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29 thg 8, 2023 · We present a method for end-to-end reinforcement learning of dynamic surrogate models for optimal performance in (e)NMPC applications, resulting in predictive controllers that …
16 thg 8, 2022 · This article presents a data-driven control strategy for nonlinear dynamical systems, enabling the construction of a Koopman-based linear system associated with …
23 thg 1, 2023 · Abstract—Control of machine learning models has emerged as an important paradigm for a broad range of robotics applications. In this paper, we present a sampling …
17 thg 5, 2022 · 文中提出一种完全基于采样的NMPC实现方法，在神经网络动态控制中具有广阔的应用前景。 还应注意的是，这种方法可以很好地接受粒子滤波/平滑的其他实现方式，取决于 …
13 thg 5, 2025 · In this work, we present a Multi-Task Learning (MTL) framework in which expert NMPC demonstrations are used to train a single neural network to predict actions for multiple …
3 thg 4, 2024 · Abstract— This paper presents an end-to-end learning ap-proach to developing a Nonlinear Model Predictive Control (NMPC) policy, which does not require an explicit first …
1 thg 11, 2024 · This paper presents an end-to-end learning approach to developing a Nonlinear Model Predictive Control (NMPC) policy, which does not require an explicit first-principles …
1 thg 12, 2022 · To facilitate the real-time implementation of nonlinear model predictive control (NMPC), this paper proposes a deep learning-based NMPC scheme, in which the NMPC law is …
To address this issue, this paper proposes a novel end-to-end training framework, called the neural network optimizer (NN Optimizer), which significantly reduces the computational burden …
9 thg 5, 2022 · Control of machine learning models has emerged as an important paradigm for a broad range of robotics applications. In this paper, we present a sampling-based nonlinear …
15 thg 3, 2022 · With this work, we present Real-time Neural MPC, a framework to efficiently integrate large, complex neural network architectures as dynamics models within a model …
3 thg 8, 2023 · We present a method for end-to-end reinforcement learning of Koopman surrogate models for optimal performance as part of (e)NMPC. We apply our method to two applications …
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