Machinery and robotics engineering increasingly depends on software. Control logic, system interactions, and operating modes influence how machines are integrated, validated, and updated, but these behaviors are not always tested early or managed consistently across variants.
The workflows on this page focus on working with system and control behavior earlier and more deliberately. This approach enables teams to validate software before hardware integration, assess the impact of changes, and reuse engineering evidence as systems evolve.
To work with behavior earlier, teams need a clear way to describe how a system is expected to behave before it is implemented in hardware or PLC code. This includes control logic, operating modes, and interactions between system components.
A model‑based systems engineering approach supports this by allowing teams to define and simulate system and control behavior independently of specific hardware or controller implementations. Building on established Model‑Based Design practices, engineers can review expected behavior, assess the impact of changes, and validate assumptions earlier in the development process.
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Model-Based Systems Engineering for Machine Control Software
Solution
When system and control behavior is available in executable form, teams can test software before hardware is built. This approach makes it easier to identify issues early, when changes are simpler and less risky.
In machinery and robotics projects, control software is tested in simulation to identify integration issues, timing problems, and unexpected interactions before commissioning on the physical machine. This helps reduce late changes during commissioning while maintaining existing control platforms.
MATLAB® and Simulink® support virtual commissioning by combining control logic, machine models, and test scenarios in a single workflow. Engineers can run repeatable tests, explore operating conditions, and evaluate changes offline before equipment is built or modified.
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Discovery
In many machinery and robotics projects, AI is applied to specific functions, such as anomaly detection, predictive maintenance, and reinforcement learning–based control. These functions typically have a clearly defined scope and must integrate with existing control software and validation workflows.
Engineering teams need to design and test AI components together with control logic and machine behavior at the system level. This makes it possible to evaluate AI behavior under normal and edge operating conditions and to understand its interaction with the rest of the machine.
MATLAB and Simulink support this workflow in a single environment. Engineers can develop AI algorithms, test them in system‑level simulations, and use plant models to generate synthetic data when measured data is limited or difficult to obtain. The high‑level MATLAB environment lowers the barrier to developing and iterating on AI algorithms, especially for engineers without a dedicated data science background.
As machine systems grow in scope, connectivity, and reuse, safety, cybersecurity, and AI‑related requirements depend strongly on system behavior. Control logic, operating modes, interfaces, and data flows all affect how machines behave across variants and configurations.
When system and control behavior is defined and tested during development, requirements, design decisions, and test results remain connected. This makes it easier to understand the impact of system and control changes, confirm that safety and cybersecurity assumptions still hold, and evaluate AI behavior under defined conditions.
MATLAB and Simulink support this by keeping requirements, models, tests, coverage, and results linked in a single workflow. As control software is reused and adapted, the same engineering artifacts can be reused to support safety, cybersecurity, and AI‑related assessments without relying on late documentation or manual rework.
