Explore examples that show how to control cyber-physical
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.
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.
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