#!/usr/bin/env python
# Copyright (c) 2016
The UUV Simulator Authors.
# All rights reserved.
#
#
Licensed under
the Apache
License, Version 2.0 (
the "
License");
# you may not use this file except in compliance with
the License.
# You may obtain a copy of
the License at
#
# http://www.apache.org/
licenses/
LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under
the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, ei
ther express or implied.
# See
the License for
the specific language governing permissions and
# limitations under
the License.
# Import
the ROS Python library
from __future__ import print_function
import rospy
# Import
the NumPy package for numerical computations
import numpy as np
# Import
the dynamic positioning controller base
class to inherit methods such as error computation update,
# publishing from some important ROS topics (e.g. trajectory, pose and velocity reference) and access to
#
the vehicle model
class, that is necessary to explicitly use
the vehicle model
from uuv_control_interfaces import DPControllerBase
class TutorialDPController(DPControllerBase):
# A new controller that is based on
the DPControllerBase must at least provide
the implementation of
#
the method update_controller.
#
The _reset_controller method can also be overridden and it will be called every time
there is a service call
# <vehicle namespace>/reset_controller.
The default implementation sets
the reference and error vectors to
# zero.
#
The update_controller method must contain
the implementation of
the control algorithm and will be called
# by every update of
the vehicle's odometry message. It is
therefore not necessary to explicitly call this update
# function in this controller implementation.
# For
the controller to send
the control torques to
the vehicle's thruster manager, at
the end of
the
# update_controller function
the 6 x 1 control vector (type numpy.ndarray) sent
using the function
# publish_control_wrench from
the super
class, which will generate a Wrench ROS message and publish it to
the
# correspondent thruster manager node.
# For this tutorial, a simple PID controller will be implemented.
The controller's control torque output is
# "tau"
therefore computed as:
#
# tau = Kp * e + Kd * de/dt + Ki int_e
#
# where e is
the pose error vector, in this case defined as e = (x, y, z, roll, pitch, yaw)^T
def __init__(self):
# Calling
the constructor of
the super-
class DPControllerBase, which has
the implementation of
the error
# computation update and to publish
the resulting torque control vector.
super(TutorialDPController, self).__init__(self)
#
The controller should read its parameters from
the ROS parameter server for initial setup
# One way to do this is to read
the parameters from
the node's private parameter namespace, which is done by
# reading
the parameter tag with an "~" at
the beginning. If this method is used,
the parameters should be
# initialized accordingly in
the controller startup launch file, such as
#
# <launch>
# <node pkg="example_package" type="example_node.py" name="example_node" output="screen">
# <rosparam>
# param_1: 0.0
# param_2: 0.0
# </rosparam>
# </node>
# </launch>
#
# For more information, see http://wiki.ros.org/roscpp_tutorials/Tutorials/AccessingPrivateNamesWithNodeHandle
# Let's initialize
the controller gain matrices Kp, Kd and Ki
self._Kp = np.zeros(shape=(6, 6))
self._Kd = np.zeros(shape=(6, 6))
self._Ki = np.zeros(shape=(6, 6))
# Initialize
the integrator component
self._int = np.zeros(shape=(6,))
# Initialize variable that will store
the vehicle pose error
self._error_pose = np.zeros(shape=(6,))
# Now
the gain matrices need to be set according to
the variables stored in
the parameter server
# For simplicity,
the gain matrices are defined as diagonal matrices, so only 6 coefficients are
# needed
if rospy.get_param('~Kp'):
Kp_diag = rospy.get_param('~Kp')
if len(Kp_diag) == 6:
self._Kp = np.diag(Kp_diag)
else:
# If
the vector provided has
the wrong dimension, raise an exception
raise rospy.ROSException('For
the Kp diagonal matrix, 6 coefficients are needed')
# Do
the same for
the o
ther two matrices
if rospy.get_param('~Kd'):
diag = rospy.get_param('~Kd')
if len(diag) == 6:
self._Kd = np.diag(diag)
print('Kd=\n', self._Kd)
else:
# If
the vector provided has
the wrong dimension, raise an exception
raise rospy.ROSException('For
the Kd diagonal matrix, 6 coefficients are needed')
if rospy.get_param('~Ki'):
diag = rospy.get_param('~Ki')
if len(diag) == 6:
self._Ki = np.diag(diag)
print('Ki=\n', self._Ki)
else:
# If
the vector provided has
the wrong dimension, raise an exception
raise rospy.ROSException('For
the Ki diagonal matrix, 6 coefficients are needed')
self._is_init =
True
def _reset_controller(self):
#
The _reset_controller method from
the super
class DPControllerBase already sets
the error
# and reference vectors to zero, but this
class has additional attributes that should also
# be taken care of.
# This implementation will,
therefore, first call
the super
class reset method
super(TutorialDPController, self)._reset_controller()
# And
then proceed to set
the internal variables back to zero
self._error_pose = np.zeros(shape=(6,))
self._int = np.zeros(shape=(6,))
def update_controller(self):
if not self._is_init:
return False
#
The controller algorithm must be implemented here,
the super
class will connect this method
# to
the odometry update as a callback function
# First test whe
ther or not
the odometry topic subscriber has already been initialized
if not self.odom_is_init:
return
# Update
the integrator, read
the super
class vector for
the pose error (orientation is represented
# with Euler angles in RPY convention) and integrate to
the stored pose error from
the last
# iteration
self._int = self._int + 0.5 * (self.error_pose_euler + self._error_pose) * self._dt
# Store
the current pose error for
the next iteration
self._error_pose = self.error_pose_euler
# Compute
the control forces and torques
using the current error vectors available
tau = np.dot(self._Kp, self.error_pose_euler) + np.dot(self._Kd, self._errors['vel']) + \
np.dot(self._Ki, self._int)
# Use
the super
class method to convert
the control force vector into a ROS message
# and publish it as an input to
the vehicle's thruster manager.
The thruster manager module
# will
then distribute
the efforts amongst
the thrusters
using the thruster allocation matrix
self.publish_control_wrench(tau)
if __name__ == '__main__':
# Since this is an ROS node, this Python script has to be treated as an executable
# Remember to convert this Python file into an executable. This can be done with
#
# cd <path_to_ros_package>/scripts
# chmod 777 tutorial_dp_controller.py
#
# This file has also to be included in this package's CMakeLists.txt
# After
the line catkin_package() in
the CMakeLists.txt, include
the following
#
# catkin_install_python(PROGRAMS scripts/tutorial_dp_controller.py DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION})
print('Tutorial - DP Controller')
rospy.init_node('tutorial_dp_controller')
try:
node = TutorialDPController()
rospy.spin()
except rospy.ROSInterruptException:
print('caught exception')
print('exiting')学习这个代码，编写一个lqr控制器
本文介绍了一种用于Java许可文件的字符串混淆方法，通过该方法可以提高软件许可证的安全性，防止未授权使用。具体实现细节可参考提供的外部链接。
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