latom.nlp.nlp

@authors: Alberto FOSSA’ Giuliana Elena MICELI

Classes

DirectSolver(**kwargs)

LinearSolver that uses linalg.solve or LU factor/solve.

GaussLobatto(**kwargs)

High-order Gauss Lobatto Transcription

Group(**kwargs)

Class used to group systems together; instantiate or inherit.

MultiPhaseNLP(body, sc, method, nb_seg, …)

MultiPhaseNLP transcribes a continuous-time optimal control problem in trajectory optimization constituted by multiple phases into a Non Linear Programming Problem (NLP) using the libraries OpenMDAO and dymos.

NLP(body, sc, method, nb_seg, order, solver)

NLP class transcribes a continuous-time optimal control problem in trajectory optimization into a Non Linear Programming Problem (NLP) using the OpenMDAO and dymos libraries.

Phase([from_phase])

The Phase object in Dymos.

Problem([model, driver, comm, root])

Top-level container for the systems and drivers.

Radau(**kwargs)

Radau Pseudospectral Method Transcription

RungeKutta(**kwargs)

The RungeKutta Transcription class.

SinglePhaseNLP(body, sc, method, nb_seg, …)

SinglePhaseNLP transcribes a continuous-time optimal control problem in trajectory optimization constituted by a single phase into a Non Linear Programming Problem (NLP) using the libraries OpenMDAO and dymos.

SqliteRecorder(filepath[, append, …])

Recorder that saves cases in a sqlite db.

Trajectory(**kwargs)

A Trajectory object serves as a container for one or more Phases, as well as the linkage conditions between phases.

pyOptSparseDriver(**kwargs)

Driver wrapper for pyoptsparse.
class latom.nlp.nlp.NLP(body, sc, method, nb_seg, order, solver, snopt_opts=None, rec_file=None)[source]

Bases: object

NLP class transcribes a continuous-time optimal control problem in trajectory optimization into a Non Linear Programming Problem (NLP) using the OpenMDAO and dymos libraries.

Parameters

  • body (Primary) – Instance of Primary class representing the central attracting body

  • sc (Spacecraft) – Instance of Spacecraft class representing the spacecraft

  • method (str) – Transcription method used to discretize the continuous time trajectory into a finite set of nodes, allowed gauss-lobatto, radau-ps and runge-kutta

  • nb_seg (int or tuple) – Number of segments in which each phase is discretized

  • order (int or tuple) – Transcription order within each phase, must be odd

  • solver (str) – NLP solver, must be supported by OpenMDAO

  • snopt_opts (dict or None, optional) – SNOPT optional settings expressed as key-value pairs. Refer to the SNOPT User Guide 1 for more details. Default is None

  • rec_file (str or None, optional) – Name of the file in which the computed solution is recorded or None. Default is None

Variables

  • body (Primary) – Instance of Primary class representing the central attracting body

  • sc (Spacecraft) – Instance of Spacecraft class representing the spacecraft

  • method (str) – Transcription method used to discretize the continuous time trajectory into a finite set of nodes, allowed gauss-lobatto, radau-ps and runge-kutta

  • nb_seg (int or tuple) – Number of segments in which each phase is discretized

  • order (int or tuple) – Transcription order within each phase, must be odd

  • solver (str) – NLP solver, must be supported by OpenMDAO

  • snopt_opts (dict or None) – SNOPT optional settings expressed as key-value pairs. Refer to the SNOPT User Guide 1 for more details.

  • rec_file (str or None) – Name of the file in which the computed solution is recorded or None

  • p (Problem) – OpenMDAO Problem class instance representing the NLP

  • trajectory (Trajectory) – Dymos Trajectory class instance representing the spacecraft trajectory

  • p_exp (Problem) – OpenMDAO Problem class instance representing the explicitly simulated trajectory

References

1(1,2,3,4)

Gill, Philip E., et al. User’s Guide for SNOPT Version 7.7: Software for Large-Scale Nonlinear Programming, Feb. 2019, p. 126.

setup()[source]

Set up the Jacobian type, linear solver and derivatives type.

exp_sim(rec_file=None)[source]

Explicitly simulate the implicitly obtained optimal solution using Scipy solve_ivp method.

cleanup()[source]

Clean up resources.

class latom.nlp.nlp.SinglePhaseNLP(body, sc, method, nb_seg, order, solver, ode_class, ode_kwargs, ph_name, snopt_opts=None, rec_file=None)[source]

Bases: latom.nlp.nlp.NLP

SinglePhaseNLP transcribes a continuous-time optimal control problem in trajectory optimization constituted by a single phase into a Non Linear Programming Problem (NLP) using the libraries OpenMDAO and dymos.

Parameters

  • body (Primary) – Instance of Primary class representing the central attracting body

  • sc (Spacecraft) – Instance of Spacecraft class representing the spacecraft

  • method (str) – Transcription method used to discretize the continuous time trajectory into a finite set of nodes, allowed gauss-lobatto, radau-ps and runge-kutta

  • nb_seg (int) – Number of segments in which each phase is discretized

  • order (int) – Transcription order within each phase, must be odd

  • solver (str) – NLP solver, must be supported by OpenMDAO

  • ode_class (ExplicitComponent) – Instance of OpenMDAO ExplicitComponent describing the Ordinary Differential Equations (ODEs) that drive the system dynamics

  • ode_kwargs (dict) – Keywords arguments to be passed to ode_class

  • ph_name (str) – Name of the phase within OpenMDAO

  • snopt_opts (dict or None, optional) – SNOPT optional settings expressed as key-value pairs. Refer to the SNOPT User Guide 1 for more details. Default is None

  • rec_file (str or None, optional) – Name of the file in which the computed solution is recorded or None. Default is None

Variables

  • phase (Phase) – Dymos Phase object representing the unique phase of the Trajectory class instance

  • phase_name (str) – Complete name of the Phase instance within OpenMDAO

  • state_nodes (ndarray) – Indexes corresponding to the state discretization nodes of the discretized phase

  • control_nodes (ndarray) – Indexes corresponding to the control discretization nodes of the discretized phase

  • t_all (ndarray) – Time instants corresponding to all discretization nodes [-]

  • t_state (ndarray) – Time instants corresponding to the state discretization nodes [-]

  • t_control (ndarray) – Time instants corresponding to the control discretization nodes [-]

  • idx_state_control (ndarray) – Indexes of the state discretization nodes among the control discretization nodes

  • tof (float) – Phase time of flight (TOF) [-]

set_objective()[source]

Set the NLP objective as the minimization of the opposite of the final spacecraft mass.

set_time_options(tof, t_bounds)[source]

Set the time options on the phase.

Parameters

  • tof (float) – Phase time of flight (TOF) [s]

  • t_bounds (tuple) – Time of flight lower and upper bounds expressed as a fraction of tof

set_time_guess(tof)[source]

Compute the time grid on the phase to retrieve the time instants corresponding to states, controls and all discretization nodes.

Parameters

tof (float) – Phase time of flight (TOF) [s]

cleanup()

Clean up resources.

exp_sim(rec_file=None)

Explicitly simulate the implicitly obtained optimal solution using Scipy solve_ivp method.

setup()

Set up the Jacobian type, linear solver and derivatives type.

class latom.nlp.nlp.MultiPhaseNLP(body, sc, method, nb_seg, order, solver, ode_class, ode_kwargs, ph_name, snopt_opts=None, rec_file=None)[source]

Bases: latom.nlp.nlp.NLP

MultiPhaseNLP transcribes a continuous-time optimal control problem in trajectory optimization constituted by multiple phases into a Non Linear Programming Problem (NLP) using the libraries OpenMDAO and dymos.

Parameters

  • body (Primary) – Instance of Primary class representing the central attracting body

  • sc (Spacecraft) – Instance of Spacecraft class representing the spacecraft

  • method (str) – Transcription method used to discretize the continuous time trajectory into a finite set of nodes, allowed gauss-lobatto, radau-ps and runge-kutta

  • nb_seg (int or tuple) – Number of segments in which each phase is discretized

  • order (int or tuple) – Transcription order within each phase, must be odd

  • solver (str) – NLP solver, must be supported by OpenMDAO

  • ode_class (tuple) – Instance of OpenMDAO ExplicitComponent describing the Ordinary Differential Equations (ODEs) that drive the system dynamics

  • ode_kwargs (tuple) – Keywords arguments to be passed to ode_class

  • ph_name (tuple) – Name of the phase within OpenMDAO

  • snopt_opts (dict or None, optional) – SNOPT optional settings expressed as key-value pairs. Refer to the SNOPT User Guide 1 for more details. Default is None

  • rec_file (str or None, optional) – Name of the file in which the computed solution is recorded or None. Default is None

Variables

  • transcription (list) – List of dymos transcription class instances representing the transcription method, order and segments within each phase

  • phase (list) – List of dymos Phase object representing the different phases of the Trajectory class instance

  • phase_name (list) – List of complete names of the Phase instance within OpenMDAO

set_time_phase(ti, tof, phase, phase_name)[source]

Compute the time grid within one phase to retrieve the time instants corresponding to the state, control and all discretization nodes.

Parameters

  • ti (float) – Initial time [-]

  • tof (float) – Time of flight (TOF) [-]

  • phase (Phase) – Current phase

  • phase_name (str) – Current phase name

Returns

  • state_nodes (ndarray) – Indexes corresponding to the state discretization nodes

  • control_nodes (ndarray) – Indexes corresponding to the control discretization nodes

  • t_state (ndarray) – Time instants on the state discretization nodes [-]

  • t_control (ndarray) – Time instants on the control discretization nodes [-]

  • t_all (ndarray) – Time instants on all discretization nodes [-]

cleanup()

Clean up resources.

exp_sim(rec_file=None)

Explicitly simulate the implicitly obtained optimal solution using Scipy solve_ivp method.

setup()

Set up the Jacobian type, linear solver and derivatives type.