6 papers
Sparse Kalman Identification for Partially Observable Systems via Adaptive Bayesian Learning
Jilan Mei, Tengjie Zheng, Lin Cheng +2
Sparse dynamics identification is an essential tool for discovering interpretable physical models and enabling efficient control in engineering systems. However, existing methods r…
Recursive Inference for Heterogeneous Multi-Output GP State-Space Models with Arbitrary Moment Matching
Tengjie Zheng, Jilan Mei, Di Wu +2
Accurate learning of system dynamics is becoming increasingly crucial for advanced control and decision-making in engineering. However, real-world systems often exhibit multiple ch…
Confidence-Aware Learning Optimal Terminal Guidance via Gaussian Process Regression
Han Wang, Donghe Chen, Tengjie Zheng +2
Modern aerospace guidance systems demand rigorous constraint satisfaction, optimal performance, and computational efficiency. Traditional analytical methods struggle to simultaneou…
Error Distribution Smoothing:Advancing Low-Dimensional Imbalanced Regression
Donghe Chen, Jiaxuan Yue, Tengjie Zheng +2
In real-world regression tasks, datasets frequently exhibit imbalanced distributions, characterized by a scarcity of data in high-complexity regions and an abundance in low-complex…
Adviser-Actor-Critic: Eliminating Steady-State Error in Reinforcement Learning Control
Donghe Chen, Yubin Peng, Tengjie Zheng +3
High-precision control tasks present substantial challenges for reinforcement learning (RL) algorithms, frequently resulting in suboptimal performance attributed to network approxi…
Recursive Gaussian Process State Space Model
Tengjie Zheng, Haipeng Chen, Lin Cheng +2
Learning dynamical models from data is not only fundamental but also holds great promise for advancing principle discovery, time-series prediction, and controller design. Among var…