Tools for Non-Linear Model Predictive Control
Learn non-linear Model-Predictive Control (MPC) to develop a lane-changing controller in this real-time demo using JuliaSim Control.
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Wednesday, March 29th | 12:30 PM ET (US)
Tools for Non-Linear Model Predictive Control (MPC)
Learn non-linear Model-Predictive Control (MPC) to develop a lane-changing controller in this real-time demo using JuliaSim Control. In non-linear MPC you have the advantage of incorporating a plant model with non-linear dynamics; a non-quadratic cost function with high interpretability; and non-linear constraints. In this webinar, we will review JuliaSim Control's suite of tools to design model predictive controllers.
Using a realistic case study of controlling a self-driving car, you'll learn to:
Build: simple dynamical models using Julia
Specify: reference trajectories and other constraints to be satisfied by the controller
Analyze: the final synthesized controller to understand its performance
Basic familiarity with modeling and simulation is all you need to amplify your skills. Register Now.
Register now to reserve your spot!
Dr. Ranjan Anantharaman
Ranjan has a PhD in Mathematics & Computational Science from the Massachusetts Institute of Technology. He is a sales engineer at JuliaHub, where he helps customers leverage modern computational methods through JuliaSim.
Combine models with tools like DiffEqFlux and NeuralPDE to discover missing physics and generate digital twins.
Combine with Pre-Built Models and Digital Twins
Grab complete models from the JuliaSim Model Store and compose the pieces to accelerate the design process.
Specialized Numerical Environments
Use the latest numerical tools, like discontinuity-aware differential equation solvers, high-performance steady state solvers, and domain-specific environments.
Blending classical physical modeling with modern Scientific Machine Learning techniques.
JuliaSim is a next generation cloud-based simulation platform, combining the latest techniques in Scientific Machine Learning with equation-based digital twin modeling and simulation. Our modern ML-based techniques accelerate simulation by up to 500x, changing the paradigm of what is possible with computational design. The premise of the software is to facilitate the design and accelerate challenging real-life models of considerable complexity.
JuliaSim allows the user to import models directly from the Model Store (more information below) into the Julia environment, making it easy to build large complex simulations. The user-friendly GUI facilitates the process and makes simulation more accessible to a wider audience.
JuliaSim produces surrogates of blackbox (and regular) dynamical systems using Continuous Time Echo State Networks (CTESNs). This novel technique allows, amongst other features, for implicit training in parameter space to stabilize the ill-conditioning present in stiff systems.
Learn more about the JuliaSim Ecosystem
Julia Computing delivers JuliaSim as an answer to accelerating simulations through digital-twin (or surrogate) modeling. By blending classical, physical modeling with advanced scientific machine learning (SciML) techniques, JuliaSim provides a next-generation platform for building, accelerating, and analyzing models.