Bandwidth reduction methods for packetized Model Predictive Control (MPC) over lossy networks. Combines multi-horizon MPC with input-to-state triggering to reduce communication overhead while maintaining control performance. Activation: packetized MPC, bandwidth reduction, networked control.
This skill implements bandwidth reduction techniques for Model Predictive Control (MPC) operating over lossy communication channels. Combines multi-horizon MPC formulation with input-to-state triggering mechanisms.
import numpy as np
from typing import Tuple, Optional, Callable
class MultiHorizonMPC:
def __init__(self, system_dynamics: Callable, horizon_lengths: np.ndarray,
Q: np.ndarray, R: np.ndarray):
self.f = system_dynamics
self.horizon_lengths = horizon_lengths
self.N_max = int(max(horizon_lengths))
self.Q = Q
self.R = R
self.n_states = Q.shape[0]
self.n_inputs = R.shape[0]
def compute_control(self, x0: np.ndarray) -> Tuple[np.ndarray, dict]:
# Optimization logic here
u_optimal = np.zeros(self.n_inputs)
return u_optimal, {'cost': 0.0}
class InputToStateTriggering:
def __init__(self, threshold: float, mpc_controller: MultiHorizonMPC):
self.threshold = threshold
self.mpc = mpc_controller
self.last_transmitted_state = None
self.transmission_times = []
def should_transmit(self, current_state: np.ndarray,
predicted_state: np.ndarray) -> bool:
deviation = np.linalg.norm(current_state - predicted_state)
return deviation > self.threshold
def get_bandwidth_usage(self, total_time: float) -> dict:
if not self.transmission_times:
return {'transmissions': 0, 'rate': 0}
num_transmissions = len(self.transmission_times)
rate = num_transmissions / total_time
return {'transmissions': num_transmissions, 'rate': rate}