edxmorgan

# Define here the models for your spider middleware
#
# See documentation in:
# https://docs.scrapy.org/en/latest/topics/spider-middleware.html
import sys
import time
import logging
from scrapy import signals
from scrapy.mail import MailSender
from scrapy.utils.project import get_project_settings

edxmorgan

I believe the article was originally written by fede.tft.

It appears they have copied source code to github and updated it for C++11: https://github.com/fedetft/serial-port

Introduction

The serial port protocol is one of the most long lived protocols currently in use. According to wikipedia, it has been standadized in 1969. First, a note: here we're talking about the RS232 serial protocol. This note is necessary because there are many other serial protocols, like SPI, I2C, CAN, and even USB and SATA.

Some time ago, when the Internet connections were done using a 56k modem, the serial port was the most common way of connecting a modem to a computer. Now that we have ADSL modems, the serial ports have disappeared from newer computers, but the protocol is still widely used.

In fact, most microcontrollers, even the newer ones have one or more peripherals capable of communicating using this protocol, and from the PC side, all operating system

edxmorgan

#include <iostream>
#include <chrono>
#include "casadi/casadi.hpp"


using namespace std;
using namespace chrono;
using namespace casadi;

int main()

edxmorgan

#include <iostream>
#include <chrono>
#include "casadi/casadi.hpp"

using namespace std;
using namespace chrono;
using namespace casadi;

int main()
{

edxmorgan

def qr_eigen(A, iterations=100):
    pQ = SX.eye(A.size1())
    X = copy.deepcopy(A)
    for _ in range(iterations):
        Q, R = qr(X)  # QR decomposition in CasADi
        pQ = pQ @ Q # Update eigenvector matrix for next iteration
        X = R @ Q  # Update eigenvalue matrix for next iteration
    return diag(X), pQ    
    
# example usage for a symbolic SX square matrix in casadi    

edxmorgan

import numpy as np
from numpy.polynomial.polynomial import Polynomial
import copy

# Define the coefficients of the polynomials A(z) and B(z)

# Introduction to Stochastic Control theory. Karl J. Astrom example
# A_coeffs = [1, 0.7, 0.5, -0.3]
# B_coeffs = [1, 0.3, 0.2, 0.1]

edxmorgan

#method 0
def linearize_new(f, x, x0):
  x_lim = SX.sym('x_lim', x.sparsity())
  return substitute(f + jtimes(f, x, x_lim - x0), vertcat(x_lim, x), vertcat(x, x0))

f_linear_new = Function('f_linear_new', [x, x0, u, mu, I_sym], [linearize_new(x_next, x, x0)])


#method 1
F_next_linear = Function('Xnext_linear', [x, x0, u, mu, I_sym], [linearize(x_next, x, x0)])

edxmorgan

from casadi import SX, inv, Function, sqrt, jacobian, vertcat
import numpy as np
import matplotlib.pyplot as plt

#true value of the parameters that will be estimated
initialPosition = 10
acceleration = 5
initialVelocity= -2

#noise standard deviation

edxmorgan

"""
Inverted Pendulum LQG control
author: Edward Morgan
"""

import casadi as cs
import math
import time
from numpy.linalg import inv, eig
import matplotlib.pyplot as plt

edxmorgan

import copy
from casadi import *
import numpy as np

def qr_eigen(A, iterations=100):
    pQ = SX.eye(A.size1())
    X = copy.deepcopy(A)
    for _ in range(iterations):
        Q, R = qr(X)  # QR decomposition in CasADi
        pQ = pQ @ Q # Update eigenvector matrix for next iteration