ADCS.controller.plan_and_track.planner_settings module

class ADCS.controller.plan_and_track.planner_settings.PlannerSettings(est_sat, dt_control=1.0, pass1_config=None, pass2_config=None, cost_main=None, cost_second=None, cost_tvlqr=None, init_traj=None, dt_tvlqr=1, tvlqr_len=60, tvlqr_overlap=15, dt_tp=30.0, precalculation_time=100, traj_overlap=150, bdot_on=1, debug_plot_on=False, include_gg=False, include_resdipole=False, include_prop=False, include_drag=False, include_srp=False, include_gendist=False)

Bases: object

Container class for all configuration parameters required by the trajectory planner.

This class aggregates physical parameters, solver configurations, actuator limits, cost weights, timing parameters, and disturbance models into a single interface consumed by the C++ trajectory planner.

Mathematically, the planner solves a constrained optimal control problem of the form

\[\min_{\mathbf{u}(t)} \; \int_{0}^{T} \ell\bigl(\mathbf{x}(t), \mathbf{u}(t)\bigr)\, dt + \ell_N\bigl(\mathbf{x}(T)\bigr)\]

subject to nonlinear rigid-body dynamics

\[\dot{\mathbf{x}} = f(\mathbf{x}, \mathbf{u}, \mathbf{d})\]

actuator saturation limits, and optional environmental disturbance torques \(\mathbf{d}\).

The settings in this class parameterize both the dynamics and the optimization algorithm, including multi-pass augmented Lagrangian iLQR configurations.

Parameters:

• est_sat (EstimatedSatellite) – Estimated satellite model providing inertia, actuator definitions, state dimension, and disturbances.

• dt_control (float) – Control update period in seconds.

• pass1_config (SolverPassConfig) – Solver configuration for the first (exploration) optimization pass.

• pass2_config (SolverPassConfig) – Solver configuration for the second (refinement) optimization pass.

• cost_main (CostWeights) – Cost weights for the main optimization pass.

• cost_second (CostWeights) – Cost weights for the second optimization pass.

• cost_tvlqr (CostWeights) – Cost weights for time-varying LQR tracking.

• init_traj (InitTrajConfig) – Initial trajectory generation configuration.

• dt_tvlqr (float) – Time step for TVLQR controller discretization.

• tvlqr_len (float) – Duration of each TVLQR segment in seconds.

• tvlqr_overlap (float) – Overlap duration between consecutive TVLQR segments in seconds.

• dt_tp (float) – Time step used by the trajectory planner.

• precalculation_time (float) – Planning horizon precomputation time in seconds.

• traj_overlap (float) – Overlap duration between planned trajectories.

• bdot_on (int) – Flag enabling B-dot detumbling logic.

• debug_plot_on (bool) – Flag enabling debug plotting.

• include_gg (bool) – Flag enabling gravity-gradient disturbance modeling.

• include_resdipole (bool) – Flag enabling residual dipole disturbance modeling.

• include_prop (bool) – Flag enabling propulsion disturbance modeling.

• include_drag (bool) – Flag enabling aerodynamic drag disturbance modeling.

• include_srp (bool) – Flag enabling solar radiation pressure disturbance modeling.

• include_gendist (bool) – Flag enabling generic disturbance torque modeling.

initTrajSettings()

Return initial trajectory generation parameters.

These settings define how the initial guess trajectory is constructed prior to optimization, which can significantly affect convergence.

Returns:

Tuple containing initial trajectory configuration parameters.

Return type:

Tuple[float, float, Tuple, Tuple]

mainAlilqrSettings()

Return solver settings for the first augmented Lagrangian iLQR pass.

The first pass prioritizes global exploration with a low penalty parameter and increased iteration limits, solving a relaxed constrained optimization problem.

Returns:

Tuple containing line search, augmented Lagrangian, convergence, and regularization settings.

Return type:

Tuple[Tuple, Tuple, Tuple, Tuple]

optMainCostSettings()

Return cost weights for the main optimization pass.

The running and terminal costs are defined as

\[\ell = \mathbf{x}^\top \mathbf{Q} \mathbf{x} + \mathbf{u}^\top \mathbf{R} \mathbf{u}\]

with increased terminal penalties to emphasize goal convergence.

Returns:

Tuple encoding main-pass cost weights.

Return type:

Tuple

optSecondCostSettings()

Return cost weights for the second optimization pass.

These weights may differ from the main pass to emphasize constraint satisfaction or control smoothing.

Returns:

Tuple encoding second-pass cost weights.

Return type:

Tuple

optTVLQRCostSettings(tracking_LQR_formulation)

Return cost weights for the time-varying LQR controller.

The TVLQR controller minimizes a quadratic tracking cost

\[J = \int (\mathbf{x} - \mathbf{x}_r)^\top \mathbf{Q} (\mathbf{x} - \mathbf{x}_r) + \mathbf{u}^\top \mathbf{R} \mathbf{u} \, dt\]

where the formulation can be adjusted via the tracking flag.

Parameters:

tracking_LQR_formulation (int) – Integer flag selecting the tracking LQR formulation.

Returns:

Tuple encoding TVLQR cost weights.

Return type:

Tuple

planner_disturbance_settings()

Return disturbance configuration parameters for the C++ planner.

Disturbance torques are modeled as additive inputs

\[\boldsymbol{\tau}_{\text{total}} = \boldsymbol{\tau}_{\text{control}} + \boldsymbol{\tau}_{\text{dist}}\]

where the disturbance torque may include aerodynamic drag, solar radiation pressure, gravity-gradient effects, propulsion torques, residual dipole effects, or generic disturbances.

Returns:

Tuple containing disturbance enable flags, SRP coefficients, drag coefficients, normalization factor, propulsion torque, generic disturbance torque, and residual dipole torque.

Return type:

Tuple[Tuple[bool, …], numpy.ndarray, numpy.ndarray, int,

numpy.ndarray, numpy.ndarray, numpy.ndarray]

secondAlilqrSettings()

Return solver settings for the second augmented Lagrangian iLQR pass.

The second pass increases constraint penalties to enforce feasibility and refine the solution obtained from the first pass.

Returns:

Tuple containing line search, augmented Lagrangian, convergence, and regularization settings.

Return type:

Tuple[Tuple, Tuple, Tuple, Tuple]

systemSettings()

Return system-level configuration parameters for the C++ planner.

These parameters define the rigid-body dynamics discretization and numerical tolerances used by the planner. The inertia matrix enters the rotational equations of motion

\[\mathbf{J}\dot{\boldsymbol{\omega}} = \boldsymbol{\tau} - \boldsymbol{\omega} \times (\mathbf{J}\boldsymbol{\omega})\]

where \(\mathbf{J}\) is the estimated inertia tensor.

Returns:

Tuple containing inertia matrix, trajectory planner time step, TVLQR time step, numerical epsilon, TVLQR segment length, and TVLQR overlap duration.

Return type:

Tuple[numpy.ndarray, float, float, float, float, float]