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import itertools
import numpy as np
def intervals_product(a, b):
Compute the product of two intervals
:param a: interval [a_min, a_max]
:param b: interval [b_min, b_max]
:return: the interval of their product ab
p = lambda x: np.maximum(x, 0)
n = lambda x: np.maximum(-x, 0)
return np.array(
[[0]), p(b[0])) -[1]), n(b[0])) -[0]), p(b[1])) +[1]), n(b[1])),[1]), p(b[1])) -[0]), n(b[1])) -[1]), p(b[0])) +[0]), n(b[0]))])
def intervals_diff(a, b):
Compute the difference of two intervals
:param a: interval [a_min, a_max]
:param b: interval [b_min, b_max]
:return: the interval of their difference a - b
return np.array([a[0] - b[1], a[1] - b[0]])
def interval_negative_part(a):
Compute the negative part of an interval
:param a: interval [a_min, a_max]
:return: the interval of its negative part min(a, 0)
return np.minimum(a, 0)
def integrator_interval(x, k):
Compute the interval of an integrator system: dx = -k*x
:param x: state interval
:param k: gain interval, must be positive
:return: interval for dx
if x[0] >= 0:
interval_gain = np.flip(-k, 0)
elif x[1] <= 0:
interval_gain = -k
interval_gain = -np.array([k[0], k[0]])
return interval_gain*x # Note: no flip of x, contrary to using intervals_product(k,interval_minus(x))
def vector_interval_section(v_i, direction):
corners = [[v_i[0, 0], v_i[0, 1]],
[v_i[0, 0], v_i[1, 1]],
[v_i[1, 0], v_i[0, 1]],
[v_i[1, 0], v_i[1, 1]]]
corners_dist = [, direction) for corner in corners]
return np.array([min(corners_dist), max(corners_dist)])
def polytope(parametrized_f, params_intervals):
:param parametrized_f: parametrized matrix function
:param params_intervals: axes: [min, max], params
:return: a0, d_a polytope that represents the matrix interval
params_means = params_intervals.mean(axis=0)
a0 = parametrized_f(params_means)
vertices_id = itertools.product([0, 1], repeat=params_intervals.shape[1])
d_a = []
for vertex_id in vertices_id:
params_vertex = params_intervals[vertex_id, np.arange(len(vertex_id))]
d_a.append(parametrized_f(params_vertex) - parametrized_f(params_means))
d_a = list({d_a_i.tostring(): d_a_i for d_a_i in d_a}.values())
return a0, d_a
def is_metzler(matrix):
return (matrix - np.diagonal(matrix) >= 0).all()
class LPV(object):
def __init__(self, x0, a0, da, d=None, center=None, x_i=None):
self.x0 = np.array(x0, dtype=float)
self.a0 = np.array(a0, dtype=float)
self.da = [np.array(da_i) for da_i in da]
self.d = np.array(d) if d is not None else np.zeros(self.x0.shape) = np.array(center) if center is not None else np.zeros(self.x0.shape)
self.coordinates = None
self.x_i = np.array(x_i) if x_i is not None else np.array([self.x0, self.x0])
self.x_i_t = None
def update_coordinates_frame(self, a0):
Ensure that the dynamics matrix A0 is Metzler.
If not, design a coordinate transformation and apply it to the model and state interval.
:param a0: the dynamics matrix A0
self.coordinates = None
# Rotation
if not is_metzler(a0):
eig_v, transformation = np.linalg.eig(a0)
if np.isreal(eig_v).all():
self.coordinates = (transformation, np.linalg.inv(transformation))
print("Non Metzler A0 with complex eigenvalues: ", eig_v)
self.coordinates = (np.eye(a0.shape[0]), np.eye(a0.shape[0]))
# Forward coordinates change of states and models
self.a0 = self.change_coordinates(self.a0, matrix=True)
self.da = self.change_coordinates(self.da, matrix=True)
self.d = self.change_coordinates(self.d, offset=False)
self.x_i_t = self.change_coordinates(self.x_i)
def change_coordinates(self, value, matrix=False, back=False, interval=False, offset=True):
Perform a change of coordinate: rotation and centering.
:param value: the object to transform
:param matrix: is it a matrix or a vector?
:param back: if True, transform back to the original coordinates
:param interval: when transforming an interval, lossy interval arithmetic must be used to preserve the inclusion
:param offset: should we apply the centering or not
:return: the transformed object
if self.coordinates is None:
return value
transformation, transformation_inv = self.coordinates
if interval:
value = intervals_product(
[self.coordinates[0], self.coordinates[0]],
value[:, :, np.newaxis]).squeeze() + offset * np.array([,])
return value
elif matrix: # Matrix
if back:
return transformation @ value @ transformation_inv
return transformation_inv @ value @ transformation
elif isinstance(value, list): # List
return [self.change_coordinates(v, back) for v in value]
elif len(value.shape) == 2:
for t in range(value.shape[0]): # Array of vectors
value[t, :] = self.change_coordinates(value[t, :], back=back)
return value
elif len(value.shape) == 1: # Vector
if back:
return transformation @ value + offset *
return transformation_inv @ (value - offset *
def step(self, dt):
self.x_i_t = self.step_interval_predictor(self.x_i_t, dt)
def step_interval_observer(self, x_i, dt):
a0, da, d = self.a0, self.da, self.d
a_i = a0 + sum(intervals_product([0, 1], [da_i, da_i]) for da_i in da)
dx_i = intervals_product(a_i, x_i) + d
return x_i + dx_i*dt
def step_interval_predictor(self, x_i, dt):
a0, da, d = self.a0, self.da, self.d
p = lambda x: np.maximum(x, 0)
n = lambda x: np.maximum(-x, 0)
da_p = sum(p(da_i) for da_i in da)
da_n = sum(n(da_i) for da_i in da)
x_m, x_M = x_i[0, :, np.newaxis], x_i[1, :, np.newaxis]
dx_m = a0 @ x_m - da_p @ n(x_m) - da_n @ p(x_M) + d[:, np.newaxis]
dx_M = a0 @ x_M + da_p @ p(x_M) + da_n @ n(x_m) + d[:, np.newaxis]
dx_i = np.array([dx_m.squeeze(axis=-1), dx_M.squeeze(axis=-1)])
return x_i + dx_i * dt
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