55 citations · 197 across the 32 of their papers we have counts for
33 papers
Neighboring Extremal Optimal Control Theory for Parameter-Dependent Closed-loop Laws
Ayush Rai, Shaoshuai Mou, Brian D. O. Anderson
This study introduces an approach to obtain a neighboring extremal optimal control (NEOC) solution for a closed-loop optimal control problem, applicable to a wide array of nonlinea…
Safe Region Multi-Agent Formation Control With Velocity Tracking
Ayush Rai, Shaoshuai Mou
This paper provides a solution to the problem of safe region formation control with reference velocity tracking for a second-order multi-agent system without velocity measurements.…
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…
A Data-Driven Approach for Inverse Optimal Control
Zihao Liang, Wenjian Hao, Shaoshuai Mou
This paper proposes a data-driven, iterative approach for inverse optimal control (IOC), which aims to learn the objective function of a nonlinear optimal control system given its…
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…
Deep Koopman Learning of Nonlinear Time-Varying Systems
Wenjian Hao, Bowen Huang, Wei Pan +2
This paper presents a data-driven approach to approximate the dynamics of a nonlinear time-varying system (NTVS) by a linear time-varying system (LTVS), which is resulted from the…
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…
Resilience for Distributed Consensus with Constraints
Xuan Wang, Shaoshuai Mou, Shreyas Sundaram
This paper proposes a new approach that enables multi-agent systems to achieve resilient \textit{constrained} consensus in the presence of Byzantine attacks, in contrast to existin…
Consensus-based Distributed Optimization Enhanced by Integral Feedback
Xuan Wang, Shaoshuai Mou, Brian. D. O. Anderson
Inspired and underpinned by the idea of integral feedback, a distributed constant gain algorithm is proposed for multi-agent networks to solve convex optimization problems with loc…
Safe Pontryagin Differentiable Programming
Wanxin Jin, Shaoshuai Mou, George J. Pappas
We propose a Safe Pontryagin Differentiable Programming (Safe PDP) methodology, which establishes a theoretical and algorithmic framework to solve a broad class of safety-critical…
Towards Resilience for Multi-Agent -Learning
Yijing Xie, Shaoshuai Mou, Shreyas Sundaram
This paper considers the multi-agent reinforcement learning (MARL) problem for a networked (peer-to-peer) system in the presence of Byzantine agents. We build on an existing distri…
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…
Learning Objective Functions Incrementally by Inverse Optimal Control
Zihao Liang, Wanxin Jin, Shaoshuai Mou
This paper proposes an inverse optimal control method which enables a robot to incrementally learn a control objective function from a collection of trajectory segments. By saying…
Learning from Sparse Demonstrations
Wanxin Jin, Todd D. Murphey, Dana Kulić +2
This paper develops the method of Continuous Pontryagin Differentiable Programming (Continuous PDP), which enables a robot to learn an objective function from a few sparsely demons…
Neural Certificates for Safe Control Policies
Wanxin Jin, Zhaoran Wang, Zhuoran Yang +1
This paper develops an approach to learn a policy of a dynamical system that is guaranteed to be both provably safe and goal-reaching. Here, the safety means that a policy must not…
Distributed traffic control for a large-scale urban network
Viet Hoang Pham, Kazunori Sakurama, Shaoshuai Mou +1
Motivated by the fact that intelligent traffic control systems have become inevitable demand to cope with the risk of traffic congestion in urban areas, this paper develops a distr…
Resilient Cyberphysical Systems and their Application Drivers: A Technology Roadmap
Somali Chaterji, Parinaz Naghizadeh, Muhammad Ashraful Alam +14
Cyberphysical systems (CPS) are ubiquitous in our personal and professional lives, and they promise to dramatically improve micro-communities (e.g., urban farms, hospitals), macro-…
Pontryagin Differentiable Programming: An End-to-End Learning and Control Framework
Wanxin Jin, Zhaoran Wang, Zhuoran Yang +1
This paper develops a Pontryagin Differentiable Programming (PDP) methodology, which establishes a unified framework to solve a broad class of learning and control tasks. The PDP d…
Grand Challenges in Resilience: Autonomous System Resilience through Design and Runtime Measures
Saurabh Bagchi, Vaneet Aggarwal, Somali Chaterji +13
A set of about 80 researchers, practitioners, and federal agency program managers participated in the NSF-sponsored Grand Challenges in Resilience Workshop held on Purdue campus on…
On the stability and applications of distance-based flexible formations
Hector Garcia de Marina, Zhiyong Sun, Shaoshuai Mou
This paper investigates the stability of distance-based \textit{flexible} undirected formations in the plane. Without rigidity, there exists a set of connected shapes for given dis…
A Resilient Convex Combination for consensus-based distributed algorithms
Xuan Wang, Shaoshuai Mou, Shreyas Sundaram
Consider a set of vectors in , partitioned into two classes: normal vectors and malicious vectors. The number of malicious vectors is bounded but their identities are…