yaocptool.methods.classic package

Submodules

yaocptool.methods.classic.collocationscheme module

Created on Thu Jul 13 17:08:34 2017

@author: marco

class CollocationScheme(solution_method)

Bases: yaocptool.methods.base.discretizationschemebase.DiscretizationSchemeBase

create_initial_guess(p=None, theta=None)

Create an initial guess for the optimal control problem using problem.x_0, problem.y_guess, problem.u_guess, and a given p and theta (for p_opt and theta_opt) if they are given. If y_guess or u_guess are None the initial guess uses a vector of zeros of appropriate size.

Parameters:

• p – Optimization parameters
• theta – Optimization theta

Returns:

create_initial_guess_with_simulation(u=None, p=None, theta=None)

Create an initial guess for the optimal control problem using by simulating with a given control u, and a given p and theta (for p_opt and theta_opt) if they are given. If no u is given the value of problem.u_guess is used, or problem.last_u, then a vector of zeros of appropriate size is used. If no p or theta is given, an vector of zeros of appropriate size is used.

Parameters:

• u –
• p – Optimization parameters
• theta – Optimization theta

Returns:

discretize(x_0=None, p=None, theta=None, last_u=None)

Discretize the OCP, returning a Optimization Problem

Parameters:

• x_0 – initial condition
• p – parameters
• theta – theta parameters
• last_u – last applied control

Return type:

NonlinearOptimizationProblem

get_system_at_given_times(x, y, u, time_dict=None, p=None, theta=None, functions=None, start_at_t_0=False)

Parameters:

• x (list) –
• y (list) –
• u (list) –
• time_dict (dict) – Dictionary of simulations times, where the KEY is the finite_element and the VALUE list a list of desired times example : {1:{‘t_0’: 0.0, ‘x’:[0.0, 0.1, 0.2], y:[0.2]}}
• p – list
• theta – dict
• start_at_t_0 – bool If TRUE the simulations in each finite_element will start at the element t_0, Otherwise the simulation will start the end of the previous element
• functions (dict) – Dictionary of Functions to be evaluated, KEY is the function identifier, VALUE is a CasADi Function with model.all_sym as input

set_data_to_optimization_result_from_raw_data(optimization_result, raw_solution_dict)

Set the raw data received from the solver and put it in the Optimization Result object :type optimization_result: yaocptool.methods.base.optimizationresult.OptimizationResult :type raw_solution_dict: dict

time_interpolation

time_interpolation_algebraics

time_interpolation_states

unpack_decision_variables(decision_variables, all_subinterval=True)

Return a structured data from the decision variables vector

Returns: (x_data, y_data, u_data, p_opt, eta)

Parameters:

• decision_variables – DM
• all_subinterval – bool

Returns:

tuple

yaocptool.methods.classic.directmethod module

Created on Fri Oct 21 16:40:15 2016

@author: marco

class DirectMethod(problem, **kwargs)

Bases: yaocptool.methods.base.solutionmethodsbase.SolutionMethodsBase

__init__(problem, **kwargs)

Parameters:

• problem – yaocptool.modelling.ocp.OptimalControlProblem
• integrator_type – str
• solution_method – str
• degree – int
• discretization_scheme – str ‘multiple-shooting’ | ‘collocation’

prepare()

Perform pre solve check, check for sizes and types. If it fails raise an Exception

yaocptool.methods.classic.directmethod.py.93e0bc8cbe8d318afa87baa70c15355a module

yaocptool.methods.classic.indirectmethod module

Created on Fri Oct 21 16:39:52 2016

@author: marco

class IndirectMethod(problem, create_cost_state=False, **kwargs)

Bases: yaocptool.methods.base.solutionmethodsbase.SolutionMethodsBase

__init__(problem, create_cost_state=False, **kwargs)

Parameters:

• problem – yaocptool.modelling.ocp.OptimalControlProblem
• create_cost_state (bool) – If True a cost state will be created to keep track of the dynamic cost.
• integrator_type – str
• solution_method – str
• degree – int
• discretization_scheme – str ‘multiple-shooting’ | ‘collocation’

calculate_optimal_control()

degree_control

prepare()

Perform pre solve check, check for sizes and types. If it fails raise an Exception

replace_with_optimal_control(u_opt)

yaocptool.methods.classic.multipleshooting module

Created on Thu Jul 13 17:08:34 2017

@author: marco

class MultipleShootingScheme(solution_method)

Bases: yaocptool.methods.base.discretizationschemebase.DiscretizationSchemeBase

create_initial_guess(p=None, theta=None)

Create an initial guess for the optimal control problem using problem.x_0, problem.y_guess, problem.u_guess, and a given p and theta (for p_opt and theta_opt) if they are given. If y_guess or u_guess are None the initial guess uses a vector of zeros of appropriate size.

Parameters:

• p – Optimization parameters
• theta – Optimization theta

Returns:

create_initial_guess_with_simulation(u=None, p=None, theta=None)

Create an initial guess for the optimal control problem using by simulating with a given control u, and a given p and theta (for p_opt and theta_opt) if they are given. If no u is given the value of problem.u_guess is used, or problem.last_u, then a vector of zeros of appropriate size is used. If no p or theta is given, an vector of zeros o appropriate size is used.

Parameters:

• u – Control initial guess
• p – Optimization parameters
• theta – Optimization theta

Returns:

discretize(x_0=None, p=None, theta=None, last_u=None)

Discretize the OCP, returning a Optimization Problem

Parameters:

• x_0 – initial condition
• p – parameters
• theta – theta parameters
• last_u – last applied control

Return type:

NonlinearOptimizationProblem

get_system_at_given_times(x_var, y_var, u_var, time_dict=None, p=None, theta=None, functions=None, start_at_t_0=False)

Parameters:

• x_var – List[List[MX]]
• y_var – List[List[MX]]
• u_var – List[List[MX]]
• p – list
• theta – dict
• start_at_t_0 – bool If TRUE the simulations in each finite_element will start at the element t_0, Otherwise the simulation will start the end of the previous element
• functions – Dict[str, Function|Dict[int] dictionary of Functions to be evaluated, KEY is the function identifier, VALUE is a CasADi Function with model.all_sym as input

set_data_to_optimization_result_from_raw_data(optimization_result, raw_solution_dict)

Set the raw data received from the solver and put it in the Optimization Result object :param OptimizationResult optimization_result: :param dict raw_solution_dict:

unpack_decision_variables(decision_variables)

Return a structured data from the decision variables vector

Returns: (x_data, y_data, u_data, p_opt, eta, theta_opt)

Parameters:decision_variables – DM Returns:tuple

Module contents