Following @zuorenchen's review note on #13, seed the remaining noisy sensor
fixtures (noisy_rotated_accelerometer, noisy_rotated_gyroscope) the same way
noisy_barometer already is, so the whole sensor suite is deterministic instead
of relying on statistical tolerance bounds. Uses the seed argument added in #13.

* Add actuator dynamics in TVC and ThrottleControl

* Refactor actuator_tau parameters to use None as default in TVC and ThrottleControl

* Add actuator dynamics to RollControl

* Move actuators to a new actuator folder and rename actuator classes

* Refactor roll, throttle, and thrust vector acutator with a new actuator class

* Add pytest for all actuators

* Update rocket.py and flight.py for actuator class update

* Fix actuator class calls

* Update active control example with new actuator class

* Update time_overshoot in flight.py to align rocketpy commit #45a89

RocketPy-Team@45a891e

* Fix actuator unit tests

* Enhance ThrustVectorActuator2D with serialization methods and improve documentation

* Fix comment msg

* Fix pylint unused parameters

* Remove unused import

* Fix pylint warnings

* Refactor __run_in_serial() to fix pylint too much statement warning

* Fix: use warning instead of printf

* Fix comment

* Fix: use warning instead of printf

* Run ruff format

* Fix thrust3 and effective_thrust name

* Fix pytests

* Fix pytest utilities

* Fix integration/gnss pytest

* Update readme

* ENH: seed sensor measurement noise per instance (#13)

* ENH: seed sensor measurement noise per instance

Sensor noise (white noise and random-walk bias drift in InertialSensor
and ScalarSensor, plus the GNSS position/velocity accuracy) was drawn
from the global numpy RNG. That made it impossible to reproduce from a
seed, and unsafe under multiprocess where workers share the global state.

Add a seed argument to the sensor constructors and draw all noise from a
per-instance numpy Generator. seed=None keeps the random-by-default
behaviour but per instance, so it no longer touches the global RNG,
matching how StochasticModel and MonteCarlo already seed. A
freshly-constructed sensor replays the same sequence, so reproducibility
lives at construction rather than in _reset.

Adds tests/unit/sensors/test_sensor_seeding.py.

* TST: seed the noisy_barometer fixture to fix a pre-existing flake

test_noisy_barometer asserts a barometer reading within rel=0.03 of the clean
pressure, but the reading carries an unseeded Gaussian noise draw plus a
+1000 Pa constant_bias the expected value omits, leaving only ~+2.42 sigma of
headroom, so it already flaked at ~0.76% on develop (a 20k-trial check: develop
0.77%, this branch 0.75%). Passing a fixed seed makes it deterministic and also
exercises the new sensor seed argument.

* TST: seed the noisy accelerometer and gyroscope fixtures (#14)

Following @zuorenchen's review note on #13, seed the remaining noisy sensor
fixtures (noisy_rotated_accelerometer, noisy_rotated_gyroscope) the same way
noisy_barometer already is, so the whole sensor suite is deterministic instead
of relying on statistical tolerance bounds. Uses the seed argument added in #13.

* ENH: pull v1.13 from RocketPy (#16)

* test: characterization tests for Flight.step_simulation() (#11)

* test: characterization tests for Flight.step_simulation()

Verify the stepped-simulation API (run_simulation=False plus repeated
step_simulation() calls) reproduces a one-shot simulate():

- initial step state is unfinished at the first phase
- stepping visits multiple phases and reaches the finished state
- the stepped trajectory (final t and the full solution array) matches simulate()
  to a tight tolerance, robust to LSODA last-bit noise across platforms
- post_process_simulation / initialize_prints_plots fire on finish
  (t_final, prints, plots present)
- stepping after finished is a no-op

Uses the parachute-free calisto flight (parachute triggers are not migrated into
the stepping path); the twin's launch parameters are read back from the
reference so it cannot drift. Scope: uncontrolled stepping only.

* test: cover controlled stepping in step_simulation()

Inject a roll command between step_simulation() calls (the Balloon Popping
Challenge use case) and assert the trajectory responds:

- a sustained command spins the body up (roll rate w3 grows) while a neutral
  command leaves w3 at zero
- a mid-flight command reversal turns the roll rate around -- only possible if
  the command is re-read every step (a latched command could not)
- a zero command leaves the angular state bit-identical to an uncontrolled run,
  isolating the divergence as the command, not the actuator's presence

Uses time_overshoot=False (as BPC does) so each step advances one solver node
and the command is injected per timestep. Tolerances/thresholds are used rather
than bit-exact equality, to stay robust to LSODA last-bit noise across platforms.

---------

Co-authored-by: RickyRicato <rickywang44@gmail.com>
Co-authored-by: ChiChun Wang <99960850+chichunwang@users.noreply.github.com>
Co-authored-by: 秀吉 <84045975+thc1006@users.noreply.github.com>