
This paper proposes a novel framework for robust nonlinear model predictive control (NMPC) of large-scale distributed parameter systems (DPS) under uncertainty, employing a powerful combination of polynomial chaos expansion (PCE), proper orthogonal decomposition (POD), and recurrent neural networks (RNNs) to handle uncertainty, high dimensionality, and non-convexity.


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robust-model-predictive-control-for-large-scale-distributed-parameter-systems-under-uncertainty-a-polynomial-chaos-expansion-proper-orthogonal-decomposition-and-recurrent-neural-network-based-approach


Robust Model Predictive Control for Large-Scale Distributed Parameter Systems Under Uncertainty: A Polynomial Chaos Expansion, Proper Orthogonal Decomposition, and Recurrent Neural Network-Based Approach


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複雑な動作の合成を可能にするCAFE-MPCの核メッセージは、高度なモデル予測制御フレームワークである。


cafe-mpc-a-cascaded-fidelity-model-predictive-control-framework-with-whole-body-control


CAFE-MPC: A Cascaded-Fidelity Model Predictive Control Framework with Whole-Body Control
