3 citations · 6 across the 20 of their papers we have counts for
22 papers
Stability Transfer for Autonomous Systems via Higher-Order Taylor-Truncated Lyapunov Certificates
Jose D. Hoyos, Zehui Lu, Shaoshuai Mou
We develop analytical sufficient conditions for certifying the local asymptotic stability of nonlinear autonomous systems based on Lyapunov functions derived from their finite-orde…
Amplitude analysis for charmed meson decays at BESIII
Han Zhang, Zhenxuan Li, Chunyi Guan +9
Amplitude analysis bridges the gap between experimental measurements of multibody charmed-meson decays and theoretical predictions of intermediate two-body processes. This work pre…
Neural Operator Learning for Collision-Aware Trajectory Planning of Spacecraft Swarms
Sidhdharth D. Sikka, Suyi Gao, Zehui Lu +2
Satellite constellations require orbital transfers that are both fuel efficient and collision avoidant. Yet, the computational cost of optimization methods traditionally used to pl…
Online Intention Prediction via Control-Informed Learning
Tianyu Zhou, Zihao Liang, Zehui Lu +1
This paper presents an online intention prediction framework for estimating the goal state of autonomous systems in real time, even when intention is time-varying, and system dynam…
Accelerating Sampling-Based Control via Learned Linear Koopman Dynamics
Wenjian Hao, Yuxuan Fang, Zehui Lu +1
This paper presents an efficient model predictive path integral (MPPI) control framework for systems with complex nonlinear dynamics. To improve the computational efficiency of cla…
Safe Online Control-Informed Learning
Tianyu Zhou, Zihao Liang, Zehui Lu +1
This paper proposes a Safe Online Control-Informed Learning framework for safety-critical autonomous systems. The framework unifies optimal control, parameter estimation, and safet…
A Control-Barrier-Function-Based Algorithm for Policy Adaptation in Reinforcement Learning
Wenjian Hao, Zehui Lu, Nicolas Miguel +1
This paper considers the problem of adapting a predesigned policy, represented by a parameterized function class, from a solution that minimizes a given original cost function to a…
Trajectory Prediction via Bayesian Intention Inference under Unknown Goals and Kinematics
Shunan Yin, Zehui Lu, Shaoshuai Mou
This work introduces an adaptive Bayesian algorithm for real-time trajectory prediction via intention inference, where a target's intentions and motion characteristics are unknown…
Reward-Based Collision-Free Algorithm for Trajectory Planning of Autonomous Robots
Jose D. Hoyos, Tianyu Zhou, Zehui Lu +1
This paper proposes a novel mission planning algorithm for autonomous robots that selects an optimal waypoint sequence from a predefined set to maximize total reward while satisfyi…
Distributed Koopman Learning using Partial Trajectories for Control
Wenjian Hao, Zehui Lu, Devesh Upadhyay +1
This paper proposes a distributed data-driven framework for dynamics learning, termed distributed deep Koopman learning using partial trajectories (DDKL-PT). In this framework, eac…
Online Control-Informed Learning
Zihao Liang, Tianyu Zhou, Zehui Lu +1
This paper proposes an Online Control-Informed Learning (OCIL) framework, which employs the well-established optimal control and state estimation techniques in the field of control…
Distributed Optimization under Edge Agreement with Application in Battery Network Management
Zehui Lu, Shaoshuai Mou
This paper investigates a distributed optimization problem under edge agreements, where each agent in the network is also subject to local convex constraints. Generalized from the…
Potential Field as Scene Affordance for Behavior Change-Based Visual Risk Object Identification
Pang-Yuan Pao, Shu-Wei Lu, Ze-Yan Lu +1
We study behavior change-based visual risk object identification (Visual-ROI), a critical framework designed to detect potential hazards for intelligent driving systems. Existing m…
A Differentiable Dynamic Modeling Approach to Integrated Motion Planning and Actuator Physical Design for Mobile Manipulators
Zehui Lu, Yebin Wang
This paper investigates the differentiable dynamic modeling of mobile manipulators to facilitate efficient motion planning and physical design of actuators, where the actuator desi…
Integrated Optimal Fast Charging and Active Thermal Management of Lithium-Ion Batteries in Extreme Ambient Temperatures
Zehui Lu, Hao Tu, Huazhen Fang +2
This paper presents an integrated control strategy for optimal fast charging and active thermal management of Lithium-ion batteries in extreme ambient temperatures, striking a bala…
Distributed Optimization under Edge Agreements: A Continuous-Time Algorithm
Zehui Lu, Shaoshuai Mou
Generalized from the concept of consensus, this paper considers a group of edge agreements, i.e. constraints defined for neighboring agents, in which each pair of neighboring agent…
Adaptive Policy Learning to Additional Tasks
Wenjian Hao, Zehui Lu, Zihao Liang +2
This paper develops a policy learning method for tuning a pre-trained policy to adapt to additional tasks without altering the original task. A method named Adaptive Policy Gradien…
Optimal Control of Nonlinear Systems with Unknown Dynamics
Wenjian Hao, Paulo C. Heredia, Shaoshuai Mou
This paper presents a data-driven method to find a closed-loop optimal controller, which minimizes a specified infinite-horizon cost function for systems with unknown dynamics. Sup…
DrMaMP: Distributed Real-time Multi-agent Mission Planning in Cluttered Environment
Zehui Lu, Tianyu Zhou, Shaoshuai Mou
Solving a collision-aware multi-agent mission planning (task allocation and path finding) problem is challenging due to the requirement of real-time computational performance, scal…
Variable Sampling MPC via Differentiable Time-Warping Function
Zehui Lu, Shaoshuai Mou
Designing control inputs for a system that involves dynamical responses in multiple timescales is nontrivial. This paper proposes a parameterized time-warping function to enable a…
Cooperative Tuning of Multi-Agent Optimal Control Systems
Zehui Lu, Wanxin Jin, Shaoshuai Mou +1
This paper investigates the problem of cooperative tuning of multi-agent optimal control systems, where a network of agents (i.e. multiple coupled optimal control systems) adjusts…
Learning from Human Directional Corrections
Wanxin Jin, Todd D. Murphey, Zehui Lu +1
This paper proposes a novel approach that enables a robot to learn an objective function incrementally from human directional corrections. Existing methods learn from human magnitu…