5 citations · 5 across the 11 of their papers we have counts for
13 papers
System Level Synthesis for Fast Chance-Constrained Post-Fault Ascent Replanning under Persistent Propulsion Uncertainty
Dai Shen, Chen Yang, Lin Cheng +3
A nonfatal propulsion fault can invalidate the nominal ascent trajectory while leaving sufficient vehicle capability to reach the original orbit or a degraded target. Deterministic…
Condensed PIPG Sequential Convex Optimization for Reusable-Rocket Powered Landing with Strong Aerodynamics
Wenbo Li, Linwei Li, Ziqi Xu +1
Reusable-rocket powered landing under strong aerodynamics couples variable mass, free final time, and bounded aerodynamic controls through nonlinear velocity-frame dynamics. This p…
Dual-Thrust Switching Analytical Guidance Algorithm for Powered Landing with Attitude Smoothness Optimization
Wenbo Li, Dai Shen, Shengping Gong
Traditional numerical guidance methods for powered landing of reusable rockets are typically constrained by high computational complexity and inadequate real-time performance. More…
Submillisecond Sequential Convex Optimization for Powered Landing via Dynamics Condensation and xPIPG
Wenbo Li, Ziqi Xu, Dai Shen +1
Powered landing with variable mass, free final time, and quadratic aerodynamic drag requires the repeated solution of local convex subproblems, whose main online cost lies in the l…
Microsecond-Class Powered-Descent Optimization via Exact Condensation and Strong Convex Regularization
Wenbo Li, Ziqi Xu, Dai Shen +1
Fuel-dominant powered descent can be written as a convex program, but the usual full-state epigraph formulation still carries many state variables, fuel epigraph variables, and dyn…
Parameter Optimization in Trajectory Planning via Differentiable Convex Programming
Ziqi Xu, Lin Cheng, Di Wu +1
Sequential convex programming has been established as an effective framework for solving nonconvex trajectory planning problems. However, its performance is highly sensitive to pro…
Deep Neural Network-Based High-Precision Identification of Weak Stability Boundary Structures
Shuyue Fu, Ziqi Xu, Di Wu +1
Weak stability boundary structures have been widely applied to the analysis on ballistic capture and the construction of low-energy transfers. The first step of this application is…
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…
Learning-Based Stable Optimal Control for Infinite-Time Nonlinear Regulation Problems
Han Wang, Di Wu, Lin Cheng +2
Infinite-time nonlinear optimal regulation control is widely utilized in aerospace engineering as a systematic method for synthesizing stable controllers. However, conventional met…
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…
Stability Enhancement in Reinforcement Learning via Adaptive Control Lyapunov Function
Donghe Chen, Han Wang, Lin Cheng +1
Reinforcement Learning (RL) has shown promise in control tasks but faces significant challenges in real-world applications, primarily due to the absence of safety guarantees during…
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…