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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.

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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

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System Complexity Multi-domain, large scale systems often with nonlinear dynamics and interconnected components

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Computational Performance
Computational inefficiency in high-fidelity models and large-scale systems

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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

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Efficiency

Users can use their existing high-fidelity models without simplifying by using features like Model-Predictive Control (MPC) and PID autotuning.   

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Comprehensive Suite

JuliaSim Control supports linear and nonlinear systems, offering a complete toolkit for control system design.

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Versatility

From robust MPC for uncertain systems to state estimation for nonlinear DAE systems, JuliaSim Control adapts to a wide range of applications.

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Optimization

Automatic tuning of controller parameters and trajectory optimization ensure optimal performance in every scenario.

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Ease of Use

With a modern interface and seamless integration within the Julia ecosystem, JuliaSim Control is accessible to both new and expert users. 

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Supports Innovation

Ideal for engineers, scientists and researchers looking to design and implement advanced control systems

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Optimization

Automatic tuning of controller parameters and trajectory optimization ensure optimal performance in every scenario.

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Efficiency

Users can use their existing high-fidelity models without simplifying by using features like Model-Predictive Control (MPC) and PID autotuning.   

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Ease of Use

With a modern interface and seamless integration within the Julia ecosystem, JuliaSim Control is accessible to both new and expert users. 

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Comprehensive Suite

JuliaSim Control supports linear and nonlinear systems, offering a complete toolkit for control system design.

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Supports Innovation

Ideal for engineers, scientists and researchers looking to design and implement advanced control systems

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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
Control
Advanced Strategies for Optimal Control

Learn more about advancements in autonomous systems, optimal control, and consensus protocols for multi-vehicle coordination

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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

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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

Ready to see how JuliaSim can accelerate your product development? Speak to a member of our team.