Design cyber-physical systems with MATLAB and Simulink
Cyber-physical systems are complex engineered systems that link cyberspace with the physical world through a network of interconnected computational elements, such as sensors, actuators, and computational processing units. These systems are highly automated, intelligent, and collaborative. Applications of cyber-physical systems include energy-neutral buildings, zero-fatality highways, personalized medical devices, smart cities, and smart manufacturing.
Designing cyber-physical systems is challenging because:
- The vast network and information technology environment connected with physical elements involves multiple domains such as controls, communication, analog and digital physics, logic, and software engineering.
- The interaction with the physical world varies widely based on time and situation.
Using multidomain models that capture such variability is critical to successful design of cyber-physical systems.
You can model the information domain of cyber-physical systems with MATLAB®, Simulink®, Stateflow®, and related products (such as Control System Toolbox™, Communications Toolbox™, Signal Processing Toolbox™, Computer Vision Toolbox™, Deep Learning Toolbox™, Statistics and Machine Learning Toolbox™, and Reinforcement Learning Toolbox™). You can also model physical behavior with Simscape™ and event-driven and message-based behavior with SimEvents™.
These products enable system-level design that spans multiple domains and levels of fidelity.
Learn how engineers who design cyber-physical systems apply MATLAB and Simulink:
- System architecture modeling and analysis for model-based systems engineering
- Verification, validation, and testing for high-integrity system design
- Hardware-in-the-loop testing with real-time simulation
- Automatic code generation onto embedded CPUs, GPUs, and FPGAs, ASICs and SoC target platforms
- Parallel computing and cloud computing to scale up computational resources
- Digital Twin and Internet-of-Things (IoT) configurations with operation-time simulation and analysis
- Automated driving applications that include driving scenarios and modeling of 3D scenes
Examples and How To
See also: Model-Based Design, design of experiments, mechatronics, real-time simulation, wireless communications, stereo vision, abstract interpretation, analytical solution, formal verification, robot programming, CAN network communication, control synthesis with model checking, Smart Emergency Response System