06: Trajectory Planner (Plan-and-Track)

In this tutorial, we solve a challenging magnetic attitude-control problem for BeaverCube 1: quick retargeting with only 3 magnetorquers (MTQs) and no reaction wheels.

The scenario uses the Plan-and-Track controller with trajectory optimization and TVLQR tracking. This controller assumes high-fidelity field knowledge during planning, which can improve accuracy for aggressive maneuvers.

A trajectory planner computes a feasible, optimized reference trajectory that the spacecraft can track while satisfying actuator limits and pointing objectives.

For more details on related trajectory optimization methods, see SALTRO documentation website.

Simulation Configuration

Component	Description
Satellite	BeaverCube 1 generated from the built-in satellite factory.
Actuation	3 MTQ-only attitude control (underactuated, no reaction wheels).
Controller	Plan-and-Track LQR with trajectory optimization settings and two solver passes.
Planner Costs	Tuned angle and angular-rate weights for trajectory and TVLQR tracking.
Goals	Piecewise inertial pointing timeline with target and no-goal intervals.
Orbit	Circular-like LEO test orbit initialized from position and velocity vectors.

import os
import sys
sys.path.append(os.path.abspath(os.path.join(__file__, "../../..")))
import ADCS as ADCS
import numpy as np
import matplotlib.pyplot as plt

satellite = ADCS.satellite_factory.create_beavercube1_cubesat()

x_0 = np.array([0, 0, 0] + [1, 0, 0, 0]) # w, q, h

planner_settings = ADCS.controller.plan_and_track.PlannerSettings(est_sat=satellite, bdot_on=0, dt_tp=50, dt_tvlqr=1.0)

planner_settings.verbosity = False
planner_settings.cost_main.use_full_cost_hessian = True
planner_settings.pass1.regularization.use_dynamics_hess = 1
planner_settings.init_traj.bdot_gain = 500
planner_settings.pass1.aug_lag.penalty_init = 1e-3
planner_settings.pass1.aug_lag.penalty_scale = 10
planner_settings.pass1.convergence.max_outer_iter = 15
planner_settings.pass1.convergence.max_inner_iter = 40
planner_settings.pass2.aug_lag.penalty_init = 1e5
planner_settings.pass2.aug_lag.penalty_scale = 10
planner_settings.pass2.convergence.max_outer_iter = 8
planner_settings.pass2.convergence.max_inner_iter = 20

planner_settings.cost_main = ADCS.controller.plan_and_track.CostWeights(
        angle=1e1,
        angle_N=1e1,
        ang_vel=1e5,
        ang_vel_N=1e5,
        ang_vel_err_dir=1e2,
        ang_vel_err_dir_N=0.0,
        ang_vel_mag=0.0,
        ang_vel_mag_N=0.0,
        control_mult=1.0,
        ang_cost_func_type=2,
    )

planner_settings.cost_second = planner_settings.cost_main

planner_settings.cost_tvlqr = ADCS.controller.plan_and_track.CostWeights(
        angle=1e5,
        angle_N=1e6,
        ang_vel=1e6,
        ang_vel_N=1e8,
        ang_vel_mag=0.0,
        ang_vel_mag_N=0.0,
        control_mult=1.0,
        ang_cost_func_type=2,
    )

controller = ADCS.controller.Plan_and_Track_LQR(est_sat=satellite, planner_settings=planner_settings)

os0 = ADCS.Orbital_State(ephem=ADCS.Ephemeris(), J2000=0.22, R=np.array([5000, 0, 5000]), V=np.array([0, 7.5, 0]))
goal_timeline = {0.0: ADCS.goals.ECI_Goal(np.array([1, 0, 0])), 300.0: ADCS.goals.No_Goal(), 400.0: ADCS.goals.ECI_Goal(np.array([0, 1, 0])), 700.0: ADCS.goals.No_Goal(), 800.0: ADCS.goals.ECI_Goal(np.array([0, 0, 1]))}
goallist = ADCS.GoalList(goal_timeline=goal_timeline, time_units="seconds", start_juliantime=0.22)

results = ADCS.simulate(
    x=x_0,
    satellite=satellite,
    controller=controller,
    goal=goallist,
    os0=os0,
    dt=1.0,
    tf=1000.0
)

ADCS.plot(
    results,
    ADCS.plots.AnimationPlot(),
    layout=(1,1),
    title="3+1 ALTRO Reduced",
)

ADCS.plot(
    results,
    ADCS.plots.AttitudePlot(sources=["real", "reference"]),
    layout=(1,1),
    title="3+0 ALTRO Mixed",
)

ADCS.plot(
    results,
    ADCS.plots.AngularVelocityPlotCombined(sources=["real"]),
    ADCS.plots.ControlPlotCombined(title="Magnetorquer Commands", units="Am²"),
    ADCS.plots.TargetHistogram(bin_width=5.0),
    ADCS.plots.TargetPlot(modes=["real_target"], title="Target Tracking"),
    layout=(2,2),
    title="3+0 ALTRO Mixed",
)

ADCS.plot(
    results,
    ADCS.plots.ControlPlotSingle(index=0, title="Magnetorquer 1", units="Am²"),
    ADCS.plots.ControlPlotSingle(index=1, title="Magnetorquer 2", units="Am²"),
    ADCS.plots.ControlPlotSingle(index=2, title="Magnetorquer 3", units="Am²"),
    layout=(3,1),
    title="3+0 ALTRO Mixed",
)

plt.show()

This Plan-and-Track setup is accurate but computationally heavier, which is expected for an optimization-based controller with strong field knowledge assumptions.

Simulation Results

Simulation Results

How To Run

Install and build prerequisites:

• docs/Install_Trajectory_Planner.md

Then run:

python examples/tutorials/06_trajectory_planner.py