eta-ctrl
eta-ctrl
A modular framework for rolling-horizon operational control using a Gym-compatible agent–environment abstraction. The framework enables integration of optimization-based agents (Pyomo), reinforcement learning agents (Stable-Baselines3), heuristic search methods, and rule-based controllers. Environments can be instantiated from FMI-compliant simulations, mathematical models, or real-world system interfaces.
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- TU Darmstadt Germany
optimization-based control, reinforcement learning, Functional Mock-up Interface FMI, model predictive control (MPC), eta factory, cyber-physical systems, industrial energy systems, Pyomo, Stable-Baselines3, simulation and co-simulation, rule-based control, Gym interface, real-time control, rolling horizon control
optimization-based control, reinforcement learning, Functional Mock-up Interface FMI, model predictive control (MPC), eta factory, cyber-physical systems, industrial energy systems, Pyomo, Stable-Baselines3, simulation and co-simulation, rule-based control, Gym interface, real-time control, rolling horizon control
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