle by steer into the fall!
  accelerating the support in the
                                     ( lateral acceleration contact point: a ≈ v2/w δ )
        direction of the fall.



                                                                          27
Anecdote




LEGO Mindstorms NXT Bicycle, built by Joep Mutsaerts, MSc TUDelft




                                                                    28
Automagic control? Steer-into-the-fall !




            Control Law: SteerMotorVoltage=8*LeanRate
LEGO Mindstorms NXT Bicycle, built by Joep Mutsaerts, MSc TUDelft

                                                                    29
A selfstable bicycle




  Yellow Bike in the Car Park (slow motion), Cornell University, Ithaca, NY.

                                                                               30
A bicycle is selfstable because ….




Gyroscopic effect of the front wheel?   Trail on the front wheel?
    (Klein & Sommerfeld 1910)             (David Jones 1970)
                      5 km/h            25 km/h


                                                                    31
4.
Control and Handling




                       32
How do we control the mostly
   unstable bicycle?




       by steer and balance
                               33
How do we steer and balance?
•   A. van Lunteren and H. G. Stassen. On
    the variance of the bicycle rider’s
    behavior. In Proceedings of the 6th
    Annual Conference on Manual Control,
    April 1970.




•   David Herbert Weir. Motorcycle Handling
    Dynamics and Rider Control and the
    Effect of Design Configuration on
    Response and Performance. PhD thesis,
    University of California, LA, 1972.




                                              36
Relevance
                                                       80% elderly
                                                     75% single vehicle
                 Number of seriously injured




                            Year
60% of the accidents are among the elderly, they just fall over !

                                                                    37
A Ride into Town
Measure rider control on an instrumented bicycle

                                                   3 rate gyros: Lean, Yaw and Steer
                                                   1 steer angle potentiometer
                                                   2 forward speed
                                                   1 pedal cadence pickup
                                                   1 video camera
                                                   Compac Rio data collection




                                                                         38
A ride into Town




                   39
A Ride into Town




                   40
Treadmill experiments




Vrije Universiteit Amsterdam , 3 x 5 m treadmill, vmax=35 km/h

                                                                 41
Treadmill experiments




A first ride…

                        42
Rider Control Observations
Treadmill Experiments Camera Bicycle – Normal Cycling, Pedaling




                                                                  43
Rider Control Observations
Treadmill Experiments Camera Bicycle – Towing




                                                44
Rider Control
Full Human Motion Capture, Optotrack active marker system

                                                  • 31 markers xyz-coor.
                                                  • Sample freq 100 Hz
                                                  • Sample time 1 min

                                                  One run is 600,000
                                                   numbers.

                                                  Data reduction by
                                                   Principal Component
                                                   Analysis (PCA).




                                                                45
Rider Control
Full Human Motion Capture, Optotrack active marker system




3 x 5 m treadmill at the Vrije Universiteit Amsterdam

                                                            46
Rider Control
Treadmill Experiments Full Human Motion Capture - Normal Cycling, Pedaling




                                                                  47
Rider Control
Treadmill Experiments, Full Human Motion Capture – PCA Motion Groups




                                                                 48
Rider Control
Treadmill Experiments, Full Human Motion Capture - Compare




                5 km/h                25 km/h
                                                             49
Rider Control: Conclusions

• During normal bicycling the dominant upper body motions: lean, bend,
  twist and bounce, are all linked to the pedaling motion.

• We hypothesize that lateral control is mainly done by steering since we
  observed only upper body motion in the pedaling frequency.

• If upper body motions are used for control then this control is in the
  pedaling frequency.

• When pedaling at low speed we observe lateral knee motions which are
  probably also used for control.




                                                                           50
5.
Selfstability, revisited




                           51
A bicycle is selfstable because ….




Gyroscopic effect of the front wheel?   Trail on the front wheel?
                      5 km/h            25 km/h


                                                                    52
Gyroscopic Effect?
Klein & Sommerfeld 1910




                              Felix Klein               Arnold Sommerfeld
                             (1849-1925)                   (1868-1951)
                          from the Klein bottle       81 Nobel prize nominations




                                              Fritz Noether
                                              (1884-1941)
                                             brother of Emmy

                                                                       53
Gyroscopic Effect?
         Klein & Sommerfeld 1910




Two sign errors:




                                   Two sign errors corrected.




                                                                56
Trail?
Jones 1970




             57
Trail?
Jones 1970



             Jones calculated handlebar torque via the
             derivative of system gravitational (potential)
             energy H with respect to steer angle at fixed
             lean.


             But careful analysis on the full dynamical system
             (linearized equations of motion) shows:




                                                        58
Two-mass-skate (TMS) bicycle
Only 8 bicycle parameters




Two point masses and wheels are replaced by skates




Only 8 non-zero bicycle parameters

                                                     61
Two-mass-skate (TMS) bicycle
Stable when….




Stable when front mass mH is within shaded area,   Eigenvalue plot

where


Stable without Gyroscopic or Trail effect!

                                                                     63
Two-mass-skate (TMS) bicycle
Experimental Bicycle




Wheelbase 0.75 m, point masses 2 kg, 99.5 % gyro free, trail -4 mm

                                                                     65
Two-mass-skate (TMS) bicycle
     Basic Experiment




J. D. G. Kooijman, J. P. Meijaard, Jim M. Papadopoulos, Andy Ruina, and A. L. Schwab (2011) A bicycle can be self-stable
       without gyroscopic or caster effects, Science 15 April: 332(6027), 339-342.

                                                                                                             68
End of story?
Rolling backwards, unstable.




                               71
Counterexample
Stable with rear wheel steering

               JBike6




                                  A bicycle with ‘rear wheel
                                  steering’ which shows a stable
                                  forward speed range. The
                                  steer axis is just in front of the
                                  rear wheel and is vertical. The
                                  heavy front assembly has a
                                  center of mass in front of the
                                  front wheel.
                                  This rear wheel steered
                                  design has a stable forward
                                  speed range to the right of
                                  the rightmost vertical line,
                                  from 3 m/s to infinity.


                                                     72
Counterexample
Stable with rear wheel steering




                                  73
Counterexample
Stable with rear wheel steering




                                  74
Conclusions
• Bicycles can be selfstable, not necessarily due to
gyros or trail.
• To balance a bicycle it has to steer into the fall.
• Bicycles are mainly controlled by steering, little by
upper body motions.
• We still know very little about how we humans
control the bicycle.


Future Work
• Identify the human controller in bicycling.
• Define and determine handling qualities of a bicycle.


   Thank you for your attention

                    www.bicycle.tudelft.nl                75

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Transport Thursday 24 January 2013 Arend Schwab

  • 1. 1
  • 2. The Art and Science of Bicycling Arend L. Schwab Laboratory for Engineering Mechanics (3mE/PME) Delft University of Technology, The Netherlands Delft University of Technology Challenge the future
  • 3. Acknowledgement TUDelft: Cornell University: UCDavis: Jaap Meijaard Andy Ruina Mont Hubbard Jodi Kooijman Jim Papadopoulos Jason Moore Nick Appelman Andrew Dressel Luke Peterson J. P. Meijaard, Jim M. Papadopoulos, Andy Ruina, A. L. Schwab (2007) Linearized dynamics equations for the balance and steer of a bicycle: a benchmark and review. Proc. R. Soc. A. 463, 1955-1982. J. D. G. Kooijman, J. P. Meijaard, Jim M. Papadopoulos, Andy Ruina, and A. L. Schwab (2011) A bicycle can be self-stable without gyroscopic or caster effects, Science 15 April: 332(6027), 339-342. www.bicycle.tudelft.nl 3
  • 4. 1. Bicycle Dynamics, some observations 4
  • 5. Movie Jour de Fête van Jacques Tati, 1949 5
  • 6. Experiment Yellow Bike in the Car Park, Cornell University, Ithaca, NY. 6
  • 7. Experiment Yellow Bike in the Car Park, Cornell University, Ithaca, NY. 7
  • 9. The Whipple/Carvallo Model (1899) 1899 Francis Whipple 1876-1943 Delft University of Technology Challenge the future
  • 10. The Whipple/Carvallo Model (1899) Modelling Assumptions: • rigid bodies • fixed rigid rider • hands-free • symmetric about vertical plane • knife-edge wheels • point contact, no side slip • flat level road • no friction or propulsion 3 velocity degrees of freedom Note: Energy Conservative 10
  • 11. Linearized Eqn’s of Motion For the straight ahead upright motion with lean angle ϕ, steering angle δ and forward speed v : ϕ   ϕ   2 ϕ  [M]   + [C1v ]    + [K 0 + K 2v ]   = 0 δ  δ  δ  Standard bicycle + rider : v = 0 130 −3   0 −40  −1003 27  0 −96  M = =   −3 0.3 , C1  , K0 =  , K2   0.6 1.8   27 −8.8    0 2.7  Assume motions: ϕ = ϕ0e λt , δ δ 0e λt = Characteristic equation : det(λ 2 [M] + λ [C1v ] + [K 0 + K 2v 2 ]) = 0 leads to a fourth order characteristic polynomial in eigenvalues λ: λ 4 + a3 (v )λ 3 + a2 (v )λ 2 + a1 (v )λ + a 0 (v ) = 0 12
  • 12. The Whipple/Carvallo Model (1899) det(λ 2 [M] + λ [C1v ] + [K 0 + K 2v 2 ]) = 0 3 velocity degrees of freedom: - lean rate ϕ  - steer rate δ - forward speed v selfstable: 4.6 < v < 7.9 m/s = ϕ0e λt , δ δ 0e λt ϕ = 13
  • 14. Experimental Validation Instrumented Bicycle, uncontrolled 2 rate gyros: -lean rate ϕ  -yaw rate ψ 1 speedometer: -forward speed v 1 potentiometer -steering angle δ Laptop Computer running LabVIEW 19
  • 17. Measured Data  c 1 + e λ1t [c 2 cos( Fit function for the lean rate: ϕ = λ2t ) + c 3 sin(λ2t )] 22
  • 18. Compare with Linearized Results λ2 = 5.52 [rad/s] λ1 = -1.22 [rad/s] forward speed: 4.9 < v <5.4 [m/s] 23
  • 19. Compare in a broad speed range Conclusion: Experimental data in good agreement with linearized analysis on 3 dof model. 24
  • 21. Selfstable: automagic control? How do we balance We balance an inverted pendulum by a bicycle? accelerating the support in the direction of the fall. 26
  • 22. Selfstable: automagic control? δ a v We balance an inverted pendulum by Balance the bicycle by steer into the fall! accelerating the support in the ( lateral acceleration contact point: a ≈ v2/w δ ) direction of the fall. 27
  • 23. Anecdote LEGO Mindstorms NXT Bicycle, built by Joep Mutsaerts, MSc TUDelft 28
  • 24. Automagic control? Steer-into-the-fall ! Control Law: SteerMotorVoltage=8*LeanRate LEGO Mindstorms NXT Bicycle, built by Joep Mutsaerts, MSc TUDelft 29
  • 25. A selfstable bicycle Yellow Bike in the Car Park (slow motion), Cornell University, Ithaca, NY. 30
  • 26. A bicycle is selfstable because …. Gyroscopic effect of the front wheel? Trail on the front wheel? (Klein & Sommerfeld 1910) (David Jones 1970) 5 km/h 25 km/h 31
  • 28. How do we control the mostly unstable bicycle? by steer and balance 33
  • 29. How do we steer and balance? • A. van Lunteren and H. G. Stassen. On the variance of the bicycle rider’s behavior. In Proceedings of the 6th Annual Conference on Manual Control, April 1970. • David Herbert Weir. Motorcycle Handling Dynamics and Rider Control and the Effect of Design Configuration on Response and Performance. PhD thesis, University of California, LA, 1972. 36
  • 30. Relevance 80% elderly 75% single vehicle Number of seriously injured Year 60% of the accidents are among the elderly, they just fall over ! 37
  • 31. A Ride into Town Measure rider control on an instrumented bicycle 3 rate gyros: Lean, Yaw and Steer 1 steer angle potentiometer 2 forward speed 1 pedal cadence pickup 1 video camera Compac Rio data collection 38
  • 32. A ride into Town 39
  • 33. A Ride into Town 40
  • 34. Treadmill experiments Vrije Universiteit Amsterdam , 3 x 5 m treadmill, vmax=35 km/h 41
  • 36. Rider Control Observations Treadmill Experiments Camera Bicycle – Normal Cycling, Pedaling 43
  • 37. Rider Control Observations Treadmill Experiments Camera Bicycle – Towing 44
  • 38. Rider Control Full Human Motion Capture, Optotrack active marker system • 31 markers xyz-coor. • Sample freq 100 Hz • Sample time 1 min One run is 600,000 numbers. Data reduction by Principal Component Analysis (PCA). 45
  • 39. Rider Control Full Human Motion Capture, Optotrack active marker system 3 x 5 m treadmill at the Vrije Universiteit Amsterdam 46
  • 40. Rider Control Treadmill Experiments Full Human Motion Capture - Normal Cycling, Pedaling 47
  • 41. Rider Control Treadmill Experiments, Full Human Motion Capture – PCA Motion Groups 48
  • 42. Rider Control Treadmill Experiments, Full Human Motion Capture - Compare 5 km/h 25 km/h 49
  • 43. Rider Control: Conclusions • During normal bicycling the dominant upper body motions: lean, bend, twist and bounce, are all linked to the pedaling motion. • We hypothesize that lateral control is mainly done by steering since we observed only upper body motion in the pedaling frequency. • If upper body motions are used for control then this control is in the pedaling frequency. • When pedaling at low speed we observe lateral knee motions which are probably also used for control. 50
  • 45. A bicycle is selfstable because …. Gyroscopic effect of the front wheel? Trail on the front wheel? 5 km/h 25 km/h 52
  • 46. Gyroscopic Effect? Klein & Sommerfeld 1910 Felix Klein Arnold Sommerfeld (1849-1925) (1868-1951) from the Klein bottle 81 Nobel prize nominations Fritz Noether (1884-1941) brother of Emmy 53
  • 47. Gyroscopic Effect? Klein & Sommerfeld 1910 Two sign errors: Two sign errors corrected. 56
  • 49. Trail? Jones 1970 Jones calculated handlebar torque via the derivative of system gravitational (potential) energy H with respect to steer angle at fixed lean. But careful analysis on the full dynamical system (linearized equations of motion) shows: 58
  • 50. Two-mass-skate (TMS) bicycle Only 8 bicycle parameters Two point masses and wheels are replaced by skates Only 8 non-zero bicycle parameters 61
  • 51. Two-mass-skate (TMS) bicycle Stable when…. Stable when front mass mH is within shaded area, Eigenvalue plot where Stable without Gyroscopic or Trail effect! 63
  • 52. Two-mass-skate (TMS) bicycle Experimental Bicycle Wheelbase 0.75 m, point masses 2 kg, 99.5 % gyro free, trail -4 mm 65
  • 53. Two-mass-skate (TMS) bicycle Basic Experiment J. D. G. Kooijman, J. P. Meijaard, Jim M. Papadopoulos, Andy Ruina, and A. L. Schwab (2011) A bicycle can be self-stable without gyroscopic or caster effects, Science 15 April: 332(6027), 339-342. 68
  • 54. End of story? Rolling backwards, unstable. 71
  • 55. Counterexample Stable with rear wheel steering JBike6 A bicycle with ‘rear wheel steering’ which shows a stable forward speed range. The steer axis is just in front of the rear wheel and is vertical. The heavy front assembly has a center of mass in front of the front wheel. This rear wheel steered design has a stable forward speed range to the right of the rightmost vertical line, from 3 m/s to infinity. 72
  • 56. Counterexample Stable with rear wheel steering 73
  • 57. Counterexample Stable with rear wheel steering 74
  • 58. Conclusions • Bicycles can be selfstable, not necessarily due to gyros or trail. • To balance a bicycle it has to steer into the fall. • Bicycles are mainly controlled by steering, little by upper body motions. • We still know very little about how we humans control the bicycle. Future Work • Identify the human controller in bicycling. • Define and determine handling qualities of a bicycle. Thank you for your attention www.bicycle.tudelft.nl 75