42 citations · 49 across the 8 of their papers we have counts for
7 papers · 1 filter
Neural-iLQR: A Learning-Aided Shooting Method for Trajectory Optimization
Zilong Cheng, Yulin Li, Kai Chen +2
Iterative linear quadratic regulator (iLQR) has gained wide popularity in addressing trajectory optimization problems with nonlinear system models. However, as a model-based shooti…
Data-Driven Predictive Control Towards Multi-Agent Motion Planning With Non-Parametric Closed-Loop Behavior Learning
Jun Ma, Zilong Cheng, Wenxin Wang +3
In many specific scenarios, accurate and effective system identification is a commonly encountered challenge in the model predictive control (MPC) formulation. As a consequence, th…
Improved Hierarchical ADMM for Nonconvex Cooperative Distributed Model Predictive Control
Xiaoxue Zhang, Jun Ma, Zilong Cheng +3
Distributed optimization is often widely attempted and innovated as an attractive and preferred methodology to solve large-scale problems effectively in a localized and coordinated…
Alternating Direction Method of Multipliers for Constrained Iterative LQR in Autonomous Driving
Jun Ma, Zilong Cheng, Xiaoxue Zhang +2
In the context of autonomous driving, the iterative linear quadratic regulator (iLQR) is known to be an efficient approach to deal with the nonlinear vehicle model in motion planni…
Trajectory Generation by Chance Constrained Nonlinear MPC with Probabilistic Prediction
Xiaoxue Zhang, Jun Ma, Zilong Cheng +3
Continued great efforts have been dedicated towards high-quality trajectory generation based on optimization methods, however, most of them do not suitably and effectively consider…
On Symmetric Gauss-Seidel ADMM Algorithm for Guaranteed Cost Control with Convex Parameterization
Jun Ma, Zilong Cheng, Xiaoxue Zhang +2
This paper involves the innovative development of a symmetric Gauss-Seidel ADMM algorithm to solve the H-infinity guaranteed cost control problem. In the presence of parametric unc…