mirror of
https://github.com/wassname/pyrobolearn.git
synced 2026-09-21 13:20:37 +08:00
28 lines
1.2 KiB
Python
28 lines
1.2 KiB
Python
# MPC: Model Predictive Control
|
|
|
|
class MPC(object):
|
|
r"""Model Predictive Control
|
|
|
|
Type: Optimal Control
|
|
|
|
MPC optimizes for a finite time-horizon :math:`T` (i.e. compute the optimal :math:`\{u_t, u_{t+1},..., u_T\}`
|
|
given the dynamical system :math:`x_{t+1} = f(x_t, u_t)` and cost :math:`c(x_t,u_t)`), executes the first best
|
|
found control law :math:`u_t`, lets the system goes to the next state :math:`x_{t+1}`, and then re-optimize again
|
|
for each next time step. The finite time-horizon allows to take into account close future events, while
|
|
re-optimizing at each time step allows to deal with the discrepancy between the modeled and real dynamical systems.
|
|
|
|
Notes:
|
|
* MPC vs LQR: LQR assumes a linear dynamical system and optimizes for the whole time horizon providing us
|
|
the single optimal solution, while MPC optimizes in a receding time window at each time step
|
|
resulting in a suboptimal solution but more robust to various perturbations, uncertainties, and so on
|
|
not accounted/modeled by our dynamical system.
|
|
|
|
References:
|
|
[1]
|
|
"""
|
|
|
|
def __init__(self):
|
|
pass
|
|
|
|
def compute(self, x):
|
|
pass |