Source code for sympy.physics.mechanics.kane

from __future__ import print_function, division

__all__ = ['KanesMethod']

from sympy import Symbol, zeros, Matrix, diff, solve_linear_system_LU, eye
from sympy.core.compatibility import reduce
from sympy.utilities import default_sort_key
from sympy.physics.mechanics.essential import ReferenceFrame, dynamicsymbols
from sympy.physics.mechanics.particle import Particle
from sympy.physics.mechanics.point import Point
from sympy.physics.mechanics.rigidbody import RigidBody
from sympy.physics.mechanics.functions import (inertia_of_point_mass,

[docs]class KanesMethod(object): """Kane's method object. This object is used to do the "book-keeping" as you go through and form equations of motion in the way Kane presents in: Kane, T., Levinson, D. Dynamics Theory and Applications. 1985 McGraw-Hill The attributes are for equations in the form [M] udot = forcing. Attributes ========== auxiliary : Matrix If applicable, the set of auxiliary Kane's equations used to solve for non-contributing forces. mass_matrix : Matrix The system's mass matrix forcing : Matrix The system's forcing vector mass_matrix_full : Matrix The "mass matrix" for the u's and q's forcing_full : Matrix The "forcing vector" for the u's and q's Examples ======== This is a simple example for a one degree of freedom translational spring-mass-damper. In this example, we first need to do the kinematics. This involves creating generalized speeds and coordinates and their derivatives. Then we create a point and set its velocity in a frame:: >>> from sympy import symbols >>> from sympy.physics.mechanics import dynamicsymbols, ReferenceFrame >>> from sympy.physics.mechanics import Point, Particle, KanesMethod >>> q, u = dynamicsymbols('q u') >>> qd, ud = dynamicsymbols('q u', 1) >>> m, c, k = symbols('m c k') >>> N = ReferenceFrame('N') >>> P = Point('P') >>> P.set_vel(N, u * N.x) Next we need to arrange/store information in the way that KanesMethod requires. The kinematic differential equations need to be stored in a dict. A list of forces/torques must be constructed, where each entry in the list is a (Point, Vector) or (ReferenceFrame, Vector) tuple, where the Vectors represent the Force or Torque. Next a particle needs to be created, and it needs to have a point and mass assigned to it. Finally, a list of all bodies and particles needs to be created:: >>> kd = [qd - u] >>> FL = [(P, (-k * q - c * u) * N.x)] >>> pa = Particle('pa', P, m) >>> BL = [pa] Finally we can generate the equations of motion. First we create the KanesMethod object and supply an inertial frame, coordinates, generalized speeds, and the kinematic differential equations. Additional quantities such as configuration and motion constraints, dependent coordinates and speeds, and auxiliary speeds are also supplied here (see the online documentation). Next we form FR* and FR to complete: Fr + Fr* = 0. We have the equations of motion at this point. It makes sense to rearrnge them though, so we calculate the mass matrix and the forcing terms, for E.o.M. in the form: [MM] udot = forcing, where MM is the mass matrix, udot is a vector of the time derivatives of the generalized speeds, and forcing is a vector representing "forcing" terms:: >>> KM = KanesMethod(N, q_ind=[q], u_ind=[u], kd_eqs=kd) >>> (fr, frstar) = KM.kanes_equations(FL, BL) >>> MM = KM.mass_matrix >>> forcing = KM.forcing >>> rhs = MM.inv() * forcing >>> rhs Matrix([[(-c*u(t) - k*q(t))/m]]) >>> KM.linearize()[0] Matrix([ [ 0, 1], [-k, -c]]) Please look at the documentation pages for more information on how to perform linearization and how to deal with dependent coordinates & speeds, and how do deal with bringing non-contributing forces into evidence. """ simp = True ___KDEqError = AttributeError('Create an instance of KanesMethod with' + 'kinematic differential equations to use' + 'this method.') def __init__(self, frame, q_ind, u_ind, kd_eqs=None, q_dependent=[], configuration_constraints=[], u_dependent=[], velocity_constraints=[], acceleration_constraints=None, u_auxiliary=[]): """Please read the online documentation. """ # Big storage things if not isinstance(frame, ReferenceFrame): raise TypeError('An intertial ReferenceFrame must be supplied') self._inertial = frame self._forcelist = None self._bodylist = None self._fr = None self._frstar = None self._rhs = None self._aux_eq = None # States self._q = None self._qdep = [] self._qdot = None self._u = None self._udep = [] self._udot = None self._uaux = None # Differential Equations Matrices and Map self._k_d = None self._f_d = None self._k_kqdot = None self._k_ku = None self._f_k = None self._qdot_u_map = None # Constraint Matrices self._f_h = Matrix([]) self._k_nh = Matrix([]) self._f_nh = Matrix([]) self._k_dnh = Matrix([]) self._f_dnh = Matrix([]) self._coords(q_ind, q_dependent, configuration_constraints) self._speeds(u_ind, u_dependent, velocity_constraints, acceleration_constraints, u_auxiliary) if kd_eqs is not None: self._kindiffeq(kd_eqs) def _find_dynamicsymbols(self, inlist, insyms=[]): """Finds all non-supplied dynamicsymbols in the expressions.""" from sympy.core.function import AppliedUndef, Derivative t = dynamicsymbols._t return reduce(set.union, [set([i]) for j in inlist for i in j.atoms(AppliedUndef, Derivative) if i.atoms() == set([t])], set()) - insyms temp_f = set().union(*[i.atoms(AppliedUndef) for i in inlist]) temp_d = set().union(*[i.atoms(Derivative) for i in inlist]) set_f = set([a for a in temp_f if a.args == (t,)]) set_d = set([a for a in temp_d if ((a.args[0] in set_f) and all([i == t for i in a.variables]))]) return list(set.union(set_f, set_d) - set(insyms)) def _find_othersymbols(self, inlist, insyms=[]): """Finds all non-dynamic symbols in the expressions.""" return list(reduce(set.union, [i.atoms(Symbol) for i in inlist]) - set(insyms)) def _mat_inv_mul(self, A, B): """Internal Function Computes A^-1 * B symbolically w/ substitution, where B is not necessarily a vector, but can be a matrix. """ r1, c1 = A.shape r2, c2 = B.shape temp1 = Matrix(r1, c1, lambda i, j: Symbol('x' + str(j) + str(r1 * i))) temp2 = Matrix(r2, c2, lambda i, j: Symbol('y' + str(j) + str(r2 * i))) for i in range(len(temp1)): if A[i] == 0: temp1[i] = 0 for i in range(len(temp2)): if B[i] == 0: temp2[i] = 0 temp3 = [] for i in range(c2): temp3.append(temp1.LDLsolve(temp2[:, i])) temp3 = Matrix([i.T for i in temp3]).T return temp3.subs(dict(list(zip(temp1, A)))).subs(dict(list(zip(temp2, B)))) def _coords(self, qind, qdep=[], coneqs=[]): """Supply all the generalized coordinates in a list. If some coordinates are dependent, supply them as part of qdep. Their dependent nature will only show up in the linearization process though. Parameters ========== qind : list A list of independent generalized coords qdep : list List of dependent coordinates coneq : list List of expressions which are equal to zero; these are the configuration constraint equations """ if not isinstance(qind, (list, tuple)): raise TypeError('Generalized coords. must be supplied in a list.') self._q = qind + qdep self._qdot = [diff(i, dynamicsymbols._t) for i in self._q] if not isinstance(qdep, (list, tuple)): raise TypeError('Dependent coordinates and constraints must each be ' 'provided in their own list.') if len(qdep) != len(coneqs): raise ValueError('There must be an equal number of dependent ' 'coordinates and constraints.') coneqs = Matrix(coneqs) self._qdep = qdep self._f_h = coneqs def _speeds(self, uind, udep=[], coneqs=[], diffconeqs=None, u_auxiliary=[]): """Supply all the generalized speeds in a list. If there are motion constraints or auxiliary speeds, they are provided here as well (as well as motion constraints). Parameters ========== uind : list A list of independent generalized speeds udep : list Optional list of dependent speeds coneqs : list Optional List of constraint expressions; these are expressions which are equal to zero which define a speed (motion) constraint. diffconeqs : list Optional, calculated automatically otherwise; list of constraint equations; again equal to zero, but define an acceleration constraint. u_auxiliary : list An optional list of auxiliary speeds used for brining non-contributing forces into evidence """ if not hasattr(uind, '__iter__'): raise TypeError('Supply generalized speeds in an iterable.') self._u = uind + udep self._udot = [diff(i, dynamicsymbols._t) for i in self._u] self._uaux = u_auxiliary if not hasattr(udep, '__iter__'): raise TypeError('Supply dependent speeds in an iterable.') if len(udep) != len(coneqs): raise ValueError('There must be an equal number of dependent ' 'speeds and constraints.') if diffconeqs is not None: if len(udep) != len(diffconeqs): raise ValueError('There must be an equal number of dependent ' 'speeds and constraints.') if len(udep) != 0: u = self._u uzero = dict(list(zip(u, [0] * len(u)))) coneqs = Matrix(coneqs) udot = self._udot udotzero = dict(list(zip(udot, [0] * len(udot)))) self._udep = udep self._f_nh = coneqs.subs(uzero) self._k_nh = (coneqs - self._f_nh).jacobian(u) # if no differentiated non holonomic constraints were given, calculate if diffconeqs is None: self._k_dnh = self._k_nh self._f_dnh = (self._k_nh.diff(dynamicsymbols._t) * Matrix(u) + self._f_nh.diff(dynamicsymbols._t)) else: self._f_dnh = diffconeqs.subs(udotzero) self._k_dnh = (diffconeqs - self._f_dnh).jacobian(udot) o = len(u) # number of generalized speeds m = len(udep) # number of motion constraints p = o - m # number of independent speeds # For a reminder, form of non-holonomic constraints is: # B u + C = 0 B = self._k_nh[:, :] C = self._f_nh[:, 0] # We partition B into indenpendent and dependent columns # Ars is then -Bdep.inv() * Bind, and it relates depedent speeds to # independent speeds as: udep = Ars uind, neglecting the C term here. self._depB = B self._depC = C mr1 = B[:, :p] ml1 = B[:, p:o] self._Ars = - self._mat_inv_mul(ml1, mr1) def _partial_velocity(self, vlist, ulist, frame): """Returns the list of partial velocities, replacing qdot's in the velocity list if necessary. """ if self._qdot_u_map is None: raise ___KDEqError v = [vel.subs(self._qdot_u_map) for vel in vlist] return partial_velocity(v, ulist, frame)
[docs] def kindiffdict(self): """Returns the qdot's in a dictionary. """ if self._qdot_u_map is None: raise ___KDEqError return self._qdot_u_map
def _kindiffeq(self, kdeqs): """Supply all the kinematic differential equations in a list. They should be in the form [Expr1, Expr2, ...] where Expri is equal to zero Parameters ========== kdeqs : list (of Expr) The listof kinematic differential equations """ if len(self._q) != len(kdeqs): raise ValueError('There must be an equal number of kinematic ' 'differential equations and coordinates.') uaux = self._uaux # dictionary of auxiliary speeds which are equal to zero uaz = dict(list(zip(uaux, [0] * len(uaux)))) #kdeqs = Matrix(kdeqs).subs(uaz) kdeqs = Matrix(kdeqs) qdot = self._qdot qdotzero = dict(list(zip(qdot, [0] * len(qdot)))) u = self._u uzero = dict(list(zip(u, [0] * len(u)))) f_k = kdeqs.subs(uzero).subs(qdotzero) k_kqdot = (kdeqs.subs(uzero) - f_k).jacobian(Matrix(qdot)) k_ku = (kdeqs.subs(qdotzero) - f_k).jacobian(Matrix(u)) self._k_ku = self._mat_inv_mul(k_kqdot, k_ku) self._f_k = self._mat_inv_mul(k_kqdot, f_k) self._k_kqdot = eye(len(qdot)) self._qdot_u_map = solve_linear_system_LU(Matrix([self._k_kqdot.T, -(self._k_ku * Matrix(self._u) + self._f_k).T]).T, self._qdot) self._k_ku = self._mat_inv_mul(k_kqdot, k_ku).subs(uaz) self._f_k = self._mat_inv_mul(k_kqdot, f_k).subs(uaz) def _form_fr(self, fl): """Form the generalized active force. Computes the vector of the generalized active force vector. Used to compute E.o.M. in the form Fr + Fr* = 0. Parameters ========== fl : list Takes in a list of (Point, Vector) or (ReferenceFrame, Vector) tuples which represent the force at a point or torque on a frame. """ if not hasattr(fl, '__iter__'): raise TypeError('Force pairs must be supplied in an iterable.') N = self._inertial self._forcelist = fl[:] u = self._u o = len(u) # number of gen. speeds b = len(fl) # number of forces FR = zeros(o, 1) # pull out relevant velocities for constructing partial velocities vel_list = [] f_list = [] for i in fl: if isinstance(i[0], ReferenceFrame): vel_list += [i[0].ang_vel_in(N)] elif isinstance(i[0], Point): vel_list += [i[0].vel(N)] else: raise TypeError('First entry in pair must be point or frame.') f_list += [i[1]] partials = self._partial_velocity(vel_list, u, N) # Fill Fr with dot product of partial velocities and forces for i in range(o): for j in range(b): FR[i] += partials[j][i] & f_list[j] # In case there are dependent speeds m = len(self._udep) # number of dependent speeds if m != 0: p = o - m FRtilde = FR[:p, 0] FRold = FR[p:o, 0] FRtilde += self._Ars.T * FRold FR = FRtilde self._fr = FR return FR def _form_frstar(self, bl): """Form the generalized inertia force. Computes the vector of the generalized inertia force vector. Used to compute E.o.M. in the form Fr + Fr* = 0. Parameters ========== bl : list A list of all RigidBody's and Particle's in the system. """ if not hasattr(bl, '__iter__'): raise TypeError('Bodies must be supplied in an iterable.') t = dynamicsymbols._t N = self._inertial self._bodylist = bl u = self._u # all speeds udep = self._udep # dependent speeds o = len(u) m = len(udep) p = o - m udot = self._udot udotzero = dict(list(zip(udot, [0] * o))) # auxiliary speeds uaux = self._uaux uauxdot = [diff(i, t) for i in uaux] # dictionary of auxiliary speeds which are equal to zero uaz = dict(list(zip(uaux, [0] * len(uaux)))) uadz = dict(list(zip(uauxdot, [0] * len(uauxdot)))) # dictionary of qdot's to u's qdots = dict(list(zip(list(self._qdot_u_map.keys()), list(self._qdot_u_map.values())))) for k, v in list(qdots.items()): qdots[k.diff(t)] = v.diff(t) MM = zeros(o, o) nonMM = zeros(o, 1) partials = [] # Fill up the list of partials: format is a list with no. elements # equal to number of entries in body list. Each of these elements is a # list - either of length 1 for the translational components of # particles or of length 2 for the translational and rotational # components of rigid bodies. The inner most list is the list of # partial velocities. for v in bl: if isinstance(v, RigidBody): partials += [self._partial_velocity([v.masscenter.vel(N), v.frame.ang_vel_in(N)], u, N)] elif isinstance(v, Particle): partials += [self._partial_velocity([v.point.vel(N)], u, N)] else: raise TypeError('The body list needs RigidBody or ' 'Particle as list elements.') # This section does 2 things - computes the parts of Fr* that are # associated with the udots, and the parts that are not associated with # the udots. This happens for RigidBody and Particle a little # differently, but similar process overall. for i, v in enumerate(bl): if isinstance(v, RigidBody): M = v.mass.subs(uaz).doit() vel = v.masscenter.vel(N).subs(uaz).doit() acc = v.masscenter.acc(N).subs(udotzero).subs(uaz).doit() inertial_force = (M.diff(t) * vel + M * acc) omega = v.frame.ang_vel_in(N).subs(uaz).doit() I = v.central_inertia.subs(uaz).doit() inertial_torque = ((I.dt(v.frame) & omega).subs(uaz).doit() + (I & v.frame.ang_acc_in(N)).subs(udotzero).subs(uaz).doit() + (omega ^ (I & omega)).subs(uaz).doit()) for j in range(o): tmp_vel = partials[i][0][j].subs(uaz).doit() tmp_ang = (I & partials[i][1][j].subs(uaz).doit()) for k in range(o): # translational MM[j, k] += M * (tmp_vel & partials[i][0][k]) # rotational MM[j, k] += (tmp_ang & partials[i][1][k]) nonMM[j] += inertial_force & partials[i][0][j] nonMM[j] += inertial_torque & partials[i][1][j] if isinstance(v, Particle): M = v.mass.subs(uaz).doit() vel = v.point.vel(N).subs(uaz).doit() acc = v.point.acc(N).subs(udotzero).subs(uaz).doit() inertial_force = (M.diff(t) * vel + M * acc) for j in range(o): temp = partials[i][0][j].subs(uaz).doit() for k in range(o): MM[j, k] += M * (temp & partials[i][0][k]) nonMM[j] += inertial_force & partials[i][0][j] # Negate FRSTAR since Kane defines the inertia forces/torques # to be negative and we didn't do so above. MM = MM.subs(qdots).subs(uaz).doit() nonMM = nonMM.subs(qdots).subs(udotzero).subs(uadz).subs(uaz).doit() FRSTAR = -(MM * Matrix(udot).subs(uadz) + nonMM) # For motion constraints, m is the number of constraints # Really, one should just look at Kane's book for descriptions of this # process if m != 0: FRSTARtilde = FRSTAR[:p, 0] FRSTARold = FRSTAR[p:o, 0] FRSTARtilde += self._Ars.T * FRSTARold FRSTAR = FRSTARtilde MMi = MM[:p, :] MMd = MM[p:o, :] MM = MMi + self._Ars.T * MMd self._frstar = FRSTAR zeroeq = -(self._fr + self._frstar) zeroeq = zeroeq.subs(udotzero) self._k_d = MM self._f_d = zeroeq return FRSTAR
[docs] def kanes_equations(self, FL, BL): """ Method to form Kane's equations, Fr + Fr* = 0. Returns (Fr, Fr*). In the case where auxiliary generalized speeds are present (say, s auxiliary speeds, o generalized speeds, and m motion constraints) the length of the returned vectors will be o - m + s in length. The first o - m equations will be the constrained Kane's equations, then the s auxiliary Kane's equations. These auxiliary equations can be accessed with the auxiliary_eqs(). Parameters ========== FL : list Takes in a list of (Point, Vector) or (ReferenceFrame, Vector) tuples which represent the force at a point or torque on a frame. BL : list A list of all RigidBody's and Particle's in the system. """ if (self._q is None) or (self._u is None): raise ValueError('Speeds and coordinates must be supplied first.') if (self._k_kqdot is None): raise __KDEqError fr = self._form_fr(FL) frstar = self._form_frstar(BL) if self._uaux != []: if self._udep == []: km = KanesMethod(self._inertial, self._q, self._uaux, u_auxiliary=self._uaux) else: km = KanesMethod(self._inertial, self._q, self._uaux, u_auxiliary=self._uaux, u_dependent=self._udep, velocity_constraints=(self._k_nh * Matrix(self._u) + self._f_nh)) km._qdot_u_map = self._qdot_u_map self._km = km fraux = km._form_fr(FL) frstaraux = km._form_frstar(BL) self._aux_eq = fraux + frstaraux self._fr = fr.col_join(fraux) self._frstar = frstar.col_join(frstaraux) return (self._fr, self._frstar) else: return (fr, frstar)
[docs] def linearize(self): """ Method used to generate linearized equations. Note that for linearization, it is assumed that time is not perturbed, but only coordinates and positions. The "forcing" vector's jacobian is computed with respect to the state vector in the form [Qi, Qd, Ui, Ud]. This is the "f_lin_A" matrix. It also finds any non-state dynamicsymbols and computes the jacobian of the "forcing" vector with respect to them. This is the "f_lin_B" matrix; if this is empty, an empty matrix is created. Consider the following: If our equations are: [M]qudot = f, where [M] is the full mass matrix, qudot is a vector of the deriatives of the coordinates and speeds, and f in the full forcing vector, the linearization process is as follows: [M]qudot = [f_lin_A]qu + [f_lin_B]y, where qu is the state vector, f_lin_A is the jacobian of the full forcing vector with respect to the state vector, f_lin_B is the jacobian of the full forcing vector with respect to any non-speed/coordinate dynamicsymbols which show up in the full forcing vector, and y is a vector of those dynamic symbols (each column in f_lin_B corresponds to a row of the y vector, each of which is a non-speed/coordinate dynamicsymbol). To get the traditional state-space A and B matrix, you need to multiply the f_lin_A and f_lin_B matrices by the inverse of the mass matrix. Caution needs to be taken when inverting large symbolic matrices; substituting in numerical values before inverting will work better. A tuple of (f_lin_A, f_lin_B, other_dynamicsymbols) is returned. """ if (self._fr is None) or (self._frstar is None): raise ValueError('Need to compute Fr, Fr* first.') # Note that this is now unneccessary, and it should never be # encountered; I still think it should be in here in case the user # manually sets these matrices incorrectly. for i in self._q: if self._k_kqdot.diff(i) != 0 * self._k_kqdot: raise ValueError('Matrix K_kqdot must not depend on any q.') t = dynamicsymbols._t uaux = self._uaux uauxdot = [diff(i, t) for i in uaux] # dictionary of auxiliary speeds & derivatives which are equal to zero subdict = dict(list(zip(uaux + uauxdot, [0] * (len(uaux) + len(uauxdot))))) # Checking for dynamic symbols outside the dynamic differential # equations; throws error if there is. insyms = set( self._q + self._qdot + self._u + self._udot + uaux + uauxdot) if any(self._find_dynamicsymbols(i, insyms) for i in [self._k_kqdot, self._k_ku, self._f_k, self._k_dnh, self._f_dnh, self._k_d]): raise ValueError('Cannot have dynamicsymbols outside dynamic ' 'forcing vector.') other_dyns = list(self._find_dynamicsymbols(self._f_d.subs(subdict), insyms)) # make it canonically ordered so the jacobian is canonical other_dyns.sort(key=default_sort_key) for i in other_dyns: if diff(i, dynamicsymbols._t) in other_dyns: raise ValueError('Cannot have derivatives of specified ' 'quantities when linearizing forcing terms.') o = len(self._u) # number of speeds n = len(self._q) # number of coordinates l = len(self._qdep) # number of configuration constraints m = len(self._udep) # number of motion constraints qi = Matrix(self._q[: n - l]) # independent coords qd = Matrix(self._q[n - l: n]) # dependent coords; could be empty ui = Matrix(self._u[: o - m]) # independent speeds ud = Matrix(self._u[o - m: o]) # dependent speeds; could be empty qdot = Matrix(self._qdot) # time derivatives of coordinates # with equations in the form MM udot = forcing, expand that to: # MM_full [q,u].T = forcing_full. This combines coordinates and # speeds together for the linearization, which is necessary for the # linearization process, due to dependent coordinates. f1 is the rows # from the kinematic differential equations, f2 is the rows from the # dynamic differential equations (and differentiated non-holonomic # constraints). f1 = self._k_ku * Matrix(self._u) + self._f_k f2 = self._f_d # Only want to do this if these matrices have been filled in, which # occurs when there are dependent speeds if m != 0: f2 = self._f_d.col_join(self._f_dnh) fnh = self._f_nh + self._k_nh * Matrix(self._u) f1 = f1.subs(subdict) f2 = f2.subs(subdict) fh = self._f_h.subs(subdict) fku = (self._k_ku * Matrix(self._u)).subs(subdict) fkf = self._f_k.subs(subdict) # In the code below, we are applying the chain rule by hand on these # things. All the matrices have been changed into vectors (by # multiplying the dynamic symbols which it is paired with), so we can # take the jacobian of them. The basic operation is take the jacobian # of the f1, f2 vectors wrt all of the q's and u's. f1 is a function of # q, u, and t; f2 is a function of q, qdot, u, and t. In the code # below, we are not considering perturbations in t. So if f1 is a # function of the q's, u's but some of the q's or u's could be # dependent on other q's or u's (qd's might be dependent on qi's, ud's # might be dependent on ui's or qi's), so what we do is take the # jacobian of the f1 term wrt qi's and qd's, the jacobian wrt the qd's # gets multiplied by the jacobian of qd wrt qi, this is extended for # the ud's as well. dqd_dqi is computed by taking a taylor expansion of # the holonomic constraint equations about q*, treating q* - q as dq, # seperating into dqd (depedent q's) and dqi (independent q's) and the # rearranging for dqd/dqi. This is again extended for the speeds. # First case: configuration and motion constraints if (l != 0) and (m != 0): fh_jac_qi = fh.jacobian(qi) fh_jac_qd = fh.jacobian(qd) fnh_jac_qi = fnh.jacobian(qi) fnh_jac_qd = fnh.jacobian(qd) fnh_jac_ui = fnh.jacobian(ui) fnh_jac_ud = fnh.jacobian(ud) fku_jac_qi = fku.jacobian(qi) fku_jac_qd = fku.jacobian(qd) fku_jac_ui = fku.jacobian(ui) fku_jac_ud = fku.jacobian(ud) fkf_jac_qi = fkf.jacobian(qi) fkf_jac_qd = fkf.jacobian(qd) f1_jac_qi = f1.jacobian(qi) f1_jac_qd = f1.jacobian(qd) f1_jac_ui = f1.jacobian(ui) f1_jac_ud = f1.jacobian(ud) f2_jac_qi = f2.jacobian(qi) f2_jac_qd = f2.jacobian(qd) f2_jac_ui = f2.jacobian(ui) f2_jac_ud = f2.jacobian(ud) f2_jac_qdot = f2.jacobian(qdot) dqd_dqi = - self._mat_inv_mul(fh_jac_qd, fh_jac_qi) dud_dqi = self._mat_inv_mul(fnh_jac_ud, (fnh_jac_qd * dqd_dqi - fnh_jac_qi)) dud_dui = - self._mat_inv_mul(fnh_jac_ud, fnh_jac_ui) dqdot_dui = - self._k_kqdot.inv() * (fku_jac_ui + fku_jac_ud * dud_dui) dqdot_dqi = - self._k_kqdot.inv() * (fku_jac_qi + fkf_jac_qi + (fku_jac_qd + fkf_jac_qd) * dqd_dqi + fku_jac_ud * dud_dqi) f1_q = f1_jac_qi + f1_jac_qd * dqd_dqi + f1_jac_ud * dud_dqi f1_u = f1_jac_ui + f1_jac_ud * dud_dui f2_q = (f2_jac_qi + f2_jac_qd * dqd_dqi + f2_jac_qdot * dqdot_dqi + f2_jac_ud * dud_dqi) f2_u = f2_jac_ui + f2_jac_ud * dud_dui + f2_jac_qdot * dqdot_dui # Second case: configuration constraints only elif l != 0: dqd_dqi = - self._mat_inv_mul(fh.jacobian(qd), fh.jacobian(qi)) dqdot_dui = - self._k_kqdot.inv() * fku.jacobian(ui) dqdot_dqi = - self._k_kqdot.inv() * (fku.jacobian(qi) + fkf.jacobian(qi) + (fku.jacobian(qd) + fkf.jacobian(qd)) * dqd_dqi) f1_q = (f1.jacobian(qi) + f1.jacobian(qd) * dqd_dqi) f1_u = f1.jacobian(ui) f2_jac_qdot = f2.jacobian(qdot) f2_q = (f2.jacobian(qi) + f2.jacobian(qd) * dqd_dqi + f2.jac_qdot * dqdot_dqi) f2_u = f2.jacobian(ui) + f2_jac_qdot * dqdot_dui # Third case: motion constraints only elif m != 0: dud_dqi = self._mat_inv_mul(fnh.jacobian(ud), - fnh.jacobian(qi)) dud_dui = - self._mat_inv_mul(fnh.jacobian(ud), fnh.jacobian(ui)) dqdot_dui = - self._k_kqdot.inv() * (fku.jacobian(ui) + fku.jacobian(ud) * dud_dui) dqdot_dqi = - self._k_kqdot.inv() * (fku.jacobian(qi) + fkf.jacobian(qi) + fku.jacobian(ud) * dud_dqi) f1_jac_ud = f1.jacobian(ud) f2_jac_qdot = f2.jacobian(qdot) f2_jac_ud = f2.jacobian(ud) f1_q = f1.jacobian(qi) + f1_jac_ud * dud_dqi f1_u = f1.jacobian(ui) + f1_jac_ud * dud_dui f2_q = (f2.jacobian(qi) + f2_jac_qdot * dqdot_dqi + f2_jac_ud * dud_dqi) f2_u = (f2.jacobian(ui) + f2_jac_ud * dud_dui + f2_jac_qdot * dqdot_dui) # Fourth case: No constraints else: dqdot_dui = - self._k_kqdot.inv() * fku.jacobian(ui) dqdot_dqi = - self._k_kqdot.inv() * (fku.jacobian(qi) + fkf.jacobian(qi)) f1_q = f1.jacobian(qi) f1_u = f1.jacobian(ui) f2_jac_qdot = f2.jacobian(qdot) f2_q = f2.jacobian(qi) + f2_jac_qdot * dqdot_dqi f2_u = f2.jacobian(ui) + f2_jac_qdot * dqdot_dui f_lin_A = -(f1_q.row_join(f1_u)).col_join(f2_q.row_join(f2_u)) if other_dyns: f1_oths = f1.jacobian(other_dyns) f2_oths = f2.jacobian(other_dyns) f_lin_B = -f1_oths.col_join(f2_oths) else: f_lin_B = Matrix([]) return (f_lin_A, f_lin_B, Matrix(other_dyns))
[docs] def rhs(self, inv_method=None): """ Returns the system's equations of motion in first order form. The output of this will be the right hand side of: [qdot, udot].T = f(q, u, t) Or, the equations of motion in first order form. The right hand side is what is needed by most numerical ODE integrators. Parameters ========== inv_method : str The specific sympy inverse matrix calculation method to use. """ if inv_method is None: self._rhs = self._mat_inv_mul(self.mass_matrix_full, self.forcing_full) else: self._rhs = (self.mass_matrix_full.inv(inv_method, try_block_diag=True) * self.forcing_full) return self._rhs
@property def auxiliary_eqs(self): if (self._fr is None) or (self._frstar is None): raise ValueError('Need to compute Fr, Fr* first.') if self._uaux == []: raise ValueError('No auxiliary speeds have been declared.') return self._aux_eq @property def mass_matrix(self): # Returns the mass matrix, which is augmented by the differentiated non # holonomic equations if necessary if (self._frstar is None) & (self._fr is None): raise ValueError('Need to compute Fr, Fr* first.') return Matrix([self._k_d, self._k_dnh]) @property def mass_matrix_full(self): # Returns the mass matrix from above, augmented by kin diff's k_kqdot if (self._frstar is None) & (self._fr is None): raise ValueError('Need to compute Fr, Fr* first.') o = len(self._u) n = len(self._q) return ((self._k_kqdot).row_join(zeros(n, o))).col_join((zeros(o, n)).row_join(self.mass_matrix)) @property def forcing(self): # Returns the forcing vector, which is augmented by the differentiated # non holonomic equations if necessary if (self._frstar is None) & (self._fr is None): raise ValueError('Need to compute Fr, Fr* first.') return -Matrix([self._f_d, self._f_dnh]) @property def forcing_full(self): # Returns the forcing vector, which is augmented by the differentiated # non holonomic equations if necessary if (self._frstar is None) & (self._fr is None): raise ValueError('Need to compute Fr, Fr* first.') f1 = self._k_ku * Matrix(self._u) + self._f_k return -Matrix([f1, self._f_d, self._f_dnh])