yaocptool.methods package

Subpackages

• yaocptool.methods.base package
  • Submodules
  • yaocptool.methods.base.discretizationschemebase module
  • yaocptool.methods.base.optimizationresult module
  • yaocptool.methods.base.solutionmethodinterface module
  • yaocptool.methods.base.solutionmethodsbase module
  • Module contents
• yaocptool.methods.classic package
  • Submodules
  • yaocptool.methods.classic.collocationscheme module
  • yaocptool.methods.classic.directmethod module
  • yaocptool.methods.classic.directmethod.py.93e0bc8cbe8d318afa87baa70c15355a module
  • yaocptool.methods.classic.indirectmethod module
  • yaocptool.methods.classic.multipleshooting module
  • Module contents
• yaocptool.methods.network package
  • Submodules
  • yaocptool.methods.network.distributedaugmetedlagrangian module
  • Module contents

Submodules

yaocptool.methods.augmented_lagrangian module

Created on Sat Oct 22 16:53:36 2016

@author: marco

class AugmentedLagrangian(problem, ocp_solver_class, solver_options=None, **kwargs)

Bases: yaocptool.methods.base.solutionmethodsbase.SolutionMethodsBase

For a minimization problem in the form

min f(x,u) = int L(x,u) dt s.t.: dot{x} = f(x,u), g_ineq (x,u) leq 0

Transforms the problem in a sequence of solution of the problem

min f(x,u) = int L(x,u) -mu sum log(-g_ineq(x,u)) dt s.t.: dot{x} = f(x,u),

__init__(problem, ocp_solver_class, solver_options=None, **kwargs)

Augmented Lagrange Method (Aguiar 2016)

Parameters:

• yaocptool.modelling.OptimalControlProblem – Optimal Control Problem
• ocp_solver_class (type) – Class of Solution Method (Direct/Indirect Method)
• solver_options – Options for the Solution Method class given
• relax_algebraic_index – Index for the algebraic equations that will be relaxed, if not given all the algebraic equations will be relaxed
• relax_algebraic_var_index – Index for the algebraic variables that will be relaxed, if not given it will be assumed the same as the ‘relax_algebraic_index’
• relax_state_bounds – This relax the states bounds and put then in the objective, via an algebraic variable
• kwargs –

call_solver(initial_guess=None, p=None, theta=None, x_0=None, last_u=None, initial_guess_dict=None)

create_nu_initial_guess()

create_optimization_problem()

create_optimization_result(raw_solution_dict, p=None, theta=None, x_0=None)

discretizer

static join_nu_to_theta(theta, nu)

model

Return type:yaocptool.modelling.SystemModel

time_interpolation_nu

yaocptool.methods.distributed_solution module

class DistributedSolution(subsystem_classes_list, parameters_list, connection_list, main_to_subsystems_list=None, **kwargs)

Bases: object

__init__(subsystem_classes_list, parameters_list, connection_list, main_to_subsystems_list=None, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

initialize()

kill()

n_subsystems

solve(initial_guess=None, p=None, theta=None, x_0=None, last_u=None, initial_guess_dict=None)

start()

Module contents