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pg-ella

Python implementation of PG-ELLA

port_apollo

psopt

Automatically exported from code.google.com/p/psopt

pylive

Python Plotter for Real-Time Data Visualization

pyplane

Phase plane analysis of nonlinear systems

python-control

The Python Control Systems Library is a Python module that implements basic operations for analysis and design of feedback control systems.

pyvision

pyVision machine learning library

qgis

QGIS is a free, open source, cross platform (lin/win/mac) geographical information system (GIS)

qpoases

Fork of the qpOASES to experiment with some improvements on the build system.

reinforcement-learning-for-autonomous-driving

ros_viz_tools

Visualization tools for easier rviz plotting.

sphinx

Main repository for the Sphinx documentation builder

stochastic-recursive-modified-extended-least-squreas-with-exponential-forgetting-factor-rmelswef

% This function is made by Ahmed ElTahan %{ This function is intended to estimate the parameters of a dynamic system of unknown parameters using the Recursive Modified Extended Least Squares With Exponential Forgetting Factor Method (RMELSWEF) for time varying parameter system which has an noise addition. After an experiment, we get the inputs, the outputs of the system. The experiment is operated with sample time Ts seconds. The model is given by A(z) y(t) = B(z)sys u(t) + C(z) eps(t) which can be written in z^(-d) B(z) C(z) y(t) = ------------------- u + ------------ e = L*u + M*e A(z) A(z) where: -- y : output of the system. -- u : control action (input to the system). -- e : color guassian noise (noise with non zero mean). -- Asys = 1 + a_1 z^-1 + a_2 z^-2 + ... + a_na z^(-na). [denominator polynomail] -- Bsys = b_0 + b_1 z^-1 + b_2 z^-2 + ... + b_nb z^(-nb). [numerator polynomail] -- C = 1 + c_1 z^-1 + c_2 z^-2 + ... + c_nc z^(-nc). [noise characteristics] -- d : delay in the system. A and C are monic polynomials. (in output estimation of the stochastic system as C is monic, we add e(t) to the estimation i.e. not starting from c1*e(t-1)) Function inputs u : input to the system in column vector form y : input of the system in column vector form na : order of the denominator polynomail nb : order of the numerator polynomail nc : order of the characteristics of the noise (usually <=2 for max) d : number represents the delay between the input and the output lambda : forgetting factor -->>> 1>lambda>0 Function Output Theta_final : final estimated parameters. Gz_estm : pulse (discrete) transfer function of the estimated parameters 1 figure for the history of the parameters that are being estimated 2 figure to validate the estimated parameters on the given output using the instantaneous estimated parameters. 3 figure to plot the input versus time. Note: the noise added shall not to be with a magnitude close to the system output, it should be smaller, this is in simulation such as here or the algorithm will go crazy that can't distinguish between the main and the noisy signal (This can be measured in practical case finding noise to signal ratio). An example is added to illustrate how to use the funcrtion %}

timedelayestimation

时间延迟估计(TDOA)相关算法

ukf-snmpc

This repository contains the source code for “Unscented Kalman filter stochastic nonlinear model predictive control” (UKF-SNMPC).

ukflib

An Unscented Kalman Filter library for Python that allows nonadditive process and measurement noise

wiki_barebone

A simple gollum wiki template which is running as a service.

work_with_grace

Find the ways to enjoy working!