martindoff/DC-NN-MPC
Computationally tractable learning-based nonlinear tube MPC using difference of convex neural network dynamic approximation
This project helps control complex, unpredictable dynamic systems, like a PVTOL aircraft, by learning their behavior and then planning future actions. It takes sensor data from the system and outputs optimal control commands to guide it along a desired path, even when disturbances occur. This is for control engineers or researchers who need to manage the movement or state of advanced machinery or autonomous systems.
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Use this if you need a computationally efficient way to robustly control a nonlinear system where precise, stable operation is critical despite unknown disturbances.
Not ideal if your system dynamics are simple and linear, or if you don't require robust control against uncertainties.
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Python
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Last pushed
Jul 05, 2024
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