Create spatial_bicycle_models.py
Two different bicycle models (simple, extended)master
matssteinweg
parent
69aaae546e
commit
a773c1893a
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import numpy as np
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import math
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import time
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import cvxpy as cp
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from abc import ABC, abstractmethod
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#########################
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# Temporal State Vector #
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#########################
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class TemporalState:
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def __init__(self, x, y, psi, v_x=0, v_y=0):
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"""
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Temporal State Vector containing x, y coordinates and heading psi
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:param x: x position in global coordinate system | [m]
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:param y: y position in global coordinate system | [m]
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:param psi: yaw angle | [rad]
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:param v_x: velocity in x direction (car frame) | [m/s]
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:param v_y: velocity in y direction (car frame) | [m/s]
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"""
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self.x = x
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self.y = y
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self.psi = psi
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self.v_x = v_x
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self.v_y = v_y
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########################
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# Spatial State Vector #
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########################
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class SpatialState(ABC):
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@abstractmethod
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def __init__(self):
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pass
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def __getitem__(self, item):
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return list(vars(self).values())[item]
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def __len__(self):
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return len(vars(self))
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def list_states(self):
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return list(vars(self).keys())
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def __iadd__(self, other):
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for state_id, state in enumerate(vars(self).values()):
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vars(self)[list(vars(self).keys())[state_id]] += other[state_id]
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return self
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class SimpleSpatialState(SpatialState):
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def __init__(self, e_y, e_psi, v):
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"""
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Temporal State Vector containing x, y coordinates and heading psi
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:param e_y: orthogonal deviation from center-line | [m]
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:param e_psi: yaw angle relative to path | [rad]
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:param v: absolute velocity | [m/s]
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"""
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super(SimpleSpatialState, self).__init__()
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self.e_y = e_y
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self.e_psi = e_psi
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self.v = v
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class ExtendedSpatialState(SpatialState):
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def __init__(self, e_y, e_psi, v_x, v_y, omega, t):
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"""
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Temporal State Vector containing x, y coordinates and heading psi
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:param e_y: orthogonal deviation from center-line | [m]
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:param e_psi: yaw angle relative to path | [rad]
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:param v: absolute velocity | [m/s]
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"""
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super(ExtendedSpatialState, self).__init__()
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self.e_y = e_y
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self.e_psi = e_psi
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self.v_x = v_x
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self.v_y = v_y
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self.omega = omega
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self.t = t
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####################################
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# Spatial Bicycle Model Base Class #
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####################################
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class SpatialBicycleModel(ABC):
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def __init__(self, reference_path):
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"""
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Construct spatial bicycle model.
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:param reference_path: reference path model is supposed to follow
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"""
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# Precision
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self.eps = 1e-12
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# Reference Path
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self.reference_path = reference_path
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# set initial distance traveled
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self.s = 0.0
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# set initial waypoint ID
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self.wp_id = 0
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# set initial waypoint
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self.current_waypoint = self.reference_path.waypoints[self.wp_id]
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# initialize spatial state
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self.spatial_state = None
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def s2t(self, reference_waypoint=None, predicted_state=None):
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"""
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Convert spatial state to temporal state. Either convert self.spatial
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state with current waypoint as reference or provide reference waypoint
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and (e_y, e_psi).
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:return x, y, psi
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"""
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# Compute spatial state for current waypoint if no waypoint given
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if reference_waypoint is None and predicted_state is None:
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# compute temporal state variables
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x = self.current_waypoint.x - self.spatial_state.e_y * np.sin(
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self.current_waypoint.psi)
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y = self.current_waypoint.y + self.spatial_state.e_y * np.cos(
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self.current_waypoint.psi)
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psi = self.current_waypoint.psi + self.spatial_state.e_psi
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else:
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# compute temporal state variables
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x = reference_waypoint.x - predicted_state[0] * np.sin(
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reference_waypoint.psi)
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y = reference_waypoint.y + predicted_state[0] * np.cos(
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reference_waypoint.psi)
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psi = reference_waypoint.psi + predicted_state[1]
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return x, y, psi
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def drive(self, delta, D):
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"""
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Update states of spatial bicycle model.
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:param delta: angular velocity | [rad]
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:param D: acceleration command | [-1, 1]
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"""
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# get spatial derivatives
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spatial_derivatives = self.get_spatial_derivatives(delta, D)
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# get delta_s
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next_waypoint = self.reference_path.waypoints[self.wp_id+1]
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delta_s = next_waypoint - self.current_waypoint
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# update spatial state (euler method)
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self.spatial_state += spatial_derivatives * delta_s
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# assert that unique projections exists
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assert self.spatial_state.e_y < (1 / (self.current_waypoint.kappa +
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self.eps))
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# increase waypoint ID
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self.wp_id += 1
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# update current waypoint
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self.current_waypoint = self.reference_path.waypoints[self.wp_id]
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# update temporal_state to match spatial state
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self.temporal_state = self.s2t()
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# update s
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self.s += delta_s
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# linearize model around new operating point
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self.A, self.B = self.linearize()
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@abstractmethod
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def get_spatial_derivatives(self, delta, D):
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pass
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@abstractmethod
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def linearize(self):
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pass
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########################
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# Simple Bicycle Model #
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########################
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class SimpleBicycleModel(SpatialBicycleModel):
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def __init__(self, reference_path, e_y, e_psi, v):
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"""
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Construct spatial bicycle model.
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:param e_y: initial deviation from reference path | [m]
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:param e_psi: initial heading offset from reference path | [rad]
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:param v: initial velocity | [m/s]
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:param reference_path: reference path model is supposed to follow
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"""
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super(SimpleBicycleModel, self).__init__(reference_path)
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# Constants
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self.C1 = 0.5
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self.C2 = 17.06
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self.Cm1 = 12.0
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self.Cm2 = 2.17
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self.Cr2 = 0.1
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self.Cr0 = 0.6
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# Spatial state
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self.spatial_state = SimpleSpatialState(e_y, e_psi, v)
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# Temporal state
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self.temporal_state = self.s2t()
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# Linear System Matrices
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self.A, self.B = self.linearize()
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def s2t(self, reference_waypoint=None, predicted_state=None):
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"""
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Convert spatial state to temporal state
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:return temporal state equivalent to self.spatial_state
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"""
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# compute velocity information
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if predicted_state is None and reference_waypoint is None:
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# get information from base class
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x, y, psi = super(SimpleBicycleModel, self).s2t()
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v_x = self.spatial_state.v
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v_y = 0
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else:
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# get information from base class
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x, y, psi = super(SimpleBicycleModel, self).s2t(reference_waypoint,
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predicted_state)
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v_x = predicted_state[2]
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v_y = 0
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return TemporalState(x, y, psi, v_x, v_y)
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def get_velocities(self, delta):
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"""
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Compute relevant velocity components for current update.
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:param delta: steering command
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:return: velocities in x, y and waypoint direction
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"""
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# approximation for small delta
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v_x = self.spatial_state.v
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v_y = self.spatial_state.v * delta * self.C1
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# velocity along waypoint direction
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v_sigma = v_x * np.cos(self.spatial_state.e_psi) - v_y * np.sin(
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self.spatial_state.e_psi)
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return v_x, v_y, v_sigma
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def get_temporal_derivatives(self, v_sigma, delta, D):
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"""
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Compute temporal derivatives needed for state update.
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:param v_sigma: velocity along the path
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:param delta: steering command
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:param D: dutycycle of DC motor
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:return: temporal derivatives of distance, angle and velocity
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"""
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# velocity along path
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s_dot = 1 / (1 - (self.spatial_state.e_y * self.current_waypoint.kappa)) * v_sigma
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# angle rate of change
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psi_dot = self.spatial_state.v * delta * self.C2
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# acceleration
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v_dot = (self.Cm1 - self.Cm2 * self.spatial_state.v) * D - self.Cr2 * (
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self.spatial_state.v ** 2) - self.Cr0 - (
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self.spatial_state.v * delta) ** 2 * self.C2 * self.C1 ** 2
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return s_dot, psi_dot, v_dot
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def get_spatial_derivatives(self, delta, D):
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"""
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Compute spatial derivatives of all state variables for update.
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:param delta: steering angle
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:param D: duty-cycle
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:return: spatial derivatives for all state variables
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"""
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# Compute velocities
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v_x, v_y, v_sigma = self.get_velocities(delta)
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# Compute state derivatives
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s_dot, psi_dot, v_dot = self.get_temporal_derivatives(v_sigma, delta,
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D)
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d_e_y = (self.spatial_state.v * np.sin(self.spatial_state.e_psi)
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+ self.spatial_state.v * delta * self.C1 * np.cos(
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self.spatial_state.e_psi)) \
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/ (s_dot + self.eps)
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d_e_psi = (psi_dot / (s_dot + self.eps) - self.current_waypoint.kappa)
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d_v = v_dot / (s_dot + self.eps)
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d_t = 1 / (s_dot + self.eps)
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return np.array([d_e_y, d_e_psi, d_v])
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def linearize(self, delta=0, D=0):
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"""
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Linearize the system equations around the current state and waypoint.
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:param delta: reference steering angle
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:param D: reference duty-cycle
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"""
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# get current state
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e_y = self.spatial_state.e_y
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e_psi = self.spatial_state.e_psi
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v = self.spatial_state.v
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# get information about current waypoint
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kappa = self.reference_path.waypoints[self.wp_id].kappa
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# get delta_s
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next_waypoint = self.reference_path.waypoints[self.wp_id+1]
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delta_s = next_waypoint - self.current_waypoint
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# set helper variables
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v_x = v
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v_y = v * delta * self.C1
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##############################
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# Helper Partial Derivatives #
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##############################
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s_dot = 1 / (1 - e_y*kappa) * (v_x * np.cos(e_psi) - v_y * np.sin(e_psi))
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d_s_dot_d_e_y = kappa / (1-e_y*kappa)**2 * (v_x * np.cos(e_psi) - v_y * np.sin(e_psi))
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d_s_dot_d_e_psi = 1 / (1 - e_y*kappa) * (-v_x * np.sin(e_psi) - v_y * np.cos(e_psi))
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d_s_dot_d_v = 1 / (1 - e_y*kappa) * (np.cos(e_psi) - delta * self.C1 * np.sin(e_psi))
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d_s_dot_d_t = 0
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d_s_dot_d_delta = 1 / (1 - e_y*kappa) * (- v * self.C1 * np.sin(e_psi))
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d_s_dot_d_D = 0
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d_s_dot_d_kappa = e_y / (1-e_y*kappa)**2 * (v_x * np.cos(e_psi) - v_y * np.sin(e_psi))
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# Check
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c_1 = (v_x * np.sin(e_psi) + v_y * np.cos(e_psi))
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d_c_1_d_e_y = 0
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d_c_1_d_e_psi = v_x * np.cos(e_psi) - v_y * np.sin(e_psi)
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d_c_1_d_v = np.sin(e_psi) + self.C1 * delta * np.cos(e_psi)
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d_c_1_d_t = 0
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d_c_1_d_delta = self.C1 * v * np.cos(e_psi)
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d_c_1_d_D = 0
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# Check
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psi_dot = v * delta * self.C2
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d_psi_dot_d_e_y = 0
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d_psi_dot_d_e_psi = 0
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d_psi_dot_d_v = delta * self.C2
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d_psi_dot_d_t = 0
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d_psi_dot_d_delta = v * self.C2
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d_psi_dot_d_D = 0
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# Check
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v_dot = (self.Cm1 - self.Cm2 * v) * D - self.Cr2 * (v ** 2) - self.Cr0 - (
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v * delta) ** 2 * self.C2 * (self.C1 ** 2)
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d_v_dot_d_e_y = 0
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d_v_dot_d_e_psi = 0
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d_v_dot_d_v = -self.Cm2 * D - 2 * self.Cr2 * v - 2 * v * (delta ** 2) * self.C2 * (self.C1 ** 2)
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d_v_dot_d_t = 0
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d_v_dot_d_delta = -2 * (v ** 2) * delta * self.C2 * self.C1 ** 2
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d_v_dot_d_D = self.Cm1 - self.Cm2 * v
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# Check
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#######################
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# Partial Derivatives #
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#######################
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# derivatives for E_Y
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d_e_y_d_e_y = -c_1 * d_s_dot_d_e_y / (s_dot**2)
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d_e_y_d_e_psi = (d_c_1_d_e_psi * s_dot - d_s_dot_d_e_psi * c_1) / (s_dot**2)
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d_e_y_d_v = (d_c_1_d_v * s_dot - d_s_dot_d_v * c_1) / (s_dot**2)
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d_e_y_d_t = 0
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d_e_y_d_D = 0
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d_e_y_d_delta = (d_c_1_d_delta * s_dot - d_s_dot_d_delta * c_1) / (s_dot**2)
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d_e_y_d_kappa = -d_s_dot_d_kappa * c_1 / (s_dot**2)
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# derivatives for E_PSI
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d_e_psi_d_e_y = - psi_dot * d_s_dot_d_e_y / (s_dot**2)
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d_e_psi_d_e_psi = - psi_dot * d_s_dot_d_e_psi / (s_dot**2)
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d_e_psi_d_v = (d_psi_dot_d_v * s_dot - psi_dot * d_s_dot_d_v) / (s_dot**2)
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d_e_psi_d_t = 0
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d_e_psi_d_delta = (d_psi_dot_d_delta * s_dot - psi_dot * d_s_dot_d_delta) / (s_dot**2)
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d_e_psi_d_D = 0
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d_e_psi_d_kappa = -d_s_dot_d_kappa * psi_dot / (s_dot**2) - 1
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# derivatives for V
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d_v_d_e_y = - d_s_dot_d_e_y * v_dot / (s_dot**2)
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d_v_d_e_psi = - d_s_dot_d_e_psi * v_dot / (s_dot**2)
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d_v_d_v = (d_v_dot_d_v * s_dot - d_s_dot_d_v * v_dot) / (s_dot**2)
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d_v_d_t = 0
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d_v_d_delta = (d_v_dot_d_delta * s_dot - d_s_dot_d_delta * v_dot) / (s_dot**2)
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d_v_d_D = d_v_dot_d_D * s_dot / (s_dot**2)
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d_v_d_kappa = -d_s_dot_d_kappa * v_dot / (s_dot**2)
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# derivatives for T
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d_t_d_e_y = - d_s_dot_d_e_y / (s_dot**2)
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d_t_d_e_psi = - d_s_dot_d_e_psi / (s_dot ** 2)
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d_t_d_v = - d_s_dot_d_v / (s_dot ** 2)
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d_t_d_t = 0
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d_t_d_delta = - d_s_dot_d_delta / (s_dot ** 2)
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d_t_d_D = 0
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d_t_d_kappa = - d_s_dot_d_kappa / (s_dot ** 2)
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a_1 = np.array([d_e_y_d_e_y, d_e_y_d_e_psi, d_e_y_d_v, d_e_y_d_kappa]) * delta_s
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a_2 = np.array([d_e_psi_d_e_y, d_e_psi_d_e_psi, d_e_psi_d_v, d_e_psi_d_kappa]) * delta_s
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a_3 = np.array([d_v_d_e_y, d_v_d_e_psi, d_v_d_v, d_v_d_kappa]) * delta_s
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a_4 = np.array([0, 0, 0, 1])
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A = np.stack((a_1, a_2, a_3, a_4), axis=0)
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A[0, 0] += 1
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A[1, 1] += 1
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A[2, 2] += 1
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b_1 = np.array([d_e_y_d_D, d_e_y_d_delta]) * delta_s
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b_2 = np.array([d_e_psi_d_D, d_e_psi_d_delta]) * delta_s
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b_3 = np.array([d_v_d_D, d_v_d_delta]) * delta_s
|
||||
b_4 = np.array([0, 0])
|
||||
B = np.stack((b_1, b_2, b_3, b_4), axis=0)
|
||||
|
||||
# set system matrices
|
||||
return A, B
|
||||
|
||||
|
||||
##########################
|
||||
# Extended Bicycle Model #
|
||||
##########################
|
||||
|
||||
class ExtendedBicycleModel(SpatialBicycleModel):
|
||||
def __init__(self, reference_path, e_y, e_psi, v_x, v_y, omega, t):
|
||||
"""
|
||||
Construct spatial bicycle model.
|
||||
:param e_y: initial deviation from reference path | [m]
|
||||
:param e_psi: initial heading offset from reference path | [rad]
|
||||
:param v: initial velocity | [m/s]
|
||||
:param reference_path: reference path model is supposed to follow
|
||||
"""
|
||||
super(ExtendedBicycleModel, self).__init__(reference_path)
|
||||
|
||||
# Constants
|
||||
self.m = 0.041
|
||||
self.Iz = 27.8e-6
|
||||
self.lf = 0.029
|
||||
self.lr = 0.033
|
||||
|
||||
self.Cm1 = 0.287
|
||||
self.Cm2 = 0.0545
|
||||
self.Cr2 = 0.0518
|
||||
self.Cr0 = 0.00035
|
||||
|
||||
self.Br = 3.3852
|
||||
self.Cr = 1.2691
|
||||
self.Dr = 0.1737
|
||||
self.Bf = 2.579
|
||||
self.Cf = 1.2
|
||||
self.Df = 0.192
|
||||
|
||||
# Spatial state
|
||||
self.spatial_state = ExtendedSpatialState(e_y, e_psi, v_x, v_y, omega, t)
|
||||
|
||||
# Temporal state
|
||||
self.temporal_state = self.s2t()
|
||||
|
||||
# Linear System Matrices
|
||||
self.A, self.B = self.linearize()
|
||||
|
||||
def s2t(self, reference_waypoint=None, predicted_state=None):
|
||||
"""
|
||||
Convert spatial state to temporal state
|
||||
:return temporal state equivalent to self.spatial_state
|
||||
"""
|
||||
|
||||
# compute velocity information
|
||||
if predicted_state is None and reference_waypoint is None:
|
||||
# get information from base class
|
||||
x, y, psi = super(ExtendedBicycleModel, self).s2t()
|
||||
v_x = self.spatial_state.v_x
|
||||
v_y = self.spatial_state.v_y
|
||||
else:
|
||||
# get information from base class
|
||||
x, y, psi = super(ExtendedBicycleModel, self).s2t(reference_waypoint,
|
||||
predicted_state)
|
||||
v_x = predicted_state[2]
|
||||
v_y = predicted_state[3]
|
||||
|
||||
return TemporalState(x, y, psi, v_x, v_y)
|
||||
|
||||
def get_forces(self, delta, D):
|
||||
"""
|
||||
Compute forces required for temporal derivatives of v_x and v_y
|
||||
:param delta:
|
||||
:param D:
|
||||
:return:
|
||||
"""
|
||||
|
||||
F_rx = (self.Cm1 - self.Cm2 * self.spatial_state.v_x) * D - self.Cr0 - self.Cr2 * self.spatial_state.v_x ** 2
|
||||
|
||||
alpha_f = - np.arctan2(self.spatial_state.omega*self.lf + self.spatial_state.v_y, self.spatial_state.v_x) + delta
|
||||
F_fy = self.Df * np.sin(self.Cf*np.arctan(self.Bf*alpha_f))
|
||||
|
||||
alpha_r = np.arctan2(self.spatial_state.omega*self.lr - self.spatial_state.v_y, self.spatial_state.v_x)
|
||||
F_ry = self.Dr * np.sin(self.Cr * np.arctan(self.Br*alpha_r))
|
||||
|
||||
return F_rx, F_fy, F_ry, alpha_f, alpha_r
|
||||
|
||||
def get_temporal_derivatives(self, delta, F_rx, F_fy, F_ry):
|
||||
"""
|
||||
Compute temporal derivatives needed for state update.
|
||||
:param delta: steering command
|
||||
:param D: duty-cycle of DC motor
|
||||
:return: temporal derivatives of distance, angle and velocity
|
||||
"""
|
||||
|
||||
# velocity along path
|
||||
s_dot = 1 / (1 - (self.spatial_state.e_y * self.current_waypoint.kappa)) \
|
||||
* (self.spatial_state.v_x * np.cos(self.spatial_state.e_psi)
|
||||
+ self.spatial_state.v_y * np.sin(self.spatial_state.e_psi))
|
||||
|
||||
# velocity in x and y direction
|
||||
v_x_dot = (F_rx - F_fy * np.sin(delta) + self.m * self.spatial_state.
|
||||
v_y * self.spatial_state.omega) / self.m
|
||||
v_y_dot = (F_ry + F_fy * np.cos(delta) - self.m * self.spatial_state.
|
||||
v_x * self.spatial_state.omega) / self.m
|
||||
|
||||
# omega dot
|
||||
omega_dot = (F_fy * self.lf * np.cos(delta) - F_ry * self.lr) / self.Iz
|
||||
|
||||
return s_dot, v_x_dot, v_y_dot, omega_dot
|
||||
|
||||
def get_spatial_derivatives(self, delta, D):
|
||||
"""
|
||||
Compute spatial derivatives of all state variables for update.
|
||||
:param delta: steering angle
|
||||
:param psi_dot: heading rate of change
|
||||
:param s_dot: velocity along path
|
||||
:param v_dot: acceleration
|
||||
:return: spatial derivatives for all state variables
|
||||
"""
|
||||
|
||||
# get required forces
|
||||
F_rx, F_fy, F_ry, _, _ = self.get_forces(delta, D)
|
||||
|
||||
# Compute state derivatives
|
||||
s_dot, v_x_dot, v_y_dot, omega_dot = \
|
||||
self.get_temporal_derivatives(delta, F_rx, F_fy, F_ry)
|
||||
|
||||
|
||||
d_e_y = (self.spatial_state.v_x * np.sin(self.spatial_state.e_psi)
|
||||
+ self.spatial_state.v_y * np.cos(self.spatial_state.e_psi)) \
|
||||
/ (s_dot + self.eps)
|
||||
d_e_psi = (self.spatial_state.omega / (s_dot + self.eps) - self.current_waypoint.kappa)
|
||||
|
||||
d_v_x = v_x_dot / (s_dot + self.eps)
|
||||
d_v_y = v_y_dot / (s_dot + self.eps)
|
||||
d_omega = omega_dot / (s_dot + self.eps)
|
||||
d_t = 1 / (s_dot + self.eps)
|
||||
|
||||
return np.array([d_e_y, d_e_psi, d_v_x, d_v_y, d_omega, d_t])
|
||||
|
||||
def linearize(self, delta=0, D=0):
|
||||
"""
|
||||
Linearize the system equations around the current state and waypoint.
|
||||
:param delta: reference steering angle
|
||||
:param D: reference dutycycle
|
||||
"""
|
||||
|
||||
# get current state
|
||||
e_y = self.spatial_state.e_y
|
||||
e_psi = self.spatial_state.e_psi
|
||||
v_x = self.spatial_state.v_x
|
||||
v_y = self.spatial_state.v_y
|
||||
omega = self.spatial_state.omega
|
||||
t = self.spatial_state.t
|
||||
|
||||
# get information about current waypoint
|
||||
kappa = self.reference_path.waypoints[self.wp_id].kappa
|
||||
|
||||
# get delta_s
|
||||
next_waypoint = self.reference_path.waypoints[self.wp_id + 1]
|
||||
delta_s = next_waypoint - self.current_waypoint
|
||||
|
||||
# get temporal derivatives
|
||||
F_rx, F_fy, F_ry, alpha_f, alpha_r = self.get_forces(delta, D)
|
||||
s_dot, v_x_dot, v_y_dot, omega_dot = self.\
|
||||
get_temporal_derivatives(delta, F_rx, F_fy, F_ry)
|
||||
|
||||
##############################
|
||||
# Forces Partial Derivatives #
|
||||
##############################
|
||||
|
||||
d_alpha_f_d_v_x = 1 / (1 + ((omega * self.lf + v_y) / v_x)**2) * (omega * self.lf + v_y) / (v_x**2)
|
||||
d_alpha_f_d_v_y = - 1 / (1 + ((omega * self.lf + v_y) / v_x)**2) / v_x
|
||||
d_alpha_f_d_omega = - 1 / (1 + ((omega * self.lf + v_y) / v_x)**2) * (self.lf / v_x)
|
||||
d_alpha_f_d_delta = 1
|
||||
|
||||
d_alpha_r_d_v_x = - 1 / (1 + ((omega * self.lr - v_y) / v_x)**2) * (omega * self.lr - v_y) / (v_x**2)
|
||||
d_alpha_r_d_v_y = - 1 / (1 + ((omega * self.lr - v_y) / v_x)**2) / v_x
|
||||
d_alpha_r_d_omega = 1 / (1 + ((omega * self.lr - v_y) / v_x)**2) * (self.lr * v_x)
|
||||
|
||||
d_F_fy_d_v_x = self.Df * np.cos(self.Cf * np.arctan(self.Bf * alpha_f)) * self.Cf / (1 + (self.Bf * alpha_f)**2) * self.Bf * d_alpha_f_d_v_x
|
||||
d_F_fy_d_v_y = self.Df * np.cos(self.Cf * np.arctan(self.Bf * alpha_f)) * self.Cf / (1 + (self.Bf * alpha_f)**2) * self.Bf * d_alpha_f_d_v_y
|
||||
d_F_fy_d_omega = self.Df * np.cos(self.Cf * np.arctan(self.Bf * alpha_f)) * self.Cf / (1 + (self.Bf * alpha_f)**2) * self.Bf * d_alpha_f_d_omega
|
||||
d_F_fy_d_delta = self.Df * np.cos(self.Cf * np.arctan(self.Bf * alpha_f)) * self.Cf / (1 + (self.Bf * alpha_f)**2) * self.Bf * d_alpha_f_d_delta
|
||||
|
||||
d_F_ry_d_v_x = self.Dr * np.cos(self.Cr * np.arctan(self.Br * alpha_r)) * self.Cr / (1 + (self.Br * alpha_r)**2) * self.Br * d_alpha_r_d_v_x
|
||||
d_F_ry_d_v_y = self.Dr * np.cos(self.Cr * np.arctan(self.Br * alpha_r)) * self.Cr / (1 + (self.Br * alpha_r)**2) * self.Br * d_alpha_r_d_v_y
|
||||
d_F_ry_d_omega = self.Dr * np.cos(self.Cr * np.arctan(self.Br * alpha_r)) * self.Cr / (1 + (self.Br * alpha_r)**2) * self.Br * d_alpha_r_d_omega
|
||||
|
||||
d_F_rx_d_v_x = - self.Cm2 * D - 2 * self.Cr2 * v_x
|
||||
d_F_rx_d_D = self.Cm1 - self.Cm2 * v_x
|
||||
|
||||
##############################
|
||||
# Helper Partial Derivatives #
|
||||
##############################
|
||||
|
||||
d_s_dot_d_e_y = kappa / (1-e_y*kappa)**2 * (v_x * np.cos(e_psi) - v_y * np.sin(e_psi))
|
||||
d_s_dot_d_e_psi = 1 / (1 - e_y*kappa) * (-v_x * np.sin(e_psi) - v_y * np.cos(e_psi))
|
||||
d_s_dot_d_v_x = 1 / (1 - e_y*kappa) * np.cos(e_psi)
|
||||
d_s_dot_d_v_y = -1 / (1 - e_y*kappa) * np.sin(e_psi)
|
||||
d_s_dot_d_omega = 0
|
||||
d_s_dot_d_t = 0
|
||||
d_s_dot_d_delta = 0
|
||||
d_s_dot_d_D = 0
|
||||
d_s_dot_d_kappa = e_y / (1-e_y*kappa)**2 * (v_x * np.cos(e_psi) - v_y * np.sin(e_psi))
|
||||
# Check
|
||||
|
||||
c_1 = (v_x * np.sin(e_psi) + v_y * np.cos(e_psi))
|
||||
d_c_1_d_e_y = 0
|
||||
d_c_1_d_e_psi = v_x * np.cos(e_psi) - v_y * np.sin(e_psi)
|
||||
d_c_1_d_v_x = np.sin(e_psi)
|
||||
d_c_1_d_v_y = np.cos(e_psi)
|
||||
d_c_1_d_omega = 0
|
||||
d_c_1_d_t = 0
|
||||
d_c_1_d_delta = 0
|
||||
d_c_1_d_D = 0
|
||||
d_c_1_d_kappa = 0
|
||||
# Check
|
||||
|
||||
d_v_x_dot_d_e_y = 0
|
||||
d_v_x_dot_d_e_psi = 0
|
||||
d_v_x_dot_d_v_x = (d_F_rx_d_v_x - d_F_fy_d_v_x * np.sin(delta)) / self.m
|
||||
d_v_x_dot_d_v_y = - (d_F_fy_d_v_y * np.sin(delta) + self.m * omega) / self.m
|
||||
d_v_x_dot_d_omega = - (d_F_fy_d_omega * np.sin(delta) + self.m * v_y) / self.m
|
||||
d_v_x_dot_d_t = 0
|
||||
d_v_x_dot_d_delta = - (F_fy * np.cos(delta) + d_F_fy_d_delta * np.sin(delta)) / self.m
|
||||
d_v_x_dot_d_D = d_F_rx_d_D / self.m
|
||||
d_v_x_dot_d_kappa = 0
|
||||
|
||||
d_v_y_dot_d_e_y = 0
|
||||
d_v_y_dot_d_e_psi = 0
|
||||
d_v_y_dot_d_v_x = (d_F_ry_d_v_x + d_F_fy_d_v_x * np.cos(delta) - self.m * omega) / self.m
|
||||
d_v_y_dot_d_v_y = (d_F_ry_d_v_y + d_F_fy_d_v_y * np.cos(delta)) / self.m
|
||||
d_v_y_dot_d_omega = (d_F_ry_d_omega + d_F_fy_d_omega * np.cos(delta) - self.m * v_x) / self.m
|
||||
d_v_y_dot_d_t = 0
|
||||
d_v_y_dot_d_delta = d_F_fy_d_delta * np.cos(delta) / self.m
|
||||
d_v_y_dot_d_D = 0
|
||||
d_v_y_dot_d_kappa = 0
|
||||
|
||||
d_omega_dot_d_e_y = 0
|
||||
d_omega_dot_d_e_psi = 0
|
||||
d_omega_dot_d_v_x = (d_F_fy_d_v_x * self.lf * np.cos(delta) - d_F_ry_d_v_x * self.lr) / self.Iz
|
||||
d_omega_dot_d_v_y = (d_F_fy_d_v_y * self.lf * np.cos(delta) - d_F_fy_d_v_y * self.lr) / self.Iz
|
||||
d_omega_dot_d_omega = (d_F_fy_d_omega * self.lf * np.cos(delta) - d_F_fy_d_omega * self.lr) / self.Iz
|
||||
d_omega_dot_d_t = 0
|
||||
d_omega_dot_d_delta = (- F_fy * np.sin(delta) + d_F_fy_d_delta * np.cos(delta)) / self.Iz
|
||||
d_omega_dot_d_D = 0
|
||||
d_omega_dot_d_kappa = 0
|
||||
|
||||
#######################
|
||||
# Partial Derivatives #
|
||||
#######################
|
||||
|
||||
# derivatives for E_Y
|
||||
d_e_y_d_e_y = -c_1 * d_s_dot_d_e_y / (s_dot**2)
|
||||
d_e_y_d_e_psi = (d_c_1_d_e_psi * s_dot - d_s_dot_d_e_psi * c_1) / (s_dot**2)
|
||||
d_e_y_d_v_x = (d_c_1_d_v_x * s_dot - d_s_dot_d_v_x * c_1) / (s_dot**2)
|
||||
d_e_y_d_v_y = (d_c_1_d_v_y * s_dot - d_s_dot_d_v_y * c_1) / (s_dot**2)
|
||||
d_e_y_d_omega = (d_c_1_d_omega * s_dot - d_s_dot_d_omega * c_1) / (s_dot**2)
|
||||
d_e_y_d_t = 0
|
||||
d_e_y_d_D = 0
|
||||
d_e_y_d_delta = (d_c_1_d_delta * s_dot - d_s_dot_d_delta * c_1) / (s_dot**2)
|
||||
d_e_y_d_kappa = -d_s_dot_d_kappa * c_1 / (s_dot**2)
|
||||
|
||||
# derivatives for E_PSI
|
||||
d_e_psi_d_e_y = - omega * d_s_dot_d_e_y / (s_dot**2)
|
||||
d_e_psi_d_e_psi = - omega * d_s_dot_d_e_psi / (s_dot**2)
|
||||
d_e_psi_d_v_x = (- omega * d_s_dot_d_v_x) / (s_dot**2)
|
||||
d_e_psi_d_v_y = (- omega * d_s_dot_d_v_y) / (s_dot**2)
|
||||
d_e_psi_d_omega = (s_dot - omega * d_s_dot_d_omega) / (s_dot**2)
|
||||
d_e_psi_d_t = 0
|
||||
d_e_psi_d_delta = (- omega * d_s_dot_d_delta) / (s_dot**2)
|
||||
d_e_psi_d_D = (- omega * d_s_dot_d_D) / (s_dot**2)
|
||||
d_e_psi_d_kappa = -d_s_dot_d_kappa * omega / (s_dot**2) - 1
|
||||
|
||||
# derivatives for V_X
|
||||
d_v_x_d_e_y = - d_s_dot_d_e_y * v_x_dot / (s_dot**2)
|
||||
d_v_x_d_e_psi = - d_s_dot_d_e_psi * v_x_dot / (s_dot**2)
|
||||
d_v_x_d_v_x = (d_v_x_dot_d_v_x * s_dot - d_s_dot_d_v_x * v_x_dot) / (s_dot**2)
|
||||
d_v_x_d_v_y = (d_v_x_dot_d_v_y * s_dot - d_s_dot_d_v_y * v_x_dot) / (s_dot**2)
|
||||
d_v_x_d_omega = (d_v_x_dot_d_omega * s_dot - d_s_dot_d_omega * v_x_dot) / (s_dot**2)
|
||||
d_v_x_d_t = 0
|
||||
d_v_x_d_delta = (d_v_x_dot_d_delta * s_dot - d_s_dot_d_delta * v_x_dot) / (s_dot**2)
|
||||
d_v_x_d_D = d_v_x_dot_d_D * s_dot / (s_dot**2)
|
||||
d_v_x_d_kappa = -d_s_dot_d_kappa * v_x_dot / (s_dot**2)
|
||||
|
||||
# derivatives for V_Y
|
||||
d_v_y_d_e_y = - d_s_dot_d_e_y * v_y_dot / (s_dot ** 2)
|
||||
d_v_y_d_e_psi = - d_s_dot_d_e_psi * v_y_dot / (s_dot ** 2)
|
||||
d_v_y_d_v_x = (d_v_y_dot_d_v_x * s_dot - d_s_dot_d_v_x * v_y_dot) / (
|
||||
s_dot ** 2)
|
||||
d_v_y_d_v_y = (d_v_y_dot_d_v_y * s_dot - d_s_dot_d_v_y * v_y_dot) / (
|
||||
s_dot ** 2)
|
||||
d_v_y_d_omega = (d_v_y_dot_d_omega * s_dot - d_s_dot_d_omega * v_y_dot) / (
|
||||
s_dot ** 2)
|
||||
d_v_y_d_t = 0
|
||||
d_v_y_d_delta = (d_v_y_dot_d_delta * s_dot - d_s_dot_d_delta * v_y_dot) / (
|
||||
s_dot ** 2)
|
||||
d_v_y_d_D = d_v_y_dot_d_D * s_dot / (s_dot ** 2)
|
||||
d_v_y_d_kappa = -d_s_dot_d_kappa * v_y_dot / (s_dot ** 2)
|
||||
|
||||
# derivatives for Omega
|
||||
d_omega_d_e_y = (d_omega_dot_d_e_y * s_dot - omega_dot * d_s_dot_d_e_y) / (s_dot**2)
|
||||
d_omega_d_e_psi = (d_omega_dot_d_e_psi * s_dot - omega_dot * d_s_dot_d_e_psi) / (s_dot**2)
|
||||
d_omega_d_v_x = (d_omega_dot_d_v_x * s_dot - omega_dot * d_s_dot_d_v_x) / (s_dot**2)
|
||||
d_omega_d_v_y = (d_omega_dot_d_v_y * s_dot - omega_dot * d_s_dot_d_v_y) / (s_dot**2)
|
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d_omega_d_omega = (d_omega_dot_d_omega * s_dot - omega_dot * d_s_dot_d_omega) / (s_dot**2)
|
||||
d_omega_d_t = (d_omega_dot_d_t * s_dot - omega_dot * d_s_dot_d_t) / (s_dot**2)
|
||||
d_omega_d_delta = (d_omega_dot_d_delta * s_dot - omega_dot * d_s_dot_d_delta) / (s_dot**2)
|
||||
d_omega_d_D = (d_omega_dot_d_D * s_dot - omega_dot * d_s_dot_d_D) / (s_dot**2)
|
||||
d_omega_d_kappa = (d_omega_dot_d_kappa * s_dot - omega_dot * d_s_dot_d_kappa) / (s_dot**2)
|
||||
|
||||
# derivatives for T
|
||||
d_t_d_e_y = - d_s_dot_d_e_y / (s_dot**2)
|
||||
d_t_d_e_psi = - d_s_dot_d_e_psi / (s_dot ** 2)
|
||||
d_t_d_v_x = - d_s_dot_d_v_x / (s_dot ** 2)
|
||||
d_t_d_v_y = - d_s_dot_d_v_y / (s_dot ** 2)
|
||||
d_t_d_omega = - d_s_dot_d_omega / (s_dot ** 2)
|
||||
d_t_d_t = 0
|
||||
d_t_d_delta = - d_s_dot_d_delta / (s_dot ** 2)
|
||||
d_t_d_D = 0
|
||||
d_t_d_kappa = - d_s_dot_d_kappa / (s_dot ** 2)
|
||||
|
||||
a_1 = np.array([d_e_y_d_e_y, d_e_y_d_e_psi, d_e_y_d_v_x, d_e_y_d_v_y, d_e_y_d_omega, d_e_y_d_t, d_e_y_d_kappa])
|
||||
a_2 = np.array([d_e_psi_d_e_y, d_e_psi_d_e_psi, d_e_psi_d_v_x, d_e_psi_d_v_y, d_e_psi_d_omega, d_e_psi_d_t, d_e_psi_d_kappa])
|
||||
a_3 = np.array([d_v_x_d_e_y, d_v_x_d_e_psi, d_v_x_d_v_x, d_v_x_d_v_y, d_v_x_d_omega, d_v_x_d_t, d_v_x_d_kappa])
|
||||
a_4 = np.array([d_v_y_d_e_y, d_v_y_d_e_psi, d_v_y_d_v_x, d_v_y_d_v_y, d_v_y_d_omega, d_v_y_d_t, d_v_y_d_kappa])
|
||||
a_5 = np.array([d_omega_d_e_y, d_omega_d_e_psi, d_omega_d_v_x, d_omega_d_v_y, d_omega_d_omega, d_omega_d_t, d_omega_d_kappa])
|
||||
a_6 = np.array([d_t_d_e_y, d_t_d_e_psi, d_t_d_v_x, d_t_d_v_y, d_t_d_omega, d_t_d_t, d_t_d_kappa])
|
||||
a_7 = np.array([0, 0, 0, 0, 0, 0, 1])
|
||||
A = np.stack((a_1, a_2, a_3, a_4, a_5, a_6, a_7), axis=0) * delta_s
|
||||
A[0, 0] += 1
|
||||
A[1, 1] += 1
|
||||
A[2, 2] += 1
|
||||
A[3, 3] += 1
|
||||
A[4, 4] += 1
|
||||
A[5, 5] += 1
|
||||
b_1 = np.array([d_e_y_d_D, d_e_y_d_delta])
|
||||
b_2 = np.array([d_e_psi_d_D, d_e_psi_d_delta])
|
||||
b_3 = np.array([d_v_x_d_D, d_v_x_d_delta])
|
||||
b_4 = np.array([d_v_y_d_D, d_v_y_d_delta])
|
||||
b_5 = np.array([d_omega_d_D, d_omega_d_delta])
|
||||
b_6 = np.array([d_t_d_D, d_t_d_delta])
|
||||
b_7 = np.array([0, 0])
|
||||
B = np.stack((b_1, b_2, b_3, b_4, b_5, b_6, b_7), axis=0) * delta_s
|
||||
|
||||
# set system matrices
|
||||
return A, B
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
state = ExtendedSpatialState(0, 1, 2, 3, 4, 5)
|
||||
print(state[0:2])
|
||||
print(len(state))
|
||||
print(state.list_states())
|
||||
state += np.array([1, 1, 1, 2, 1, 1])
|
||||
print(vars(state))
|
||||
Loading…
Reference in New Issue