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eess.SY2022
Robust Bayesian Inference for Moving Horizon Estimation
Wenhan Cao, Chang Liu, Zhiqian Lan +3
The accuracy of moving horizon estimation (MHE) suffers significantly in the presence of measurement outliers. Existing methods address this issue by treating measurements leading…
eess.SY2022
Primal-dual Estimator Learning: an Offline Constrained Moving Horizon Estimation Method with Feasibility and Near-optimality Guarantees
Wenhan Cao, Jingliang Duan, Shengbo Eben Li +3
This paper proposes a primal-dual framework to learn a stable estimator for linear constrained estimation problems leveraging the moving horizon approach. To avoid the online compu…
eess.SY2020
Mixed Reinforcement Learning with Additive Stochastic Uncertainty
Yao Mu, Shengbo Eben Li, Chang Liu +4
Reinforcement learning (RL) methods often rely on massive exploration data to search optimal policies, and suffer from poor sampling efficiency. This paper presents a mixed reinfor…