MATLAB and Simulink capabilities for AI with Model-Based Design

As engineers, we are no strangers to AI’s transformative potential or the rigor of model-based design (MBD) for system development. But the real opportunity lies in combining AI with MBD—a fusion that allows us to enhance model accuracy, optimize control strategies, and innovate faster, all while leveraging the physics-based foundation of MBD.

This blog focuses on three critical AI techniques:

• Virtual sensor modeling: Enhancing system observability without additional hardware.
• Reduced-order modeling (ROM): Accelerating simulations for real-time applications.
• Reinforcement learning (RL): Training intelligent systems to optimize performance in complex scenarios.

While you might already be familiar with these concepts individually, this post will highlight how MATLAB and Simulink provide an integrated platform to combine AI and MBD effectively. Whether you’re tackling automotive control systems, aerospace design, robotics, or energy optimization, these tools can streamline your workflow and accelerate results.

Why combine AI with MBD?

MBD excels at designing and validating systems with physics-based models, while AI provides the ability to learn from data and make intelligent decisions. Together, they address key challenges like:

• Improving system observability: AI can help predicting signals or values that are hard or impossible to measure.
• Handling high computational complexity: Reduced-order models (ROMs) bridge the gap between accuracy and speed.
• Optimizing control strategies: Reinforcement learning adapts control to accommodate complex, dynamic environments.

MATLAB and Simulink are well known for their robust capabilities in both AI and MBD. These tools offer a seamless environment for developing, simulating, and deploying complex systems. Industries such as automotive, aerospace, and robotics have leveraged these platforms to enhance their AI-driven engineering workflows. The integration of AI with MBD in MATLAB and Simulink allows for more sophisticated system designs and simulations, which bridge the gap between theoretical models and practical applications.

Virtual sensor modeling

Virtual sensor modeling involves creating software-based sensors that estimate physical quantities using mathematical models and real sensor data. This approach is particularly useful in scenarios where physical sensors are impractical or too costly.

Example: An automotive engine control system might use a virtual oxygen sensor to estimate air-fuel ratios based on system dynamics and real-time data. This avoids the cost and durability issues associated with physical sensors.

How MATLAB and Simulink help

MathWorks provides a comprehensive suite of tools for virtual sensor modeling. Simulink’s simulation capabilities, combined with MATLAB’s data analysis functions, enable the development of high-fidelity virtual sensors. These tools support a model-based design approach that allows for rapid prototyping and iterative testing.

• Experiment with multiple architectures: Design and compare models using various deep learning and machine learning approaches.
• Leverage external AI models: Seamlessly import AI models developed in frameworks such as TensorFlow™ or PyTorch® and integrate them into Simulink for simulation and deployment.
• System integration: Simulate and validate AI-based virtual sensors alongside other system components within the same environment.
• Optimize for deployment: Compress AI models to reduce complexity and deploy them efficiently to microcontrollers or ECUs using library-free C code generation.
• Real-time adaptation: Enable virtual sensor models to process live data streams by incorporating incremental learning techniques.
• Increased flexibility: Combine data-driven models with physics-based components for enhanced robustness.

Learn to develop virtual sensor models using advanced AI techniques such as feedforward neural networks, LSTMs, and decision trees in our on-demand webinar, AI for Model-Based Design: Virtual Sensor Modeling.

By investing in MATLAB and Simulink, your organization can:

Speed up development

• Streamline workflows and automate repetitive tasks;
• Quickly iterate on designs and explore multiple options;
• Reduce time-to-market for new products and features.

Enhance accuracy and reliability

• Create high-fidelity models that accurately represent real-world systems;
• Identify potential issues early on in the design process;
• Reduce the need for costly physical prototypes and testing.

Foster collaboration and knowledge sharing

• Facilitate seamless teamwork and knowledge transfer across teams;
• Improve decision-making and problem-solving;
• Centralize and manage engineering data and IP.

By adopting MATLAB and Simulink, your organization can gain a competitive edge, drive innovation, and achieve long-term success.

Reduced-order modeling (ROM)

Reduced-order modeling simplifies complex systems by creating lower-dimensional models that retain essential characteristics. This technique is crucial for efficient simulations and real-time applications.

Example: In aerospace design, aerodynamic models often involve computationally intensive simulations. ROM simplifies these models to enable quicker analysis and integration into flight controllers.

How MATLAB and Simulink help

MATLAB and Simulink offer various model reduction techniques, such as balanced truncation and proper orthogonal decomposition. These tools help engineers create and validate reduced-order models to ensure faster simulations and reduced computational costs.

• Develop AI-based models: Build dynamic system models using measured or simulated data to capture nonlinear behavior effectively.
• Data-driven Tools: Use the Deep Network Designer, the Regression Learner or the System Identification app to create AI-driven models from experimental data, enabling more accurate and predictive system insights.
• Combine physics and AI: Enhance model fidelity by merging physics-based insights with AI techniques, such as nonlinear model identification using neural state space models, nonlinear ARX, and other advanced architectures.
• Leverage third-party models: Simplify FEM, FEA, and CFD models by creating AI-based reduced-order representations for integration into Simulink for control design and system development.
• Streamlined workflow: Use the Reduced Order Modeler app to configure design of experiments (DoE), generate training datasets, and utilize ready-made templates to train and evaluate AI-driven ROMs.
• Flexible deployment options: Bring reduced-order models into Simulink for desktop simulation or hardware-in-the-loop testing, or export them as Functional Mock-Up Units (FMUs) for use in external environments.

Speed up simulations and analysis of complex systems without compromising on accuracy by replacing a high-fidelity model with a reduced-order model in our on-demand webinar, AI for Model-Based Design: Reduced Order Modeling

By implementing ROM, your organization can:

• Speed up simulations and analysis:
  • Reduce model complexity and computational cost;
  • Enable faster design iterations and optimization;
  • Accelerate time-to-market for new products.
• Lower computational costs:
  • Reduce hardware and software requirements;
  • Minimize energy consumption and operational costs.
• Enhance real-time performance:
  • Improve the responsiveness of real-time systems;
  • Enable more complex simulations and control strategies;
  • Optimize system performance in real-time.

By adopting ROM, your organization can make more informed decisions, optimize designs, and achieve greater efficiency and productivity.

Reinforcement learning (RL) in Simulink

Reinforcement learning is an AI technique where agents learn to make decisions through trial and error by interacting with their environment. It is widely used in control systems and robotics to develop intelligent, adaptive behaviors.

Example:In robotics, RL can train manipulators to perform tasks like pick-and-place operations while adapting to new payloads or configurations.

How MATLAB and Simulink help

The Reinforcement Learning Toolbox in MATLAB, combined with Simulink, provides a seamless environment for developing and deploying reinforcement learning models. Engineers can simulate and test their models within Simulink before deploying them to physical systems.

• Dynamic environment interactions: Train intelligent agents by enabling them to learn through iterative trial-and-error interactions within dynamic environments modeled in Simulink.
• Algorithm flexibility: Choose from a wide range of built-in reinforcement learning algorithms and implement them seamlessly using the RL Agent block in Simulink.
• Interactive development tools: Leverage the Reinforcement Learning Designer app to visually design, train, and simulate RL agents in an interactive and user-friendly interface.
• System-level validation and deployment: Perform comprehensive system-level testing of RL agents and deploy them using automatic code generation to embedded systems, ensuring their readiness for real-world applications.

Learn how to apply Reinforcement Learning using MATLAB® and Simulink® products in our on-demand webinar, Reinforcement Learning Workflow in MATLAB

By leveraging RL, your organization can:

 

• Speed up development and testing:
  • Automate the design and optimization process;
  • Reduce the time and effort required to develop control algorithms;
  • Accelerate the testing and validation of new designs.
• Seamlessly integrate with physical systems:
  • Deploy trained RL agents to control real-world systems;
  • Enable adaptive and intelligent behavior in dynamic environments;
  • Optimize system performance in real-time.
• Enhance performance and robustness:
  • Develop robust and adaptive control strategies;
  • Improve system performance and efficiency;
  • Reduce the risk of system failures and malfunctions.

By adopting RL, your organization can create more intelligent, autonomous, and resilient systems.

Conclusion

Virtual sensors, ROMs, and RL aren’t stand-alone tools—they form a cohesive framework when combined in MATLAB and Simulink. For example:

• Virtual Sensors provide critical insights for RL training or ROM validation.
• ROMs reduce computational requirements, which enables faster RL training and real-time deployment.
• All components integrate within a unified Simulink model to ensure consistency and scalability.

Case study:

Imagine developing a hybrid-electric vehicle. You could:

1. Use virtual sensors to monitor battery health without adding hardware;
2. Develop reduced-order models of the powertrain for real-time optimization;
3. Train a reinforcement learning agent to optimize energy management and improve efficiency.

MATLAB and Simulink enable you to achieve this within a single environment, streamlining design, testing, and deployment.

Featured products

MathWorks® products:

• MATLAB
• Simulink
• Reinforcement Learning Designer app
• Reinforcement Learning Toolbox

Learn more

• On-demand webinar: AI for Model-based Design: Virtual Sensor Modeling
  Learn to develop virtual sensor models using advanced AI techniques such as feedforward neural networks, LSTMs, and decision trees.
  Watch the on-demand webinar
• On-demand webinar: AI for Model-Based Design: Reduced Order Modeling
  Learn to distill complex systems into simplified models; ROM streamlines the design process, making simulations faster, more efficient, and easier to manage.
  Watch the on-demand webinar
• On-demand webinar: Reinforcement Learning Workflow in MATLAB
  Learn how to apply Reinforcement Learning using MATLAB® and Simulink® products
  Watch the on-demand webinar