Sep 12, 2023 · It outperforms a state-of-the-art neural estimator by up to 68.1% in force estimation accuracy, utilizing only 1.4% of its network parameters.

The recently proposed neural moving horizon estimation (NeuroMHE), which uses a portable neural network to model the MHE's weightings, has shown promise in ...

The trust-region method offers adaptive step-size updates, faster convergence, and improved robustness to initialization. It has shown efficacy across machine ...

The Trust-Region Neural Moving Horizon Estimation (TR-NeuroMHE) is an adaptive optimal state estimator tuned using the trust-region method.

The Neural Moving Horizon Estimation (NeuroMHE) is an auto-tuning and adaptive optimal estimator. It fuses a neural network with an MHE to realize accurate ...

Sep 11, 2024 · The neural moving horizon estimator (NMHE) is a relatively new and powerful state estimator that combines the strengths of neural networks ...

A neural moving horizon estimator that can automatically tune its key parameters modeled by a neural network and adapt to different flight scenarios and ...

Dec 9, 2024 · Notably, NeuroMHE outperforms a state-of-the-art neural network-based estimator, reducing force estimation errors by up to 76.7%, while using a ...

Co-authors ; Neural Moving Horizon Estimation for Robust Flight Control. B Wang, Z Ma, S Lai, L Zhao. IEEE Transactions on Robotics 40, 639-659, 2024. 19, 2024.

Nov 7, 2024 · https://dblp.org/rec/conf/icra/WangC024 · Bingheng Wang, Xuyang Chen, Lin Zhao: Trust-Region Neural Moving Horizon Estimation for Robots. ICRA ...