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pro vyhledávání: '"Hose, Henrik"'
Complicated first principles modelling and controller synthesis can be prohibitively slow and expensive for high-mix, low-volume products such as hydraulic excavators. Instead, in a data-driven approach, recorded trajectories from the real system can
Externí odkaz:
http://arxiv.org/abs/2409.16875
Publikováno v:
PMLR 242:349-360, 2024
Model Predictive Control (MPC) is a method to control nonlinear systems with guaranteed stability and constraint satisfaction but suffers from high computation times. Approximate MPC (AMPC) with neural networks (NNs) has emerged to address this limit
Externí odkaz:
http://arxiv.org/abs/2404.05835
Model predictive control (MPC) achieves stability and constraint satisfaction for general nonlinear systems, but requires computationally expensive online optimization. This paper studies approximations of such MPC controllers via neural networks (NN
Externí odkaz:
http://arxiv.org/abs/2304.09575