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708 lines
22 KiB
708 lines
22 KiB
#
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# Copyright (c) The acados authors.
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#
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# This file is part of acados.
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#
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# The 2-Clause BSD License
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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#
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# 1. Redistributions of source code must retain the above copyright notice,
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# this list of conditions and the following disclaimer.
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#
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# 2. Redistributions in binary form must reproduce the above copyright notice,
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# this list of conditions and the following disclaimer in the documentation
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# and/or other materials provided with the distribution.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
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# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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# POSSIBILITY OF SUCH DAMAGE.;
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#
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import os
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import casadi as ca
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from .utils import is_empty, casadi_length
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def get_casadi_symbol(x):
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if isinstance(x, ca.MX):
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return ca.MX.sym
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elif isinstance(x, ca.SX):
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return ca.SX.sym
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else:
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raise TypeError("Expected casadi SX or MX.")
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################
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# Dynamics
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################
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def generate_c_code_discrete_dynamics( model, opts ):
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casadi_codegen_opts = dict(mex=False, casadi_int='int', casadi_real='double')
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# load model
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x = model.x
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u = model.u
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p = model.p
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phi = model.disc_dyn_expr
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model_name = model.name
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nx = casadi_length(x)
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symbol = get_casadi_symbol(x)
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# assume nx1 = nx !!!
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lam = symbol('lam', nx, 1)
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# generate jacobians
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ux = ca.vertcat(u,x)
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jac_ux = ca.jacobian(phi, ux)
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# generate adjoint
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adj_ux = ca.jtimes(phi, ux, lam, True)
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# generate hessian
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hess_ux = ca.jacobian(adj_ux, ux)
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# change directory
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cwd = os.getcwd()
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model_dir = os.path.abspath(os.path.join(opts["code_export_directory"], f'{model_name}_model'))
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if not os.path.exists(model_dir):
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os.makedirs(model_dir)
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os.chdir(model_dir)
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# set up & generate ca.Functions
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fun_name = model_name + '_dyn_disc_phi_fun'
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phi_fun = ca.Function(fun_name, [x, u, p], [phi])
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phi_fun.generate(fun_name, casadi_codegen_opts)
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fun_name = model_name + '_dyn_disc_phi_fun_jac'
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phi_fun_jac_ut_xt = ca.Function(fun_name, [x, u, p], [phi, jac_ux.T])
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phi_fun_jac_ut_xt.generate(fun_name, casadi_codegen_opts)
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fun_name = model_name + '_dyn_disc_phi_fun_jac_hess'
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phi_fun_jac_ut_xt_hess = ca.Function(fun_name, [x, u, lam, p], [phi, jac_ux.T, hess_ux])
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phi_fun_jac_ut_xt_hess.generate(fun_name, casadi_codegen_opts)
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os.chdir(cwd)
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return
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def generate_c_code_explicit_ode( model, opts ):
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casadi_codegen_opts = dict(mex=False, casadi_int='int', casadi_real='double')
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generate_hess = opts["generate_hess"]
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# load model
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x = model.x
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u = model.u
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p = model.p
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f_expl = model.f_expl_expr
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model_name = model.name
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## get model dimensions
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nx = x.size()[0]
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nu = u.size()[0]
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symbol = get_casadi_symbol(x)
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## set up functions to be exported
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Sx = symbol('Sx', nx, nx)
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Sp = symbol('Sp', nx, nu)
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lambdaX = symbol('lambdaX', nx, 1)
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fun_name = model_name + '_expl_ode_fun'
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## Set up functions
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expl_ode_fun = ca.Function(fun_name, [x, u, p], [f_expl])
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vdeX = ca.jtimes(f_expl,x,Sx)
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vdeP = ca.jacobian(f_expl,u) + ca.jtimes(f_expl,x,Sp)
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fun_name = model_name + '_expl_vde_forw'
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expl_vde_forw = ca.Function(fun_name, [x, Sx, Sp, u, p], [f_expl, vdeX, vdeP])
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adj = ca.jtimes(f_expl, ca.vertcat(x, u), lambdaX, True)
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fun_name = model_name + '_expl_vde_adj'
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expl_vde_adj = ca.Function(fun_name, [x, lambdaX, u, p], [adj])
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if generate_hess:
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S_forw = ca.vertcat(ca.horzcat(Sx, Sp), ca.horzcat(ca.DM.zeros(nu,nx), ca.DM.eye(nu)))
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hess = ca.mtimes(ca.transpose(S_forw),ca.jtimes(adj, ca.vertcat(x,u), S_forw))
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hess2 = []
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for j in range(nx+nu):
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for i in range(j,nx+nu):
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hess2 = ca.vertcat(hess2, hess[i,j])
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fun_name = model_name + '_expl_ode_hess'
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expl_ode_hess = ca.Function(fun_name, [x, Sx, Sp, lambdaX, u, p], [adj, hess2])
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# change directory
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cwd = os.getcwd()
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model_dir = os.path.abspath(os.path.join(opts["code_export_directory"], f'{model_name}_model'))
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if not os.path.exists(model_dir):
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os.makedirs(model_dir)
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os.chdir(model_dir)
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# generate C code
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fun_name = model_name + '_expl_ode_fun'
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expl_ode_fun.generate(fun_name, casadi_codegen_opts)
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fun_name = model_name + '_expl_vde_forw'
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expl_vde_forw.generate(fun_name, casadi_codegen_opts)
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fun_name = model_name + '_expl_vde_adj'
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expl_vde_adj.generate(fun_name, casadi_codegen_opts)
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if generate_hess:
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fun_name = model_name + '_expl_ode_hess'
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expl_ode_hess.generate(fun_name, casadi_codegen_opts)
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os.chdir(cwd)
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return
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def generate_c_code_implicit_ode( model, opts ):
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casadi_codegen_opts = dict(mex=False, casadi_int='int', casadi_real='double')
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# load model
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x = model.x
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xdot = model.xdot
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u = model.u
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z = model.z
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p = model.p
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f_impl = model.f_impl_expr
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model_name = model.name
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# get model dimensions
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nx = casadi_length(x)
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nz = casadi_length(z)
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# generate jacobians
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jac_x = ca.jacobian(f_impl, x)
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jac_xdot = ca.jacobian(f_impl, xdot)
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jac_u = ca.jacobian(f_impl, u)
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jac_z = ca.jacobian(f_impl, z)
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# Set up functions
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p = model.p
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fun_name = model_name + '_impl_dae_fun'
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impl_dae_fun = ca.Function(fun_name, [x, xdot, u, z, p], [f_impl])
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fun_name = model_name + '_impl_dae_fun_jac_x_xdot_z'
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impl_dae_fun_jac_x_xdot_z = ca.Function(fun_name, [x, xdot, u, z, p], [f_impl, jac_x, jac_xdot, jac_z])
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fun_name = model_name + '_impl_dae_fun_jac_x_xdot_u_z'
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impl_dae_fun_jac_x_xdot_u_z = ca.Function(fun_name, [x, xdot, u, z, p], [f_impl, jac_x, jac_xdot, jac_u, jac_z])
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fun_name = model_name + '_impl_dae_fun_jac_x_xdot_u'
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impl_dae_fun_jac_x_xdot_u = ca.Function(fun_name, [x, xdot, u, z, p], [f_impl, jac_x, jac_xdot, jac_u])
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fun_name = model_name + '_impl_dae_jac_x_xdot_u_z'
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impl_dae_jac_x_xdot_u_z = ca.Function(fun_name, [x, xdot, u, z, p], [jac_x, jac_xdot, jac_u, jac_z])
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if opts["generate_hess"]:
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x_xdot_z_u = ca.vertcat(x, xdot, z, u)
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symbol = get_casadi_symbol(x)
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multiplier = symbol('multiplier', nx + nz)
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ADJ = ca.jtimes(f_impl, x_xdot_z_u, multiplier, True)
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HESS = ca.jacobian(ADJ, x_xdot_z_u)
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fun_name = model_name + '_impl_dae_hess'
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impl_dae_hess = ca.Function(fun_name, [x, xdot, u, z, multiplier, p], [HESS])
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# change directory
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cwd = os.getcwd()
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model_dir = os.path.abspath(os.path.join(opts["code_export_directory"], f'{model_name}_model'))
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if not os.path.exists(model_dir):
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os.makedirs(model_dir)
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os.chdir(model_dir)
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# generate C code
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fun_name = model_name + '_impl_dae_fun'
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impl_dae_fun.generate(fun_name, casadi_codegen_opts)
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fun_name = model_name + '_impl_dae_fun_jac_x_xdot_z'
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impl_dae_fun_jac_x_xdot_z.generate(fun_name, casadi_codegen_opts)
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fun_name = model_name + '_impl_dae_jac_x_xdot_u_z'
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impl_dae_jac_x_xdot_u_z.generate(fun_name, casadi_codegen_opts)
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fun_name = model_name + '_impl_dae_fun_jac_x_xdot_u_z'
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impl_dae_fun_jac_x_xdot_u_z.generate(fun_name, casadi_codegen_opts)
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fun_name = model_name + '_impl_dae_fun_jac_x_xdot_u'
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impl_dae_fun_jac_x_xdot_u.generate(fun_name, casadi_codegen_opts)
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if opts["generate_hess"]:
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fun_name = model_name + '_impl_dae_hess'
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impl_dae_hess.generate(fun_name, casadi_codegen_opts)
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os.chdir(cwd)
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return
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def generate_c_code_gnsf( model, opts ):
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casadi_codegen_opts = dict(mex=False, casadi_int='int', casadi_real='double')
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model_name = model.name
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# set up directory
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cwd = os.getcwd()
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model_dir = os.path.abspath(os.path.join(opts["code_export_directory"], f'{model_name}_model'))
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if not os.path.exists(model_dir):
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os.makedirs(model_dir)
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os.chdir(model_dir)
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# obtain gnsf dimensions
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get_matrices_fun = model.get_matrices_fun
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phi_fun = model.phi_fun
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size_gnsf_A = get_matrices_fun.size_out(0)
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gnsf_nx1 = size_gnsf_A[1]
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gnsf_nz1 = size_gnsf_A[0] - size_gnsf_A[1]
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gnsf_nuhat = max(phi_fun.size_in(1))
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gnsf_ny = max(phi_fun.size_in(0))
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gnsf_nout = max(phi_fun.size_out(0))
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# set up expressions
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# if the model uses ca.MX because of cost/constraints
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# the DAE can be exported as ca.SX -> detect GNSF in Matlab
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# -> evaluated ca.SX GNSF functions with ca.MX.
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u = model.u
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symbol = get_casadi_symbol(u)
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y = symbol("y", gnsf_ny, 1)
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uhat = symbol("uhat", gnsf_nuhat, 1)
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p = model.p
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x1 = symbol("gnsf_x1", gnsf_nx1, 1)
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x1dot = symbol("gnsf_x1dot", gnsf_nx1, 1)
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z1 = symbol("gnsf_z1", gnsf_nz1, 1)
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dummy = symbol("gnsf_dummy", 1, 1)
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empty_var = symbol("gnsf_empty_var", 0, 0)
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## generate C code
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fun_name = model_name + '_gnsf_phi_fun'
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phi_fun_ = ca.Function(fun_name, [y, uhat, p], [phi_fun(y, uhat, p)])
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phi_fun_.generate(fun_name, casadi_codegen_opts)
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fun_name = model_name + '_gnsf_phi_fun_jac_y'
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phi_fun_jac_y = model.phi_fun_jac_y
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phi_fun_jac_y_ = ca.Function(fun_name, [y, uhat, p], phi_fun_jac_y(y, uhat, p))
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phi_fun_jac_y_.generate(fun_name, casadi_codegen_opts)
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fun_name = model_name + '_gnsf_phi_jac_y_uhat'
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phi_jac_y_uhat = model.phi_jac_y_uhat
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phi_jac_y_uhat_ = ca.Function(fun_name, [y, uhat, p], phi_jac_y_uhat(y, uhat, p))
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phi_jac_y_uhat_.generate(fun_name, casadi_codegen_opts)
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fun_name = model_name + '_gnsf_f_lo_fun_jac_x1k1uz'
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f_lo_fun_jac_x1k1uz = model.f_lo_fun_jac_x1k1uz
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f_lo_fun_jac_x1k1uz_eval = f_lo_fun_jac_x1k1uz(x1, x1dot, z1, u, p)
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# avoid codegeneration issue
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if not isinstance(f_lo_fun_jac_x1k1uz_eval, tuple) and is_empty(f_lo_fun_jac_x1k1uz_eval):
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f_lo_fun_jac_x1k1uz_eval = [empty_var]
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f_lo_fun_jac_x1k1uz_ = ca.Function(fun_name, [x1, x1dot, z1, u, p],
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f_lo_fun_jac_x1k1uz_eval)
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f_lo_fun_jac_x1k1uz_.generate(fun_name, casadi_codegen_opts)
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fun_name = model_name + '_gnsf_get_matrices_fun'
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get_matrices_fun_ = ca.Function(fun_name, [dummy], get_matrices_fun(1))
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get_matrices_fun_.generate(fun_name, casadi_codegen_opts)
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# remove fields for json dump
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del model.phi_fun
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del model.phi_fun_jac_y
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del model.phi_jac_y_uhat
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del model.f_lo_fun_jac_x1k1uz
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del model.get_matrices_fun
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os.chdir(cwd)
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return
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################
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# Cost
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################
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def generate_c_code_external_cost(model, stage_type, opts):
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casadi_codegen_opts = dict(mex=False, casadi_int='int', casadi_real='double')
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x = model.x
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p = model.p
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u = model.u
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z = model.z
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symbol = get_casadi_symbol(x)
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if stage_type == 'terminal':
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suffix_name = "_cost_ext_cost_e_fun"
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suffix_name_hess = "_cost_ext_cost_e_fun_jac_hess"
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suffix_name_jac = "_cost_ext_cost_e_fun_jac"
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ext_cost = model.cost_expr_ext_cost_e
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custom_hess = model.cost_expr_ext_cost_custom_hess_e
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# Last stage cannot depend on u and z
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u = symbol("u", 0, 0)
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z = symbol("z", 0, 0)
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elif stage_type == 'path':
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suffix_name = "_cost_ext_cost_fun"
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suffix_name_hess = "_cost_ext_cost_fun_jac_hess"
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suffix_name_jac = "_cost_ext_cost_fun_jac"
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ext_cost = model.cost_expr_ext_cost
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custom_hess = model.cost_expr_ext_cost_custom_hess
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elif stage_type == 'initial':
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suffix_name = "_cost_ext_cost_0_fun"
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suffix_name_hess = "_cost_ext_cost_0_fun_jac_hess"
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suffix_name_jac = "_cost_ext_cost_0_fun_jac"
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ext_cost = model.cost_expr_ext_cost_0
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custom_hess = model.cost_expr_ext_cost_custom_hess_0
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nunx = x.shape[0] + u.shape[0]
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# set up functions to be exported
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fun_name = model.name + suffix_name
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fun_name_hess = model.name + suffix_name_hess
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fun_name_jac = model.name + suffix_name_jac
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# generate expression for full gradient and Hessian
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hess_uxz, grad_uxz = ca.hessian(ext_cost, ca.vertcat(u, x, z))
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hess_ux = hess_uxz[:nunx, :nunx]
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hess_z = hess_uxz[nunx:, nunx:]
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hess_z_ux = hess_uxz[nunx:, :nunx]
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if custom_hess is not None:
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hess_ux = custom_hess
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ext_cost_fun = ca.Function(fun_name, [x, u, z, p], [ext_cost])
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ext_cost_fun_jac_hess = ca.Function(
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fun_name_hess, [x, u, z, p], [ext_cost, grad_uxz, hess_ux, hess_z, hess_z_ux]
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)
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ext_cost_fun_jac = ca.Function(
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fun_name_jac, [x, u, z, p], [ext_cost, grad_uxz]
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)
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# change directory
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cwd = os.getcwd()
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cost_dir = os.path.abspath(os.path.join(opts["code_export_directory"], f'{model.name}_cost'))
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if not os.path.exists(cost_dir):
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os.makedirs(cost_dir)
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os.chdir(cost_dir)
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ext_cost_fun.generate(fun_name, casadi_codegen_opts)
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ext_cost_fun_jac_hess.generate(fun_name_hess, casadi_codegen_opts)
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ext_cost_fun_jac.generate(fun_name_jac, casadi_codegen_opts)
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os.chdir(cwd)
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return
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def generate_c_code_nls_cost( model, cost_name, stage_type, opts ):
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casadi_codegen_opts = dict(mex=False, casadi_int='int', casadi_real='double')
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x = model.x
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z = model.z
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p = model.p
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u = model.u
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symbol = get_casadi_symbol(x)
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if stage_type == 'terminal':
|
|
middle_name = '_cost_y_e'
|
|
u = symbol('u', 0, 0)
|
|
y_expr = model.cost_y_expr_e
|
|
|
|
elif stage_type == 'initial':
|
|
middle_name = '_cost_y_0'
|
|
y_expr = model.cost_y_expr_0
|
|
|
|
elif stage_type == 'path':
|
|
middle_name = '_cost_y'
|
|
y_expr = model.cost_y_expr
|
|
|
|
# change directory
|
|
cwd = os.getcwd()
|
|
cost_dir = os.path.abspath(os.path.join(opts["code_export_directory"], f'{model.name}_cost'))
|
|
if not os.path.exists(cost_dir):
|
|
os.makedirs(cost_dir)
|
|
os.chdir(cost_dir)
|
|
|
|
# set up expressions
|
|
cost_jac_expr = ca.transpose(ca.jacobian(y_expr, ca.vertcat(u, x)))
|
|
dy_dz = ca.jacobian(y_expr, z)
|
|
ny = casadi_length(y_expr)
|
|
|
|
y = symbol('y', ny, 1)
|
|
|
|
y_adj = ca.jtimes(y_expr, ca.vertcat(u, x), y, True)
|
|
y_hess = ca.jacobian(y_adj, ca.vertcat(u, x))
|
|
|
|
## generate C code
|
|
suffix_name = '_fun'
|
|
fun_name = cost_name + middle_name + suffix_name
|
|
y_fun = ca.Function( fun_name, [x, u, z, p], [ y_expr ])
|
|
y_fun.generate( fun_name, casadi_codegen_opts )
|
|
|
|
suffix_name = '_fun_jac_ut_xt'
|
|
fun_name = cost_name + middle_name + suffix_name
|
|
y_fun_jac_ut_xt = ca.Function(fun_name, [x, u, z, p], [ y_expr, cost_jac_expr, dy_dz ])
|
|
y_fun_jac_ut_xt.generate( fun_name, casadi_codegen_opts )
|
|
|
|
suffix_name = '_hess'
|
|
fun_name = cost_name + middle_name + suffix_name
|
|
y_hess = ca.Function(fun_name, [x, u, z, y, p], [ y_hess ])
|
|
y_hess.generate( fun_name, casadi_codegen_opts )
|
|
|
|
os.chdir(cwd)
|
|
|
|
return
|
|
|
|
|
|
|
|
def generate_c_code_conl_cost(model, cost_name, stage_type, opts):
|
|
|
|
casadi_codegen_opts = dict(mex=False, casadi_int='int', casadi_real='double')
|
|
|
|
x = model.x
|
|
z = model.z
|
|
p = model.p
|
|
|
|
symbol = get_casadi_symbol(x)
|
|
|
|
if stage_type == 'terminal':
|
|
u = symbol('u', 0, 0)
|
|
|
|
yref = model.cost_r_in_psi_expr_e
|
|
inner_expr = model.cost_y_expr_e - yref
|
|
outer_expr = model.cost_psi_expr_e
|
|
res_expr = model.cost_r_in_psi_expr_e
|
|
|
|
suffix_name_fun = '_conl_cost_e_fun'
|
|
suffix_name_fun_jac_hess = '_conl_cost_e_fun_jac_hess'
|
|
|
|
custom_hess = model.cost_conl_custom_outer_hess_e
|
|
|
|
elif stage_type == 'initial':
|
|
u = model.u
|
|
|
|
yref = model.cost_r_in_psi_expr_0
|
|
inner_expr = model.cost_y_expr_0 - yref
|
|
outer_expr = model.cost_psi_expr_0
|
|
res_expr = model.cost_r_in_psi_expr_0
|
|
|
|
suffix_name_fun = '_conl_cost_0_fun'
|
|
suffix_name_fun_jac_hess = '_conl_cost_0_fun_jac_hess'
|
|
|
|
custom_hess = model.cost_conl_custom_outer_hess_0
|
|
|
|
elif stage_type == 'path':
|
|
u = model.u
|
|
|
|
yref = model.cost_r_in_psi_expr
|
|
inner_expr = model.cost_y_expr - yref
|
|
outer_expr = model.cost_psi_expr
|
|
res_expr = model.cost_r_in_psi_expr
|
|
|
|
suffix_name_fun = '_conl_cost_fun'
|
|
suffix_name_fun_jac_hess = '_conl_cost_fun_jac_hess'
|
|
|
|
custom_hess = model.cost_conl_custom_outer_hess
|
|
|
|
# set up function names
|
|
fun_name_cost_fun = model.name + suffix_name_fun
|
|
fun_name_cost_fun_jac_hess = model.name + suffix_name_fun_jac_hess
|
|
|
|
# set up functions to be exported
|
|
outer_loss_fun = ca.Function('psi', [res_expr, p], [outer_expr])
|
|
cost_expr = outer_loss_fun(inner_expr, p)
|
|
|
|
outer_loss_grad_fun = ca.Function('outer_loss_grad', [res_expr, p], [ca.jacobian(outer_expr, res_expr).T])
|
|
|
|
if custom_hess is None:
|
|
outer_hess_fun = ca.Function('inner_hess', [res_expr, p], [ca.hessian(outer_loss_fun(res_expr, p), res_expr)[0]])
|
|
else:
|
|
outer_hess_fun = ca.Function('inner_hess', [res_expr, p], [custom_hess])
|
|
|
|
Jt_ux_expr = ca.jacobian(inner_expr, ca.vertcat(u, x)).T
|
|
Jt_z_expr = ca.jacobian(inner_expr, z).T
|
|
|
|
cost_fun = ca.Function(
|
|
fun_name_cost_fun,
|
|
[x, u, z, yref, p],
|
|
[cost_expr])
|
|
|
|
cost_fun_jac_hess = ca.Function(
|
|
fun_name_cost_fun_jac_hess,
|
|
[x, u, z, yref, p],
|
|
[cost_expr, outer_loss_grad_fun(inner_expr, p), Jt_ux_expr, Jt_z_expr, outer_hess_fun(inner_expr, p)]
|
|
)
|
|
# change directory
|
|
cwd = os.getcwd()
|
|
cost_dir = os.path.abspath(os.path.join(opts["code_export_directory"], f'{model.name}_cost'))
|
|
if not os.path.exists(cost_dir):
|
|
os.makedirs(cost_dir)
|
|
os.chdir(cost_dir)
|
|
|
|
# generate C code
|
|
cost_fun.generate(fun_name_cost_fun, casadi_codegen_opts)
|
|
cost_fun_jac_hess.generate(fun_name_cost_fun_jac_hess, casadi_codegen_opts)
|
|
|
|
os.chdir(cwd)
|
|
|
|
return
|
|
|
|
|
|
################
|
|
# Constraints
|
|
################
|
|
def generate_c_code_constraint( model, con_name, is_terminal, opts ):
|
|
|
|
casadi_codegen_opts = dict(mex=False, casadi_int='int', casadi_real='double')
|
|
|
|
# load constraint variables and expression
|
|
x = model.x
|
|
p = model.p
|
|
|
|
symbol = get_casadi_symbol(x)
|
|
|
|
if is_terminal:
|
|
con_h_expr = model.con_h_expr_e
|
|
con_phi_expr = model.con_phi_expr_e
|
|
# create dummy u, z
|
|
u = symbol('u', 0, 0)
|
|
z = symbol('z', 0, 0)
|
|
else:
|
|
con_h_expr = model.con_h_expr
|
|
con_phi_expr = model.con_phi_expr
|
|
u = model.u
|
|
z = model.z
|
|
|
|
if (not is_empty(con_h_expr)) and (not is_empty(con_phi_expr)):
|
|
raise Exception("acados: you can either have constraint_h, or constraint_phi, not both.")
|
|
|
|
if (is_empty(con_h_expr) and is_empty(con_phi_expr)):
|
|
# both empty -> nothing to generate
|
|
return
|
|
|
|
if is_empty(con_h_expr):
|
|
constr_type = 'BGP'
|
|
else:
|
|
constr_type = 'BGH'
|
|
|
|
if is_empty(p):
|
|
p = symbol('p', 0, 0)
|
|
|
|
if is_empty(z):
|
|
z = symbol('z', 0, 0)
|
|
|
|
if not (is_empty(con_h_expr)) and opts['generate_hess']:
|
|
# multipliers for hessian
|
|
nh = casadi_length(con_h_expr)
|
|
lam_h = symbol('lam_h', nh, 1)
|
|
|
|
# set up & change directory
|
|
cwd = os.getcwd()
|
|
constraints_dir = os.path.abspath(os.path.join(opts["code_export_directory"], f'{model.name}_constraints'))
|
|
if not os.path.exists(constraints_dir):
|
|
os.makedirs(constraints_dir)
|
|
os.chdir(constraints_dir)
|
|
|
|
# export casadi functions
|
|
if constr_type == 'BGH':
|
|
if is_terminal:
|
|
fun_name = con_name + '_constr_h_e_fun_jac_uxt_zt'
|
|
else:
|
|
fun_name = con_name + '_constr_h_fun_jac_uxt_zt'
|
|
|
|
jac_ux_t = ca.transpose(ca.jacobian(con_h_expr, ca.vertcat(u,x)))
|
|
jac_z_t = ca.jacobian(con_h_expr, z)
|
|
constraint_fun_jac_tran = ca.Function(fun_name, [x, u, z, p], \
|
|
[con_h_expr, jac_ux_t, jac_z_t])
|
|
|
|
constraint_fun_jac_tran.generate(fun_name, casadi_codegen_opts)
|
|
if opts['generate_hess']:
|
|
|
|
if is_terminal:
|
|
fun_name = con_name + '_constr_h_e_fun_jac_uxt_zt_hess'
|
|
else:
|
|
fun_name = con_name + '_constr_h_fun_jac_uxt_zt_hess'
|
|
|
|
# adjoint
|
|
adj_ux = ca.jtimes(con_h_expr, ca.vertcat(u, x), lam_h, True)
|
|
# hessian
|
|
hess_ux = ca.jacobian(adj_ux, ca.vertcat(u, x))
|
|
|
|
adj_z = ca.jtimes(con_h_expr, z, lam_h, True)
|
|
hess_z = ca.jacobian(adj_z, z)
|
|
|
|
# set up functions
|
|
constraint_fun_jac_tran_hess = \
|
|
ca.Function(fun_name, [x, u, lam_h, z, p], \
|
|
[con_h_expr, jac_ux_t, hess_ux, jac_z_t, hess_z])
|
|
|
|
# generate C code
|
|
constraint_fun_jac_tran_hess.generate(fun_name, casadi_codegen_opts)
|
|
|
|
if is_terminal:
|
|
fun_name = con_name + '_constr_h_e_fun'
|
|
else:
|
|
fun_name = con_name + '_constr_h_fun'
|
|
h_fun = ca.Function(fun_name, [x, u, z, p], [con_h_expr])
|
|
h_fun.generate(fun_name, casadi_codegen_opts)
|
|
|
|
else: # BGP constraint
|
|
if is_terminal:
|
|
fun_name = con_name + '_phi_e_constraint'
|
|
r = model.con_r_in_phi_e
|
|
con_r_expr = model.con_r_expr_e
|
|
else:
|
|
fun_name = con_name + '_phi_constraint'
|
|
r = model.con_r_in_phi
|
|
con_r_expr = model.con_r_expr
|
|
|
|
nphi = casadi_length(con_phi_expr)
|
|
con_phi_expr_x_u_z = ca.substitute(con_phi_expr, r, con_r_expr)
|
|
phi_jac_u = ca.jacobian(con_phi_expr_x_u_z, u)
|
|
phi_jac_x = ca.jacobian(con_phi_expr_x_u_z, x)
|
|
phi_jac_z = ca.jacobian(con_phi_expr_x_u_z, z)
|
|
|
|
hess = ca.hessian(con_phi_expr[0], r)[0]
|
|
for i in range(1, nphi):
|
|
hess = ca.vertcat(hess, ca.hessian(con_phi_expr[i], r)[0])
|
|
|
|
r_jac_u = ca.jacobian(con_r_expr, u)
|
|
r_jac_x = ca.jacobian(con_r_expr, x)
|
|
|
|
constraint_phi = \
|
|
ca.Function(fun_name, [x, u, z, p], \
|
|
[con_phi_expr_x_u_z, \
|
|
ca.vertcat(ca.transpose(phi_jac_u), ca.transpose(phi_jac_x)), \
|
|
ca.transpose(phi_jac_z), \
|
|
hess,
|
|
ca.vertcat(ca.transpose(r_jac_u), ca.transpose(r_jac_x))])
|
|
|
|
constraint_phi.generate(fun_name, casadi_codegen_opts)
|
|
|
|
# change directory back
|
|
os.chdir(cwd)
|
|
|
|
return
|
|
|
|
|