A hybrid system with both continuous time and discrete event sections. The discrete event part models tanks, represented by entities, which are being queued and need to be filled up. Each tank has a "Capacity" attribute. The continuous time part models the process of filling up a tank, modeled by an Integrator. When a tank is filled to capacity, this event can be detected by a Hit Crossing block, which will generate a message corresponding to this event. The generated message will trigger the server to release the tank.

How, depending on the workload, a AT90S8535 microcontroller uses a dynamic voltage scaling (DVS) feature to adjust the input voltage. By lowering the input voltage when the workload is low, the microcontroller reduces energy consumption while guaranteeing quality of service. The DVS controller is based on an online gradient estimation technique called infinitesimal perturbation analysis (IPA). In a single simulation of a parameterized system, not the large number of simulations required by a traditional finite-difference approach, IPA can provide sensitivity information that yields a first-order approximation of the system performance metrics as a function of the parameters.

Model the task scheduling of a control application using SimEvents blocks. SimEvents expands Simulink with the capability to model and simulate architectural components of a real-time system.

Model a customer scheduler using the SimEvents MATLAB Discrete-Event System block. The model includes a Scheduler block that can simulate a multicore system with an arbitrary number of cores, tasks, and mutually exclusive resources.

How stochastic network traffic causes timing latency and uncertainty in an anti-lock braking system (ABS) that uses Control Area Network (CAN) communications. The model is representative of a real-world heavily-loaded network and also illustrates a domain-specific model of a distributed system. By including real-world timing effects in a model, you gain confidence about the behavior and robustness of your design before you test it in hardware.

Cyber-physical systems combine computer and physical systems to achieve design goals. Simulation of cyber-physical systems requires a combination of modeling techniques such as continuous-time, discrete-time, discrete-event, and finite state modeling. Simulink® and its companion products provide functionality to apply a wide range of modeling techniques and seamlessly integrate them in one simulation environment, which is ideal for modeling cyber-physical systems.