Unlock Performance in Control Systems
JuliaSim Control is a comprehensive toolkit for the design, simulation, analysis, and optimization of control systems. Built with the Julia programming language and integrated with ModelingToolkit.jl and the JuliaControl ecosystem, it offers a unified platform for the analysis, design and optimization of linear and nonlinear control systems.
Enhance Control System Performance And Work With High-Fidelity Models
Current tools face significant challenges when working with high-fidelity models and large-scale complex systems. Engineers often need to simplify these models to make them manageable, which can lead to inaccurate or incomplete solutions or can be prohibitively expensive. JuliaSim Control addresses these issues by providing an environment optimized for handling complex, large-scale control problems more efficiently. This allows engineers to work directly with complex systems without the need for oversimplification or higher computational costs.
CHALLENGES
IN CONTROL ENGINEERING
System Complexity Multi-domain, large scale systems often with nonlinear dynamics and interconnected components
Computational Performance
Computational inefficiency in high-fidelity models and large-scale systems
Adaptation to Uncertainty
Adapting to uncertain environments or incomplete data
OUR SOLUTION
Comprehensive tools
for the analysis and design of both linear and nonlinear control systems
Efficiency
Users can use their existing high-fidelity models without simplifying by using features like Model-Predictive Control (MPC) and PID autotuning.
Comprehensive Suite
JuliaSim Control supports linear and nonlinear systems, offering a complete toolkit for control system design.
Versatility
From robust MPC for uncertain systems to state estimation for nonlinear DAE systems, JuliaSim Control adapts to a wide range of applications.
Optimization
Automatic tuning of controller parameters and trajectory optimization ensure optimal performance in every scenario.
Ease of Use
With a modern interface and seamless integration within the Julia ecosystem, JuliaSim Control is accessible to both new and expert users.
Supports Innovation
Ideal for engineers, scientists and researchers looking to design and implement advanced control systems
Optimization
Automatic tuning of controller parameters and trajectory optimization ensure optimal performance in every scenario.
Efficiency
Users can use their existing high-fidelity models without simplifying by using features like Model-Predictive Control (MPC) and PID autotuning.
Ease of Use
With a modern interface and seamless integration within the Julia ecosystem, JuliaSim Control is accessible to both new and expert users.
Comprehensive Suite
JuliaSim Control supports linear and nonlinear systems, offering a complete toolkit for control system design.
Supports Innovation
Ideal for engineers, scientists and researchers looking to design and implement advanced control systems
Versatility
From robust MPC for uncertain systems to state estimation for nonlinear DAE systems, JuliaSim Control adapts to a wide range of applications.
FEATURES
Rapid prototyping,
validation & deployment of ideas
Model-predictive control (MPC) for linear and nonlinear systems
Robust MPC for uncertain systems
Surrogatization of MPC controllers for reduced computational complexity
JuliaSim Control is implemented in Julia and allows rapid prototyping, validation & deployment of ideas without the need to translate the code to a lower-level language. It builds on ModelingToolkit.jl a symbolic acausal, modeling framework for model component based physical systems. This makes it easy to build detailed plant models out of reusable components.
State estimation for nonlinear DAE-systems
PID autotuning to automate workflows and quickly tune PID controllers
Optimal control and trajectory optimization
Automatic tuning of controller parameters to meet design criteria
GUI apps for autotuning and model reduction
Full suite of classical control tools
Modeling and execution of discrete-time controllers and state machines
JuliaSim Control is implemented in Julia and allows rapid prototyping, validation & deployment of ideas without the need to translate the code to a lower-level language. It builds on ModelingToolkit.jl a symbolic acausal, modeling framework for model component based physical systems. This makes it easy to build detailed plant models out of reusable components.
State estimation for nonlinear DAE-systems
PID autotuning to automate workflows and quickly tune PID controllers
Optimal control and trajectory optimization
Automatic tuning of controller parameters to meet design criteria
GUI apps for autotuning and model reduction
Full suite of classical control tools
Modeling and execution of discrete-time controllers and state machines
Model-predictive control (MPC) for linear and nonlinear systems
Robust MPC for uncertain systems
Surrogatization of MPC controllers for reduced computational complexity
LEARN MORE ABOUT JULIASIM CONTROL AND HOW IT IS BEING USED
Advanced Strategies for Optimal Control
Learn more about advancements in autonomous systems, optimal control, and consensus protocols for multi-vehicle coordination
Access our library of control videos and tutorials
Discover the potential of JuliaSim Control to create system models, do frequency-domain analysis, implement PID-controller design and more
Acausal Modeling for Nonlinear Control and Analysis
Use acausal modeling to define system models for both simulation and control to design and analyze complex control systems