Mechatronic System Design

MATLAB, Simulink, and Simscape for Mechatronic System Design

Design, optimize, and verify mechatronic systems

Developing mechatronic systems requires integrating physical subsystems with control systems and embedded software. Engineers use Model-Based Design to model, simulate, and verify multidisciplinary mechatronic systems from initial development to production.

With MATLAB, Simulink, and Simscape, you can:

Understand complex system interactions from algorithm design to plant behavior
Speed up development by working in parallel with multiple teams
Predict and optimize system performance
Improve the quality of mechatronic systems and test using fewer hardware prototypes
Eliminate manual coding errors by automatically generating code from simulation models
Maintain traceability from requirements to design to code
Reuse design models as operational digital twins

“Using Simulink for Model-Based Design enables us to develop the sophisticated pneumatic controls required for the Bionic Handling Assistant and other mechatronic designs. With Simulink PLC Coder, it is now much easier to get from a design to a product.”
Dr. Rüdiger Neumann, Festo

Using MATLAB, Simulink, and Simscape for Mechatronic System Design

German Aerospace Center (DLR) Robotics and Mechatronics Center Develops the Autonomous Robot Justin with MATLAB and Simulink

Getting Started with Simscape

B&R Develops Virtual Sensor Technology to Improve Servo Drive Performance

Accelerate Mechatronic System Development with MATLAB and Simulink

Modeling

Use Simscape to develop system or component level models to represent the physical portions of the system in the electrical, mechanical, or fluid domains. Import designs from existing CAD files to visualize 3D physical components and SPICE subcircuits to incorporate manufacturer specific behavior. Optimize system performance and detect integration errors early in development via simulation. Repurpose simulation models for virtual commissioning or operational digital twins.

Getting Started with Simscape

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Model-Based Design in Industrial Automation at Tetra Pak (9:32)

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Control Design and Supervisory Logic

Linearize nonlinear physical models to develop closed-loop control systems with linear control techniques, like Bode plots or root locus, or use advanced control strategies, like model predictive control or robust control. Leverage prebuilt functions and interactive tools to automatically tune and optimize controllers to meet the performance requirements and stability constraints of your system. Analyze key performance and stability characteristics in the time and frequency domains such as overshoot, rise time, phase margin, and gain margin.

Develop and verify state machines for supervisory control and error handling. Use graphical animation to analyze and debug supervisory logic while it is executing to identify potential design errors.

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Design of Safety Control Logic for Crane Application (4:28)

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Hardware-in-the-Loop Testing and Rapid Control Prototyping

Refine your algorithms with rapid control prototyping (RCP) to prepare for your production environment. Use hardware-in-the-loop (HIL) simulations of your plant and environment model to reduce physical prototypes. Run real-time simulations on Speedgoat hardware and analyze the results in MATLAB to improve the performance of your mechatronic system.

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Production Code Generation

Eliminate manual coding errors by automatically generating optimized C, C++, IEC 61131-3 (Structured Text and Ladder Diagram), CUDA^®, Verilog^®, or VHDL code directly from MATLAB and Simulink. Leverage floating and fixed-point design tools to investigate performance tradeoffs. Integrate the generated hardware independent code into your PLC platform’s integrated development environment (IDE) for deployment on your real-time hardware and for online debugging.

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Model-Based Design for Predictive Maintenance (7 Videos)

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Verification and Validation

Author, import, and manage requirements in your model to maintain traceability across designs, tests, and generated code. Prove that designs meet requirements, automatically generate test cases for model coverage, and improve the quality of designs throughout the development process using formal test methods. Check model and code compliance using formal methods and static analysis. Find bugs and prove the absence of critical run-time errors with static code analysis. Produce reports and artifacts necessary to certify to industry standards such as IEC 61508, ISO 26262, and DO-178.