Displaying 1 to 20 from 22 results

mpc - A Parser Combinator library for C

  •    C

mpc is a lightweight and powerful Parser Combinator library for C. Using mpc might be of interest to you if you are...

mpc - MSFvenom Payload Creator (MSFPC)

  •    Shell

A quick way to generate various "basic" Meterpreter payloads via msfvenom (part of the Metasploit framework). MSFvenom Payload Creator (MSFPC) is a wrapper to generate multiple types of payloads, based on users choice. The idea is to be as simple as possible (only requiring one input) to produce their payload.

MPC Pattern for Microsoft Silverlight 4.0


If you have struggled with MVVM in Silverlight line of business applications and you want a good framework for building an application, MPC is for you. MPC is a Model, ViewModel, Presenter and Controller pattern enhanced with XAML defined States, Actions, and Async WCF.

powersoftau - An independent implementation of the Powers of Tau MPC ceremony.

  •    Go

powersoftau is an independent implementation of the Powers of Tau MPC ceremony. It is written in Go, shares no code with the main Rust implementation, and uses the RELIC toolkit for BLS12-381.

simple-sampler - A simple MPC type sampler using the WebAudio API

  •    Javascript

a simple demonstration of webaudio to build a mpc like sampler

project1 - Project 1

  •    Modelica

For the main web site, visit https://ibpsa.github.io/project1/. For the wiki that contains meeting announcements and agendas, visit https://github.com/ibpsa/project1/wiki.

PyAdvancedControl - Python codes for advanced control

  •    Python

Python codes for advanced control

dotfiles - My dotfiles managed by GNU Stow - Arch, i3-gaps, bspwm, ncmpcpp, (neo)vim, zsh etc.

  •    Shell

This is my collection of user/application settings ("dotfiles") and personal scripts. They are mostly adapted to my personal needs, and some scripts make a few assumptions about the environment that may not necessarily be considered "standard", so it's not recommended to just copy-paste them as-is. Nevertheless, I try to keep them as clean and non-WTF as possible, and people are invited to take a look at them, get ideas for their own dotfiles, and drop comments, suggestions, questions and bug reports if something seems odd.

ctrlp-mpc.vim - Control MPD from vim, through mpc, with ctrlp!

  •    VimL

ctrlp-mpc.vim has a little cache in place for artists. These can be A LOT, and on my library it takes a couple of seconds to load, so I figured I'd add a cache for it.

mpc1k-node - A library to parse & generate preset files (PGM) for the AKAI MPC1000

  •    Javascript

A library to parse & generate preset files (PGM) for the AKAI MPC1000

simple-mpc - A GNU Emacs major mode which acts as a front end to mpc.

  •    Emacs

A GNU Emacs major mode that acts as a front end to mpc. The easiest way to install is through MELPA.

control_box_rst - The control_box_rst package provides C++ libraries for predictive control, direct optimal control, optimization and simulation

  •    C++

Build and installation instructions as well as further documentation are provided in the project wiki. Since a lot of time and effort has gone into the development, please cite at least one of the following publications if you are using the software for published work.

mpc_local_planner - The mpc_local_planner package implements a plugin to the base_local_planner of the 2D navigation stack

  •    C++

The mpc_local_planner package implements a plugin to the base_local_planner of the 2D navigation stack. It provides a generic and versatile model predictive control implementation with minimum-time and quadratic-form receding-horizon configurations. For custom build instructions (e.g. compilation with other third-party solvers) see this wiki.

autogenu-jupyter - An automatic code generator for nonlinear model predictive control (NMPC) and the continuation/GMRES method (C/GMRES) based numerical solvers for NMPC

  •    C++

This project provides the continuation/GMRES method (C/GMRES method) based solvers for nonlinear model predictive control (NMPC) and an automatic code generator for NMPC, called AutoGenU. You can also build source files for numerical simulation, execute numerical simulation, and plot or save simulation result on AutoGenU.ipynb.

ilqr - Iterative Linear Quadratic Regulator with auto-differentiatiable dynamics models

  •    Python

This is an implementation of the Iterative Linear Quadratic Regulator (iLQR) for non-linear trajectory optimization based on Yuval Tassa's paper. It is compatible with both Python 2 and 3 and has built-in support for auto-differentiating both the dynamics model and the cost function using Theano.

pddp - WIP implementation of Probabilistic Differential Dynamic Programming in PyTorch

  •    Jupyter

Probabilistic Differential Dynamic Programming (PDDP) is a data-driven, probabilistic trajectory optimization framework for systems with unknown dynamics. This is an implementation of Yunpeng Pan and Evangelos A. Theodorou's paper in PyTorch, [1]. This is a work in progress and does not work/converge as is yet.

controls-js - ⚙️ Controls

  •    Javascript

Controls.js is a sandbox showcasing a few modern controls techiques directly in the browser. It harnesses eigen-js for all linear algebra and quadratic programming, and nlopt-js for non-linear optimization.

SyMPC - A SMPC companion library for Syft

  •    Python

SyMPC /ˈsɪmpəθi/ is a library which extends PySyft ≥0.3 with SMPC support. It allows computing over encrypted data, and to train and evaluate neural networks. SyMPC is a companion library for PySyft. Therefore, we will need to install PySyft among other dependencies. We recommend using a virtual environment like conda.

high_mpc - Learning High-Level Policies for Model Predictive Control.

  •    C

The combination of policy search and deep neural networks holds the promise of automating a variety of decision- making tasks. Model Predictive Control (MPC) provides robust solutions to robot control tasks by making use of a dynamical model of the system and solving an optimization problem online over a short planning horizon. In this work, we leverage probabilistic decision-making approaches and the generalization capability of artificial neural networks to the powerful online optimization by learning a deep high-level policy for the MPC (High-MPC). Conditioning on robot's local observations, the trained neural network policy is capable of adaptively selecting high-level decision variables for the low-level MPC controller, which then generates optimal control commands for the robot. First, we formulate the search of high-level decision variables for MPC as a policy search problem, specifically, a probabilistic inference problem. The problem can be solved in a closed-form solution. Second, we propose a self-supervised learning algorithm for learning a neural network high-level policy, which is useful for online hyperparameter adaptations in highly dynamic environments. We demonstrate the importance of incorporating the online adaption into autonomous robots by using the proposed method to solve a challenging control problem, where the task is to control a simulated quadrotor to fly through a swinging gate. We show that our approach can handle situations that are difficult for standard MPC. Please find a list of demonstrations in here.

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