Deepak Ingole's repositories
Data-Driven-Predictive-Control
Data-Driven Predictive Control
Model-Predictive-Control-4
MPC controllers for temperature regulation of a building. Graded project for the ETH course "Model Predictive Control".
adaptive-model-predicitive-control-matlab
Adaptive Model Predicitive Control - Matlab
CartPoleSimulation
This repository contains CartPole simulator with its GUI, implemented controller (LQR) and generator of random desired position trace. It also contains files to train and test RNN predicting future states of a CartPole.
Enhancement-of-direct-torque-control-of-three-phase-induction-motor
Control of three phase induction motor using Sliding Mode Control and Direct Torque Control and compare the performance between each of them,
gym
A toolkit for developing and comparing reinforcement learning algorithms.
introtodeeplearning
Lab Materials for MIT 6.S191: Introduction to Deep Learning
kdro
Code for the paper: Kernel Distributionally Robust Optimization
keras-rl
Deep Reinforcement Learning for Keras.
LMPC
This repo collets few LMPC examples coded in Python
MPC-Project---Trajectory-Generation-Using-MPC-For-High-Speed-Overtaking
In this study model predictive control is applied to a vehicle overtaking slower moving vehicles in a one way, two lane road. A risk map is defined considering the road boundaries, the center of the two lanes, and distance relative to other vehicles. The results of this study found that the vehicle was able to conduct safe lane changes while avoidi
mpc_av
Model Predictive Controller for Local Trajectory Generation and Tracking of Autonomus Vehicles
mpc_ros
Nonlinear Model Predictive Control on Differential Wheeled Mobile Robot using ROS
panweihit.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
parodis
This is the official repository to PARODIS, the Matlab PAReto Optimal Model Predictive Control framework for DIstributed Systems.
phd-bibliography
References on Optimal Control, Reinforcement Learning and Motion Planning
PINNs-based-MPC
We discuss nonlinear model predictive control (NMPC) for multi-body dynamics via physics-informed machine learning methods. Physics-informed neural networks (PINNs) are a promising tool to approximate (partial) differential equations. PINNs are not suited for control tasks in their original form since they are not designed to handle variable control actions or variable initial values. We thus present the idea of enhancing PINNs by adding control actions and initial conditions as additional network inputs. The high-dimensional input space is subsequently reduced via a sampling strategy and a zero-hold assumption. This strategy enables the controller design based on a PINN as an approximation of the underlying system dynamics. The additional benefit is that the sensitivities are easily computed via automatic differentiation, thus leading to efficient gradient-based algorithms. Finally, we present our results using our PINN-based MPC to solve a tracking problem for a complex mechanical system, a multi-link manipulator.
Pontryagin-Differentiable-Programming
A unified end-to-end learning and control framework that is able to learn a (neural) control objective function, dynamics equation, control policy, or/and optimal trajectory in a control system.
quadrotor
Quadrotor control, path planning and trajectory optimization
reinforcement-learning-an-introduction
Python Implementation of Reinforcement Learning: An Introduction
rlqp
Accelerating Quadratic Optimization with Reinforcement Learning