From b6f1da05e498a5dbcc6eb837ac5f697407da82d4 Mon Sep 17 00:00:00 2001 From: "Jason K. Moore" Date: Mon, 3 Jun 2024 22:23:52 +0200 Subject: [PATCH] Update to Lagrange chapter for 2024. --- lagrange.rst | 693 ++++++++++++++++++++++++++++--------------------- notation.rst | 57 ++-- references.rst | 26 +- 3 files changed, 444 insertions(+), 332 deletions(-) diff --git a/lagrange.rst b/lagrange.rst index a7f29940..195fe873 100644 --- a/lagrange.rst +++ b/lagrange.rst @@ -39,41 +39,39 @@ Learning Objectives After completing this chapter readers will be able to: -- Derive the Lagrangian for a system of particles and rigid bodies +- Derive the Lagrangian for a system of interconnected particles and rigid bodies - Use the Euler-Lagrange equation to derive equations of motions given a Lagrangian - Use the method of Lagrange multipliers to add constraints to the equations of motions -- Know how to determine the generalized momenta of a system. +- Determine the generalized momenta of a system Introduction ============ -This book has already discussed two methods to derive the equations -of motion of multibody systems: Newton-Euler and Kane's method. This -chapter will add a third: the `Lagrange method`_, originally -developed by Joseph-Louis Lagrange. These materials focus on Engineering -applications for multi-body systems, and therefore build the Lagrange method around -the terms found earlier in Kane's equations. In other textbooks, the Lagrange method -is often derived from the `Variational principles`_, such as virtual work or the principle -of least action. A good starting point for studying the physical -and mathematical background of the Lagrange approach is [Lanczos1970]_. - +This book has already discussed two methods to derive the equations of motion +of multibody systems: Newton-Euler and Kane's method. This chapter will add a +third: the `Lagrange method`_, originally developed by `Joseph-Louis +Lagrange`_. These materials focus on Engineering applications for multibody +systems, and therefore build the Lagrange method around the terms found earlier +in Kane's equations. In other textbooks, the Lagrange method is often derived +from the `Variational principles`_, such as virtual work or the principle of +least action. A good starting point for studying the physical and mathematical +background of the Lagrange approach is [Lanczos1986]_. + +.. _Joseph-Louis Lagrange: https://en.wikipedia.org/wiki/Joseph-Louis_Lagrange .. _Variational principles: https://en.wikipedia.org/wiki/Variational_principle .. _`Lagrange method`: https://en.wikipedia.org/wiki/Lagrangian_mechanics - - -Inertial forces from kinetic energy +Inertial Forces from Kinetic Energy =================================== -In Kane's method the negated generalized inertial -forces equal the applied forces, see :ref:`Unconstrained Equations of Motion`. -A large part of Kane's method of deriving the equations of motions for a -system is involved with finding the generalized inertial forces. - -As an alternative, the following equation also calculates the generalized inertial forces of a -system, now by starting from the kinetic energy :math:`K (\dot{\bar{q}}, \bar{q})` -expressed as function of the generalized coordinates :math:`\bar{q}`, and -their time derivatives. See :ref:`Energy and Power` for the definition of kinetic energy. +In Kane's method the negated generalized inertial forces equal the applied +forces, see :ref:`Unconstrained Equations of Motion`. A large part of Kane's +method of deriving the equations of motions for a system is involved with +finding the generalized inertial forces. As an alternative, the following +equation also calculates the generalized inertial forces of a system, now by +starting from the kinetic energy :math:`K (\dot{\bar{q}}, \bar{q})` expressed +as function of the generalized coordinates :math:`\bar{q}`, and their time +derivatives. See :ref:`Energy and Power` for the definition of kinetic energy. .. math:: :label: eq-lagrange-inertial @@ -90,89 +88,104 @@ their time derivatives. See :ref:`Energy and Power` for the definition of kineti :math:`\bar{u} = \dot{\bar{q}}` is assumed when using the Lagrange method. Therefore, throughout this chapter :math:`\dot{\bar{q}}` is used. -The generalized inertial forces computed in this manner are the same as when following -Kane's method, or the TMT method used in the next chapter. This can be shown by carefully -matching terms in these formulations, as is done for a a system of point-masses in [Vallery2020]_. +The generalized inertial forces computed in this manner are the same as when +following Kane's method or the TMT method, used in the next chapter. This can +be shown by carefully matching terms in these formulations, as is done for a +system of point-masses in [Vallery2020]_. -Example: freely moving 3D body ------------------------------- +Example: Torque Free Rigid Body +------------------------------- -This example is largely the same as the example in :ref:`Body Fixed Newton-Euler Equations`. A key difference -is a difference between the generalized speeds describing the rotation. In the calculation with Kane's method, -they were body-fixed angular velocities, whereas here they are the rates of change of the Euler angles. +This example is largely the same as the example in :ref:`Body Fixed +Newton-Euler Equations`. A key difference is a difference between the +generalized speeds describing the rotation. In the calculation with Kane's +method, they were body-fixed angular velocities, whereas here they are the +rates of change of the Euler angles. -First, set up the generalized coordinates, reference frames and mass properties: +First, set up the generalized coordinates, reference frames, and mass +properties for a single rigid body located by coordinates :math:`x,y,z` from +point :math:`O` and oriented by Euler angles :math:`\psi,\theta,\phi` relative +to the inertial reference frame :math:`N`: .. jupyter-execute:: t = me.dynamicsymbols._t - psi,theta, phi, x, y, z = me.dynamicsymbols('psi theta phi x y z') + m, Ixx, Iyy, Izz = sm.symbols('m, I_{xx}, I_{yy}, I_{zz}') + psi, theta, phi, x, y, z = me.dynamicsymbols('psi, theta, phi, x, y, z') + q = sm.Matrix([psi, theta, phi, x, y, z]) qd = q.diff(t) qdd = qd.diff(t) + + q, qd, qdd + +.. jupyter-execute:: + N = me.ReferenceFrame('N') B = me.ReferenceFrame('B') B.orient_body_fixed(N, (psi, theta, phi), 'zxy') - m, Ixx, Iyy, Izz = sm.symbols('M, I_{xx}, I_{yy}, I_{zz}') + I_B = me.inertia(B, Ixx, Iyy, Izz) - q -Then compute the kinetic energy: +Then compute the kinetic energy: .. jupyter-execute:: N_w_B = B.ang_vel_in(N) - r_O_P = x*N.x + y*N.y + z*N.z - N_v_C = r_O_P.dt(N) - K = N_w_B.dot(I_B.dot(N_w_B))/2 + m*N_v_C.dot(N_v_C)/2 + r_O_Bo = x*N.x + y*N.y + z*N.z + N_v_C = r_O_Bo.dt(N) + K = m*N_v_C.dot(N_v_C)/2 + N_w_B.dot(I_B.dot(N_w_B))/2 K -Use the kinetic energy to find the generalized inertial forces. Here we start with -the generalized coordinate :math:`\psi` +Use the kinetic energy to find the generalized inertial forces. Here we, as an +example, start with the generalized coordinate :math:`\psi`: .. jupyter-execute:: - psid = psi.diff(t) - F_psi_s = K.diff(psid).diff(t) - K.diff(psi) + F_psi_s = K.diff(psi.diff(t)).diff(t) - K.diff(psi) + F_psi_s -A similar derivation should be made for all generalized coordinates. We could write -a loop, but there there is a method to derive all the equations in one go. -The vector of partial derivatives of a function, that is the gradient, can be created -using the :external:py:meth:`~sympy.matrices.matrices.MatrixCalculus.jacobian` method. The generalized inertial forces can then be found like this: +A similar derivation should be made for all generalized coordinates. We could +write a loop, but there there is a method to derive all the equations in one +go. The vector of partial derivatives of a function, that is the gradient, can +be created using the +:external:py:meth:`~sympy.matrices.matrices.MatrixCalculus.jacobian` method. +The generalized inertial forces can then be found like this: .. jupyter-execute:: K_as_matrix = sm.Matrix([K]) - Fs_transposed = K_as_matrix.jacobian(qd).diff(t) - K_as_matrix.jacobian(q) - Fs = Fs_transposed.transpose() + Fs = (K_as_matrix.jacobian(qd).diff(t) - K_as_matrix.jacobian(q)).transpose() Fs +While these are correct generalized inertia forces, the terms, particularly the +terms involving :math:`\ddot{q}_r` are mangled. It is common to extract the +system mass matrix :math:`\mathbf{M}_d` and velocity related force vector +:math:`\bar{g}_d` like before: -While these are correct equations of motion, the terms, particularly the terms -involving :math:`\ddot{q}_r` are mangled. It is common to extract the system -mass matrix :math:`\mathbf{M}_d` and velocity forces vector :math:`\bar{g}_d` like before: +.. jupyter-execute:: + + Md = Fs.jacobian(qdd) + sm.trigsimp(Md) .. jupyter-execute:: qdd_zerod = {qddr: 0 for qddr in qdd} - Md = Fs.jacobian(qdd) gd = Fs.xreplace(qdd_zerod) - Md.simplify() - gd.simplify() - Md, gd - + sm.trigsimp(gd) Conservative Forces =================== -Recall from :ref:`Energy and Power` that `conservative forces`_, can -be expressed using the gradient of a scalar function of the generalized coordinates, -known as the `potential energy`_ :math:`V(\bar{q})`: +Recall from :ref:`Energy and Power` that `conservative forces`_, can be +expressed using the gradient of a scalar function of the generalized +coordinates, known as the `potential energy`_ :math:`V(\bar{q})`, thus the +conservative generalized active forces can be written as: .. math:: :label: eq-potential-energy - \bar{F}_r = -\frac{\partial V}{\partial q_r} + \bar{F}_r^\textrm{c} = -\frac{\partial V}{\partial q_r} .. warning:: Note the minus sign in the above equation. @@ -181,26 +194,27 @@ known as the `potential energy`_ :math:`V(\bar{q})`: Some examples of conservative forces are: -* a uniform gravitational field, for example on the surface of the earth, with potential :math:`V = m g h(\bar{q})`, -* gravity from Newton's universal gravitation, with potential :math:`V = -G \frac{m_1m_2}{r(\bar{q})}`, -* a linear spring, with potential :math:`V = \frac{1}{2}k(l(\bar{q}) - l_0)^2`. - -For conservative forces, it is often convenient to derive the applied forces via -the potential energy. +- a uniform gravitational field, for example on the surface of the earth, with + potential :math:`V = m g h(\bar{q})`, +- gravity from Newton's universal gravitation, with potential :math:`V = -G + \frac{m_1m_2}{r(\bar{q})}`, +- a linear spring, with potential :math:`V = \frac{1}{2}k(l(\bar{q}) - l_0)^2`. +For conservative forces, it is often convenient to derive the applied forces +via the potential energy. The Lagrange Method =================== -Both the equation for computing the inertial forces from the kinetic energy, and -the equation for computing the applied forces from a potential energy have a term -in them with the partial derivative with respect to the generalized coordinate. -Furtermore, the potential energy does not depend on the generalized speeds. -Therefore, the resulting (inertial and conservative applied) forces can be derived -in one go, by combining the two equations. +Both the equation for computing the inertial forces from the kinetic energy and +the equation for computing the applied conservative forces from a potential +energy have a term in them with the partial derivative with respect to the +generalized coordinate. Furthermore, the potential energy does not depend on +the generalized speeds. Therefore, the resulting (inertial and conservative +applied) forces can be derived in one go, by combining the two equations. -Step 1. Compute the so called `Lagrangian`_ :math:`L`, the difference between the -kinetic energy and potential energy: +Step 1. Compute the so called `Lagrangian`_ :math:`L`, the difference between +the kinetic energy and potential energy: .. math:: :label: eq-lagrangian @@ -209,26 +223,26 @@ kinetic energy and potential energy: .. _`Lagrangian`: https://en.wikipedia.org/wiki/Lagrangian -Step 2. Use the Euler-Lagrange equations (the name for the equation +Step 2. Use the Euler-Lagrange equations (the name for the equation :math:numref:`eq-lagrange-inertial`) to find the equations of motion: .. math:: :label: eq-euler-lagrange \frac{\mathrm{d}}{\mathrm{d}t}\left(\frac{\partial L}{\partial \dot{q}_r} - \right) - \frac{\partial L}{\partial q_r} = F_r, + \right) - \frac{\partial L}{\partial q_r} = F_r^\textrm{nc} + \textrm{ for } r = 1,\ldots,n -while being careful to include a force either in the applied forces -:math:`\bar{F}_r`, or in the potential energy :math:`V`, but never -in both. +while being careful to include conservative applied forces in the potential +energy :math:`V` term, but not in the non-conservative generalized active force +:math:`F_r^\textrm{nc}`. +Example: Unconstrained System +----------------------------- -Example: Double pendulum with springs and sliding pointmass ------------------------------------------------------------ - -This example will use the Lagrange method to derive the equations of motion -for the system introduced in :ref:`Example of Kane's Equations`. The description -of the system is shown again in :numref:`fig-eom-double-rod-pendulum-repeat`. +This example will use the Lagrange method to derive the equations of motion for +the system introduced in :ref:`Example of Kane's Equations`. The description of +the system is shown again in :numref:`fig-eom-double-rod-pendulum-repeat`. .. _fig-eom-double-rod-pendulum-repeat: .. figure:: figures/eom-double-rod-pendulum.svg @@ -248,44 +262,76 @@ is the same as for Kane's method. .. jupyter-execute:: - m, g, kt, kl, l = sm.symbols('m, g, k_t, k_l, l') - q1, q2, q3 = me.dynamicsymbols('q1, q2, q3') + m, g, kt, kl, l = sm.symbols('m, g, k_t, k_l, l') + q1, q2, q3 = me.dynamicsymbols('q1, q2, q3') + t = me.dynamicsymbols._t + + q = sm.Matrix([q1, q2, q3]) + qd = q.diff(t) + qdd = qd.diff(t) - N = me.ReferenceFrame('N') - A = me.ReferenceFrame('A') - B = me.ReferenceFrame('B') + N = me.ReferenceFrame('N') + A = me.ReferenceFrame('A') + B = me.ReferenceFrame('B') - A.orient_axis(N, q1, N.z) - B.orient_axis(A, q2, A.x) + A.orient_axis(N, q1, N.z) + B.orient_axis(A, q2, A.x) - O = me.Point('O') - Ao = me.Point('A_O') - Bo = me.Point('B_O') - Q = me.Point('Q') + O = me.Point('O') + Ao = me.Point('A_O') + Bo = me.Point('B_O') + Q = me.Point('Q') - Ao.set_pos(O, l/2*A.x) - Bo.set_pos(O, l*A.x) - Q.set_pos(Bo, q3*B.y) + Ao.set_pos(O, l/2*A.x) + Bo.set_pos(O, l*A.x) + Q.set_pos(Bo, q3*B.y) - O.set_vel(N, 0) + O.set_vel(N, 0) - I = m*l**2/12 - I_A_Ao = I*me.outer(A.y, A.y) + I*me.outer(A.z, A.z) - I_B_Bo = I*me.outer(B.x, B.x) + I*me.outer(B.z, B.z) + I = m*l**2/12 + I_A_Ao = I*me.outer(A.y, A.y) + I*me.outer(A.z, A.z) + I_B_Bo = I*me.outer(B.x, B.x) + I*me.outer(B.z, B.z) -Then, set up the Lagrangian: +Start by defining the kinetic energy for each rigid body and particle: .. jupyter-execute:: - t = sm.symbols('t') - q = sm.Matrix([q1, q2, q3]) - qd = q.diff(t) - qdd = qd.diff(t) + KA = m*Ao.vel(N).dot(Ao.vel(N))/2 + A.ang_vel_in(N).dot(I_A_Ao.dot(A.ang_vel_in(N)))/2 + KA - K = m/2*(Ao.vel(N).dot(Ao.vel(N)) + Bo.vel(N).dot(Bo.vel(N)) + Q.vel(N).dot(Q.vel(N))) + ( - A.ang_vel_in(N).dot(I_A_Ao.dot(A.ang_vel_in(N))) + B.ang_vel_in(N).dot(I_B_Bo.dot(B.ang_vel_in(N))) - )/2 - V = m*g*(Ao.pos_from(O).dot(-N.x) + Bo.pos_from(O).dot(-N.x)) + kt/2*(q1**2) + kt/2*q2**2 + kl/2*q3**2 +.. jupyter-execute:: + + KB = m*Bo.vel(N).dot(Bo.vel(N))/2 + B.ang_vel_in(N).dot(I_B_Bo.dot(B.ang_vel_in(N)))/2 + KB + +.. jupyter-execute:: + + KQ = m/4*Q.vel(N).dot(Q.vel(N))/2 + KQ + +.. jupyter-execute:: + + K = KA + KB + KQ + +Form the potential energy from the conservative gravitational and spring forces: + +.. jupyter-execute:: + + V_grav = m*g*(Ao.pos_from(O) + Bo.pos_from(O)).dot(-N.x) + m/4*g*Q.pos_from(O).dot(-N.x) + V_grav + +.. jupyter-execute:: + + V_springs = kt/2*q1**2 + kt/2*q2**2 + kl/2*q3**2 + V_springs + +.. jupyter-execute:: + + V = V_grav + V_springs + +The Lagrangian is then: + +.. jupyter-execute:: L = sm.Matrix([K - V]) sm.trigsimp(L) @@ -294,31 +340,42 @@ Finally, derive the equations of motion: .. jupyter-execute:: - left_hand_side = L.jacobian(qd).diff(t) - L.jacobian(q) + fd = -(L.jacobian(qd).diff(t) - L.jacobian(q)).transpose() qdd_zerod = {qddr: 0 for qddr in qdd} - Md = left_hand_side.jacobian(qdd) - gd = left_hand_side.xreplace(qdd_zerod) + Md = fd.jacobian(qdd) + gd = sm.trigsimp(fd.xreplace(qdd_zerod)) me.find_dynamicsymbols(Md), me.find_dynamicsymbols(gd) -The mass matrix :math:`\mathbf{M}_d` only depends on :math:`\bar{q}`, and :math:`\bar{g}_d` depends -on :math:`\dot{\bar{q}}` and :math:`\bar{q}`, just as in Kane's method. Note that :math:`\bar{g}_d` now -combines the effects of the velocity force vector and the conservative forces. In this setting, -:math:`\bar{g}_d` is often called the dynamic bias. +.. jupyter-execute:: + + Md + +.. jupyter-execute:: + + gd -It is often useful to use a vector of intermediate variables when finding the Euler-Lagrange equations. The variables -are defined as: +The mass matrix :math:`\mathbf{M}_d` only depends on :math:`\bar{q}`, and +:math:`\bar{g}_d` depends on :math:`\dot{\bar{q}}` and :math:`\bar{q}`, just as +in Kane's method. Note that :math:`\bar{g}_d` now combines the effects of the +velocity related force vector and the conservative forces. In this setting, +:math:`\bar{g}_d` is often called the dynamic bias. + +Generalized Momenta +=================== + +It is often useful to use a vector of intermediate variables when finding the +Euler-Lagrange equations. The variables are defined as: .. math:: p_r = \frac{\partial L}{\partial \dot{q_r}} -The variables are collected in a vector :math:`\bar{p}`. - -They are called the generalized momenta, -as they coincide with linear momentum in the case of a Lagrangian describing a particle. -Similar to the situation in the dynamics of particles, there can -be conservation of generalized momentum. This is the case for the generalized momentum -associated with ignorable coordinates, as defined in :ref:`Equations of Motion with Nonholonomic Constraints`. +The variables are collected in a vector :math:`\bar{p}`. They are called the +generalized momenta, as they coincide with linear momentum in the case of a +Lagrangian describing a particle. Similar to the situation in the dynamics of +particles, there can be conservation of generalized momentum. This is the case +for the generalized momentum associated with ignorable coordinates, as defined +in :ref:`Equations of Motion with Nonholonomic Constraints`. For the example pendulum, the generalized momenta are calculated as: @@ -327,83 +384,104 @@ For the example pendulum, the generalized momenta are calculated as: p = L.jacobian(qd).transpose() sm.trigsimp(p) - -Constrained equations of motion +Constrained Equations of Motion =============================== -When using Kane's method, constraints are handled by dividing the generalized speeds into two sets: -the dependent and independent generalized speeds. Depending on the type of constraints, the -dependent generalized speeds are eliminated by solving the constraint equation (for non-holonomic -constraints) or the time derivative of the constraint equation (holonomic constraints). Kane's -method only gives rise to :math:`p = n - m` dynamical equations, one for each independent generalized -speed. - -The Lagrange method gives rise to :math:`n` dynamical equations, one for each generalized coordinate. -To eliminate the constraints, and end up with the right number of equations (:math:`n - m`, one for -each degree of freedom), both the generalized speeds and the generalized coordinates should be solved -using the constraint equation. For non-holonomic constraints, this elimination is not possible (by the -definition of non-holonomic), and for holonomic constraints this elimination requires solving often -difficult non-linear equations for the generalized coordinates. The method of elimination is therefore -not useful within the Lagrange method. - -Instead, constraints are handled using a generalized version of the approach in -:ref:`Exposing Noncontributing Forces`. First the constraints are omitted. Then a constraint force is added, -with a known direction, but unknown magnitude. Finally, the (second) time derivative of the constraint -equation is then appended to the equations found with the Euler-Lagrange equation. - -For example, consider a particle of mass :math:`m` and position -:math:`\bar{r}^{P/O} = q_1 \hat{n}_x + q_2 \hat{n}_y + q_3\hat{n}_z` -on a slope :math:`q_1 = q_2`. The unconstrained Lagrangian is -:math:`L = \frac{1}{2}m(\dot{q}_1^2 + \dot{q}_2^2 + \dot{q}_3^2) - mgq_3`. -The constraint force is perpendicular to the slope, so is described -as :math:`\bar{F} = F\hat{n}_x - F\hat{n}_y`. The appended equation is -the second time derivative of the constraint equation :math:`\ddot{q_1} - \ddot{q_2} = 0`. -Combining all, gives: +When using Kane's method, constraints are handled by dividing the generalized +speeds into two sets: the dependent and independent generalized speeds. +Depending on the type of constraints, the dependent generalized speeds are +eliminated by solving the constraint equation (for nonholonomic constraints) +or the time derivative of the constraint equation (holonomic constraints). +Kane's method only gives rise to :math:`p = n - m` dynamical equations, one for +each independent generalized speed. The Lagrange method gives rise to :math:`N` +dynamical equations, one for each coordinate plus `M + m` additional equations +to manage the constraints. + +The constraints are handled using a generalized version of the approach in +:ref:`Exposing Noncontributing Forces`. First the constraints are omitted, then +a constraint force is added, with a known direction, but unknown magnitude. +Finally, the (second) time derivative of the constraint equation is then +appended to the equations found with the Euler-Lagrange equation. + +For example, consider a particle of mass :math:`m` with position +:math:`\bar{r}^{P/O} = q_1 \hat{n}_x + q_2 \hat{n}_y + q_3\hat{n}_z` on a slope +:math:`q_1 = q_2` with gravity pulling the mass down the slope. The +unconstrained Lagrangian is :math:`L = \frac{1}{2}m(\dot{q}_1^2 + \dot{q}_2^2 + +\dot{q}_3^2) - mgq_3`. The constraint force is perpendicular to the slope, so +is described as :math:`\bar{F} = F\hat{n}_x - F\hat{n}_y`. The appended +equation is the second time derivative of the constraint equation +:math:`\ddot{q_1} - \ddot{q_2} = 0`. Combining all, gives: .. math:: - \begin{array}{r} - m\ddot{q}_1= \phantom{-}F\\ - m\ddot{q}_2= -F\\ - m\ddot{q}_3 + mg = \phantom{-}0\\ - \ddot{q}_1 - \ddot{q}_2\!\! = \phantom{-}0 - \end{array} -This can be put in matrix-form, by extracting the unknown acceleration and force magnitude: + \begin{array}{r} + m\ddot{q}_1= \phantom{-}F\\ + m\ddot{q}_2= -F\\ + m\ddot{q}_3 + mg = \phantom{-}0\\ + \ddot{q}_1 - \ddot{q}_2\!\! = \phantom{-}0 + \end{array} -.. math:: - \begin{bmatrix} m & 0 & 0 &-1 \\ 0 & m & 0 & 1 \\ 0 & 0 & m & 0 \\ 1 & -1 & 0 & 0\end{bmatrix} - \begin{bmatrix} \ddot{q}_1 \\ \ddot{q}_2 \\ \ddot{q}_3 \\ F \end{bmatrix} = \begin{bmatrix} 0 \\ 0 \\ -mg \\ 0\end{bmatrix} +This can be put in matrix-form, by extracting the unknown acceleration and +force magnitude: +.. math:: -It can be challenging to find the direction of the constraint force from the geometry of the system directly. -There is a trick, called the method of the `Lagrange multipliers`_, to quickly find the correct generalized -forces associated with the constraint forces. - -.. _`Lagrange multipliers`: https://en.wikipedia.org/wiki/Lagrange_multiplier - -Given a motion constraint (time derivatives of configuration constraint or a nonholonomic constraint) in the general form + \begin{bmatrix} + m & 0 & 0 &-1 \\ + 0 & m & 0 & 1 \\ + 0 & 0 & m & 0 \\ + 1 & -1 & 0 & 0 + \end{bmatrix} + \begin{bmatrix} + \ddot{q}_1 \\ + \ddot{q}_2 \\ + \ddot{q}_3 \\ + F + \end{bmatrix} = + \begin{bmatrix} + 0 \\ + 0 \\ -mg \\ 0 + \end{bmatrix} + +It can be challenging to find the direction of the constraint force from the +geometry of the system directly. There is a trick, called the method of the +`Lagrange multipliers`_, to quickly find the correct generalized forces +associated with the constraint forces. + +.. _`Lagrange multipliers`: https://en.wikipedia.org/wiki/Lagrange_multiplier + +Given a motion constraint (time derivatives of configuration constraint or a +nonholonomic constraint) in the general form + +.. todo:: Use same notation as my constraint chapters. .. math:: \sum_r a_r(\bar{q}) \dot{q}_r = 0 -The generalized force is found as: +The generalized constraint force is found as: + +.. todo:: Reconsider using Fr here, maybe Cr would make it distinct from + generalized active forces. .. math:: F_r = \lambda a_r(\bar{q}) -Here :math:`\lambda` is a variable encoding the magnitude of the constraint force. It is -called the Lagrange multiplier. The same :math:`\lambda` is used for each :math:`r`, that is, -each constraint has a single associated Lagrange multiplier. +Here :math:`\lambda` is a variable encoding the magnitude of the constraint +force. It is called the Lagrange multiplier. The same :math:`\lambda` is used +for each :math:`r`, that is, each constraint has a single associated Lagrange +multiplier. -Due to how it is constructed, the power produced by the constraint force is always zero, as expected. +Due to how it is constructed, the power produced by the constraint force is +always zero, as expected. .. math:: - P = \sum_r F_r\dot{q}_r = \sum \lambda a_r(\bar{q})\dot{q}_r = \lambda \sum a_r(\bar{q})\dot{q}_r = \lambda \cdot 0 + P = \sum_r F_r\dot{q}_r = \sum \lambda a_r(\bar{q})\dot{q}_r = + \lambda \sum a_r(\bar{q})\dot{q}_r = \lambda \cdot 0 -For example, consider the pointmass to be constrained to move in a bowl +For example, consider the point mass to be constrained to move in a bowl :math:`q_1^2 + q_2^2 + q_3^2 -1 = 0`, :numref:`fig-lagrange-bowl`. Taking the time derivative gives: :math:`a_1 = 2q_1`, :math:`a_2 = 2q_2`, and :math:`a_3 = 2q_3`. This results in generalized reaction forces :math:`F_1 = 2\lambda q_1`, @@ -416,13 +494,15 @@ time derivative gives: :math:`a_1 = 2q_1`, :math:`a_2 = 2q_2`, and :math:`a_3 = Point mass :math:`P` constrained to the surface of a spherical bowl with radius :math:`1` and constraint force measure numbers :math:`F_1,F_2,F_3`. -Often, there are multiple constraints on the same system. For convenience, the handling of these constraints can be combined. -Consider the :math:`m+M` dimensional general constraint equations consisting of the time derivatives of the holonomic constraints -and/or the non-holonomic constraints: +Often, there are multiple constraints on the same system. For convenience, the +handling of these constraints can be combined. Consider the :math:`m+M` +dimensional general constraint equations consisting of the time derivatives of +the holonomic constraints and/or the nonholonomic constraints: .. math:: - \bar{f}_{hn}(\bar{q}, \bar{\dot{q}}) = \mathbf{M}_{hn}\bar{\dot{q}} = 0, + \bar{f}_{hn}(\dot{\bar{q}}, \bar{q}) = + \mathbf{M}_{hn}\dot{\bar{q}} + \bar{g}_{hn} = 0 \in \mathbb{R}^{M+m} the combined constraint forces are given as: @@ -430,72 +510,76 @@ the combined constraint forces are given as: \bar{F}_r = \mathbf{M}_{hn}^\text{T}\bar{\lambda}, -where :math:`\bar{\lambda}` is a vector of :math:`m + M` Lagrange multipliers, one for each constraint (row in :math:`\mathbf{M}_{hn}`). - +where :math:`\bar{\lambda}` is a vector of :math:`m + M` Lagrange multipliers, +one for each constraint (row in :math:`\mathbf{M}_{hn}`). -Example: turning the freely floating body discussed earlier into a rolling sphere. ----------------------------------------------------------------------------------- +Example: Turn the Torque Free Rigid Body into a Rolling Sphere +-------------------------------------------------------------- -The non-slip condition of the rolling sphere is split into three constraints: the velocity of -the contact point (:math:`G`) is zero in the :math:`\hat{n}_x`, the :math:`\hat{n}_y` and the :math:`\hat{n}_z` -direction. The first two constraints are non-holonomic, the last constraint is the time derivative of -a holonomic constraint. All three constraints are enforced by contact forces in their respective directions. +The non-slip condition of the rolling sphere is split into three constraints: +the velocity of the contact point (:math:`G`) is zero in the :math:`\hat{n}_x`, +the :math:`\hat{n}_y`, and the :math:`\hat{n}_z` directions. The first two +constraints are nonholonomic, the last constraint is the time derivative of a +holonomic constraint. All three constraints are enforced by contact forces in +their respective directions. -The contact point can be found according by :math:`\bar{r}^{G/C} = -r \hat{n}_z`. By using the :ref:`Velocity -Two Point Theorem`, the following constraints are found. +The contact point can be found according by :math:`\bar{r}^{G/B_o} = -r +\hat{n}_z`. By using the :ref:`Velocity Two Point Theorem`, the following +constraints are found. .. math:: - \begin{array}{l} - \bar{n}_x\cdot ({}^N\bar{v}^C + {}^N\bar{\omega}^B \times (-r\hat{n}_z)) = 0 \\ - \bar{n}_y\cdot ({}^N\bar{v}^C + {}^N\bar{\omega}^B \times (-r\hat{n}_z)) = 0 \\ - \bar{n}_z\cdot ({}^N\bar{v}^C + {}^N\bar{\omega}^B \times (-r\hat{n}_z)) = 0 \\ - \end{array} + \begin{array}{l} + \bar{n}_x\cdot ({}^N\bar{v}^{B_o} + {}^N\bar{\omega}^B \times (-r\hat{n}_z)) = 0 \\ + \bar{n}_y\cdot ({}^N\bar{v}^{B_o} + {}^N\bar{\omega}^B \times (-r\hat{n}_z)) = 0 \\ + \bar{n}_z\cdot ({}^N\bar{v}^{B_o} + {}^N\bar{\omega}^B \times (-r\hat{n}_z)) = 0 \\ + \end{array} -These can be used to derive the constraint force and the additional equations using the Lagrange-multiplier -method as shown below. Note that here only the first time derivative of the constraint equation is used, -again because the second time derivatives of the generalized coordinates appear. +These can be used to derive the constraint force and the additional equations +using the Lagrange-multiplier method as shown below. Note that here only the +first time derivative of the constraint equation is used, again because the +second time derivatives of the generalized coordinates appear. .. admonition:: Frames and Body Setup :class: dropdown - Setting up reference frames - - .. jupyter-execute:: + Setting up reference frames - psi,theta, phi, x, y, z = me.dynamicsymbols('psi theta phi x y z') - N = me.ReferenceFrame('N') - B = me.ReferenceFrame('B') - B.orient_body_fixed(N, (psi, theta, phi), 'zxy') + .. jupyter-execute:: - # Mass and inertia - m, Ixx, Iyy, Izz = sm.symbols('M, I_{xx}, I_{yy}, I_{zz}') - I_B = me.inertia(B, Ixx, Iyy, Izz) + psi,theta, phi, x, y, z = me.dynamicsymbols('psi theta phi x y z') + N = me.ReferenceFrame('N') + B = me.ReferenceFrame('B') + B.orient_body_fixed(N, (psi, theta, phi), 'zxy') - Finding the kinetic energy: + # Mass and inertia + m, Ixx, Iyy, Izz = sm.symbols('M, I_{xx}, I_{yy}, I_{zz}') + I_B = me.inertia(B, Ixx, Iyy, Izz) - .. jupyter-execute:: + Finding the kinetic energy: - omega_B = B.ang_vel_in(N) - r_com = x*N.x + y*N.y + z*N.z - v_com = r_com.dt(N) - K = omega_B.dot(I_B.dot(omega_B))/2 + m*v_com.dot(v_com)/2 + .. jupyter-execute:: - Deriving equations of motion: + omega_B = B.ang_vel_in(N) + r_com = x*N.x + y*N.y + z*N.z + v_com = r_com.dt(N) + K = omega_B.dot(I_B.dot(omega_B))/2 + m*v_com.dot(v_com)/2 - .. jupyter-execute:: + Deriving equations of motion: - t = me.dynamicsymbols._t - q = sm.Matrix([psi, theta, phi, x, y, z]) - qd = q.diff(t) - qdd = qd.diff(t) + .. jupyter-execute:: + + t = me.dynamicsymbols._t + q = sm.Matrix([psi, theta, phi, x, y, z]) + qd = q.diff(t) + qdd = qd.diff(t) - L = sm.Matrix([K]) - left_hand_side = L.jacobian(qd).diff(t) - L.jacobian(q) + L = sm.Matrix([K]) + fd = L.jacobian(qd).diff(t) - L.jacobian(q) - qdd_zerod = {qddr: 0 for qddr in qdd} - Md = left_hand_side.jacobian(qdd) - gd = left_hand_side.xreplace(qdd_zerod) + qdd_zerod = {qddr: 0 for qddr in qdd} + Md = fd.jacobian(qdd) + gd = fd.xreplace(qdd_zerod) To make this free floating body a rolling wheel, three constraints are needed: the velocity of the contact point should be zero in :math:`\hat{n}_x`, :math:`\hat{n}_y` @@ -503,83 +587,110 @@ and :math:`\hat{n}_x` direction. .. jupyter-execute:: - lambda1, lambda2, lambda3 = me.dynamicsymbols('lambda1, lambda2, lambda3') - constraint = (v_com + B.ang_vel_in(N).cross(-N.z)).to_matrix(N) - M_hn = constraint.jacobian(qd) - diff_constraint = constraint.diff(t) - sm.trigsimp(constraint) + r = sm.symbols('r') + lambda1, lambda2, lambda3 = me.dynamicsymbols('lambda1, lambda2, lambda3') + + constraint = (v_com + B.ang_vel_in(N).cross(-r*N.z)).to_matrix(N) + sm.trigsimp(constraint) + +The Jacobian of the constraints with respect to :math:`\dot{\bar{q}}` is then: + +.. jupyter-execute:: + + Mhn = constraint.jacobian(qd) + sm.trigsimp(Mhn) This constraint information must then be added to the original equations. To do -so, we make use of a useful fact. +so, we make use of a useful fact: .. jupyter-execute:: - diff_constraint.jacobian(qdd) - M_hn + diff_constraint = constraint.diff(t) + diff_constraint.jacobian(qdd) - Mhn -This equality is true for all constraints, as can easily be shown by taking the time -derivative of the constraint equation, using the chain rule. +This equality is true for all constraints, as can easily be shown by taking the +time derivative of the constraint equation, using the chain rule. The combined equations can now be written in a block matrix form: .. math:: - \begin{bmatrix} \mathbf{M}_d & -\mathbf{M}_{hn}^T \\ \mathbf{ - M}_{hn} & 0\end{bmatrix}\begin{bmatrix}\ddot{\bar{q}} \\ \bar{\lambda} \end{bmatrix} = - \begin{bmatrix} \bar{F}_r - \bar{g}_d \\ - \frac{\partial \mathbf{M}_{hn}\dot{\bar{q}}}{\partial \bar{q}}\dot{\bar{q}} \end{bmatrix}, -where :math:`\bar{g}` is the dynamic bias, and the last term on the right hand side, -called the constraint bias, can be quickly computed as: + \begin{bmatrix} + \mathbf{M}_d & \mathbf{M}_{hn}^T \\ + \mathbf{ M}_{hn} & 0 + \end{bmatrix} + \begin{bmatrix} + \ddot{\bar{q}} \\ + \bar{\lambda} + \end{bmatrix} + + \begin{bmatrix} + \bar{g}_d \\ + \bar{g}_{hnd} + \end{bmatrix} + = + \begin{bmatrix} + 0 \\ + 0 + \end{bmatrix} + +where :math:`\bar{g}_d` is the dynamic bias, and the last term on the right +hand side, called the constraint bias, can be quickly computed as: .. jupyter-execute:: - constraint_bias = diff_constraint.xreplace({qddr : 0 for qddr in qdd}) - -We call the block matrix called the extended mass matrix, and the vector on the right hand side the extended dynamic bias. - -With these `n + m + M` equations, it is possible to solve for :math:`\ddot{\bar{q}}` and :math:`\lambda`. It is therefore possible to -integrate/simulate the system directly. However, because only the second derivative of the constraint is satisfied, numerical -errors can build up, so the constraint is not satisfied. It is better to use a differential algebraic solver, as discussed -in :ref:`Equations of Motion with Holonomic Constraints`. See `the scikit.ode documentation`_ for a worked example. - -.. _`the scikit.ode documentation`: https://github.com/bmcage/odes/blob/master/ipython_examples/Planar%20Pendulum%20as%20DAE.ipynb + ghnd = diff_constraint.xreplace({qddr : 0 for qddr in qdd}) + sm.trigsimp(ghnd) +We call the block matrix called the extended mass matrix and the vector on the +right hand side the extended dynamic bias. +With these `N + m + M` equations, it is possible to solve for +:math:`\ddot{\bar{q}}` and :math:`\lambda`. It is therefore possible to +integrate/simulate the system directly. However, because only the second +derivative of the constraint is satisfied, numerical errors can build up due to +not satisfying the actual constraint the constraint is not satisfied. It is +better to use a differential algebraic solver, as discussed in :ref:`Equations +of Motion with Holonomic Constraints`. See `the scikits.ode documentation`_ for +a worked example. -The method of the Lagrange multiplier can of course also be used within Kane's method. However, -it increases the number of equations, which is why the elimination approach is often -preferred there. An exception being scenarios where the constraint force itself is a useful output, -for instance to check no-slip conditions in case of limited friction. +.. _`the scikits.ode documentation`: https://github.com/bmcage/odes/blob/master/ipython_examples/Planar%20Pendulum%20as%20DAE.ipynb +The method of the Lagrange multipliers can of course also be used within Kane's +method. However, it increases the number of equations, which is why the +elimination approach is often preferred there. An exception being scenarios +where the constraint force itself is a useful output, for instance to check +no-slip conditions in case of limited friction. Lagrange's vs Kane's ==================== -The is book has now presented two alternatives to the Newton-Euler method: Kane's method and Lagrange's method. -This raises the questions: when should each alternative method be used? +The is book has now presented two alternatives to the Newton-Euler method: +Kane's method and Lagrange's method. This raises the questions: when should +each alternative method be used? -For constrained systems, Kane's method has the advantage that the equations of motion are given for a set of -independent generalized speeds only. In other words, Kane's method gives a minimal set of equations. This can -give rise to simplified equations, additional insight, and -numerically more efficient simulation. This also gives the benefit that Lagrange multipliers are not needed -when solving constrained systems with Kane's method. +For constrained systems, Kane's method has the advantage that the equations of +motion are given for a set of independent generalized speeds only. In other +words, Kane's method gives a minimal set of equations. This can give rise to +simplified equations, additional insight, and numerically more efficient +simulation. This also gives the benefit that Lagrange multipliers are not +needed when solving constrained systems with Kane's method. -Furthermore, the connection from Kane's method to vector mechanics, that is, Newton's laws, is clearer, which -can provide additional insight, and make it easier to encorporate non-conservative forces such as friction. +Furthermore, the connection from Kane's method to vector mechanics, that is, +Newton's Laws, is clearer, which can provide additional insight, and make it +easier to incorporate non-conservative forces such as friction. -On the other hand, the Lagrange method only requires energies as input, for which only the velocities -of the bodies are needed. Therefore, it can be simpler to derive than the accelerations which are needed for Kane's -method. +On the other hand, the Lagrange method only requires energies as input, for +which only the velocities of the bodies are needed. Therefore, it can be +simpler to derive than the accelerations which are needed for Kane's method. -Furthermore, the Lagrange method results in a set of equations with well understood structures and properties. -These structures and properties are not studied further in these materials. A starting point for further study -is `Noether's theorem`_, which extends the idea of ignorable coordinates to find conserved quantities like -momentum and energy. +Furthermore, the Lagrange method results in a set of equations with well +understood structures and properties. These structures and properties are not +studied further in these materials. A starting point for further study is +`Noether's theorem`_, which extends the idea of ignorable coordinates to find +conserved quantities like momentum and energy. .. _`Noether's theorem`: https://en.wikipedia.org/wiki/Noether%27s_theorem_ - - - - .. (Learn more) Generalized momentum .. ================================= @@ -611,7 +722,7 @@ momentum and energy. .. A.orient_axis(N, q1, N.z) .. B.orient_axis(A, q2, A.z) -.. C.orient_axis(B, q3, B.x) +.. C.orient_axis(B, q3, B.x) .. g = 1 .. rho = 1 @@ -631,7 +742,7 @@ momentum and energy. .. Co = me.Point("C_c") .. Co.set_pos(Bo, -0.5*l*B.z -0.5*l*C.z) -.. The next step is again to form the Lagrangian and find the equations of motion. As the system has no further constraints, +.. The next step is again to form the Lagrangian and find the equations of motion. As the system has no further constraints, .. the Lagrange multiplier method is not needed. The actuator torques are added to the right hand side of the equation, in .. the same way as active forces are added to Kane's equations. Here the torques are represented by the variables :math:`T_b` .. and :math:`T_c` are used to represent. @@ -659,7 +770,7 @@ momentum and energy. .. qdd_sol = Md.solve(F_r - gd) -.. .. Practice problem: add a damping force or a coulomb friction force in the first joint +.. .. Practice problem: add a damping force or a coulomb friction force in the first joint .. .. (the example and this problem are inspired by a talk by A. Ruina, https://www.youtube.com/watch?v=j-wHI764dWU) @@ -672,7 +783,7 @@ momentum and energy. .. .. math:: -.. \dot{q_r} = \dot{q_r}(\bar{p}) +.. \dot{q_r} = \dot{q_r}(\bar{p}) .. which forms a `Hamiltonian System`_. Hamiltonian systems and their .. extension Port-Hamiltonian systems are often used in physics and control theory respectively. @@ -707,7 +818,7 @@ momentum and energy. .. p.transpose().jacobian(qd) - Md .. The Jacobian of the generalized momenta with respect to the generalized velocities is the mass matrix. This is always -.. true, because the kinetic energy can be written as :math:`\frac{1}{2}\dot{\bar{q}}^\text{T}\mathbf{M}_d\dot{\bar{q}}`. +.. true, because the kinetic energy can be written as :math:`\frac{1}{2}\dot{\bar{q}}^\text{T}\mathbf{M}_d\dot{\bar{q}}`. .. As a result .. .. math:: @@ -738,7 +849,7 @@ momentum and energy. .. .. math:: -.. \min_{q(t)} \int_{0}^{T} L(t, q, \dot{q})\text{d}t \quad \text{subject to} \quad q(0) = 0, q(T) = q_T +.. \min_{q(t)} \int_{0}^{T} L(t, q, \dot{q})\text{d}t \quad \text{subject to} \quad q(0) = 0, q(T) = q_T .. Examples of such optimizations are: @@ -757,7 +868,7 @@ momentum and energy. .. which we recognize as the Euler-Lagrange equations. .. This means that the laws of nature governing rigid body motions result in motions that minimize the integral of the -.. Lagrangian. This is called Hamilton's principle. It turns out that +.. Lagrangian. This is called Hamilton's principle. It turns out that .. `many physical laws_` take such a form of minimizing .. the value of a function. One example is Fermat's principle, which states that light takes the path of minimum time. @@ -765,9 +876,3 @@ momentum and energy. .. The optimization point-of-view of the Lagrange method also gives an interpretation for the Lagrange multipliers. They .. are the same as the Lagrange multipliers used in optimization. - - - - - - diff --git a/notation.rst b/notation.rst index f66e55e1..ad8b65c9 100644 --- a/notation.rst +++ b/notation.rst @@ -231,6 +231,8 @@ Equations of Motion with Nonholonomic Constraints Terms not linear in :math:`\dot{\bar{u}}_r` in the time differentiated nonholonomic constraint equations. +.. todo:: Mnd = Mn = Ar, right? + Equations of Motion with Holonomic Constraints ============================================== @@ -240,7 +242,7 @@ Equations of Motion with Holonomic Constraints Linear coefficient matrix for :math:`\bar{u}_r` in the time differentiated holonomic constraints. :math:`\bar{g}_{hd}` - Terms not inear in :math:`\bar{u}_r` in the time differentiated holonomic + Terms not linear in :math:`\bar{u}_r` in the time differentiated holonomic constraints. Energy and Power @@ -258,6 +260,33 @@ Energy and Power :math:`E`, ``E`` Total energy, i.e. :math:`E=K+V` +Lagrange's method +================= + +:math:`\bar{F}_r^\textrm{c}` + Conservative generalized active force. +:math:`\bar{F}_r^\textrm{nc}` + Non-conservative generalized active force. +:math:`L`, ``L`` + Lagrangian the difference between the kinetic energy and the potential energy: :math:`L = K - V` +:math:`a_r` + Multiplicative term associated with generalized speed :math:`q_r` in a constraint equation +:math:`\lambda` + Lagrange multiplier, variable encoding the (scaled) magnitude of a constraint force +:math:`\bar{f}_{hn}` + Combined time-derivatives of holonomic constraints and nonholonomic constraints +:math:`\mathbfl{M}_{hn}`, ``Mhn`` + Jacobian of constraint equations with respect to :math:`\dot{\bar{q}}`. +:math:`\bar{g}_{hn}` + Constraint bias (terms not linear in :math:`\dot{\bar{q}}`). +:math:`\bar{g}_{hnd}` + Constraint bias (terms not linear in :math:`\ddot{\bar{q}}`). +:math:`\bar{p}`, ``p`` + Generalized momenta associated with the :math:`\bar{q}` generalized coordinates +:math:`\bar{g}_d` + Dynamic bias, the sum of terms not linear in :math:`\ddot{\bar{q}}` in the + inertial forces and the generalized forces considered in the Lagrangian. + .. |notation-scalar| image:: figures/notation-scalar.svg :height: 10px @@ -297,32 +326,6 @@ Energy and Power .. |notation-vec-time-diff| image:: figures/notation-vec-time-diff.svg :height: 30px -Lagrange's method -================= - -:math:`L`, ``L`` - Lagrangian the difference between the kinetic energy and the potential energy: :math:`L = K - V` - -:math:`a_r` - Multiplicative term associated with generalized speed :math:`q_r` in a constraint equation - -:math:`\lambda` - Lagrange multiplier, variable encoding the (scaled) magnitude of a constraint force - -:math:`\bar{f}_{hn}` - Combined time-derivatives of holonomic constraints and non-holonomic constraints - -:math:`\boldsymbol{M}_{hn}`, ``M_hn`` - Jacobian of constraint equations with respect to :math:`\dot{\bar{q}}` - -:math:`\bar{p}`, ``p`` - Generalized momenta ssociated with the :math:`\bar{q}` generalized coordinates - -:math:`\bar{g}_d` - Dynamic bias, the sum of terms not linear - in $\ddot{\bar{q}}$ in the inertial forces and the generalized conservative forces - considered in the Lagrangian. - Figure Sign Conventions ======================= diff --git a/references.rst b/references.rst index 66446f45..9c27ee56 100644 --- a/references.rst +++ b/references.rst @@ -2,26 +2,30 @@ References ========== -.. [Flores2023] Paulo Flores, Jorge Ambrósio, Hamid M. Lankarani, - "Contact-impact events with friction in mulitbody dynamics: Back to basics", - Mechanism and Machine Theory, vol. 184, 2023. - https://doi.org/10.1016/j.mechmachtheory.2023.105305 +Ordered by date of publication. + .. [Hunt1975] K. H. Hunt, F. R. E. Crossley, "Coefficient of restitution interpreted as damping in vibroimpact", J. Appl. Mech., 42 (2) (1975), pp. 440-445. .. [Kane1985] Thomas R. Kane, and David A. Levinson. Dynamics, Theory and Application. McGraw Hill, 1985. http://hdl.handle.net/1813/638. +.. [Lanczos1986] Cornelius Lanczos, "The Variational Principles of Mechanics", + 4th edition, Dover Publications, 1986, ISBN/EAN 978-04-8665-067-8 +.. [Ostrowski1994] Ostrowski, J., Lewis, A., Murray, R., & Burdick, J. (1994, + May). Nonholonomic mechanics and locomotion: the snakeboard example. In + Proceedings of the 1994 IEEE International Conference on Robotics and + Automation (pp. 2391-2397). IEEE. +.. [Mitiguy1996] P. Mitiguy, "Motion variables Leading to Efficient Equations + of Motion," The International Journal of Robotics Research, vol. 15, no. 5, + pp. 522–532, 1996. .. [Meijaard2007] J. P. Meijaard, J. M. Papadopoulos, A. Ruina, and A. L. Schwab, “Linearized dynamics equations for the balance and steer of a bicycle: A benchmark and review,” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 463, no. 2084, pp. 1955–1982, Aug. 2007. -.. [Mitiguy1996] P. Mitiguy, "Motion variables Leading to Efficient Equations - of Motion," The International Journal of Robotics Research, vol. 15, no. 5, - pp. 522–532, 1996. -.. [Ostrowski1994] Jim Ostrowski, Andrew Lewis, Richard Murray, Joel Burdick - Nonholonomic Mechanics and Locomotion: The Snakeboard Example. 1994 .. [Vallery2020] Heike Vallery and Arend L. Schwab, "Advanced Dynamics", 3rd edition, Delft University of Technology, 2020, ISBN/EAN 978-90-8309-060-3 -.. [Lanczos1970] Cornelius Lanczos, "The Variational Principles of Mechanics", - 4th edition, Dover Publications, 1970, ISBN/EAN 978-04-8665-067-8 +.. [Flores2023] Paulo Flores, Jorge Ambrósio, Hamid M. Lankarani, + "Contact-impact events with friction in mulitbody dynamics: Back to basics", + Mechanism and Machine Theory, vol. 184, 2023. + https://doi.org/10.1016/j.mechmachtheory.2023.105305