13 citations · 43 across the 15 of their papers we have counts for
18 papers
TinySDP: Real Time Semidefinite Optimization for Certifiable and Agile Edge Robotics
Ishaan Mahajan, Jon Arrizabalaga, Andrea Grillo +4
Semidefinite programming (SDP) provides a principled framework for convex relaxations of nonconvex geometric constraints in motion planning, yet existing solvers are too computatio…
Sample-Efficient Linear Representation Learning from Non-IID Non-Isotropic Data
Thomas T. C. K. Zhang, Leonardo F. Toso, James Anderson +1
A powerful concept behind much of the recent progress in machine learning is the extraction of common features across data from heterogeneous sources or tasks. Intuitively, using a…
Distributed and Localized Model Predictive Control. Part II: Theoretical Guarantees
Carmen Amo Alonso, Jing Shuang Li, Nikolai Matni +1
Engineered cyberphysical systems are growing increasingly large and complex. These systems require scalable controllers that robustly satisfy state and input constraints in the pre…
Distributed and Localized Model Predictive Control. Part I: Synthesis and Implementation
Carmen Amo Alonso, Jing Shuang Li, James Anderson +1
The increasing presence of large-scale distributed systems highlights the need for scalable control strategies where only local communication is required. Moreover, in safety-criti…
Robust Distributed and Localized Model Predictive Control
Carmen Amo Alonso, Jing Shuang Li, Nikolai Matni +1
We present a robust Distributed and Localized Model Predictive Control (rDLMPC) framework for large-scale structured linear systems. The proposed algorithm uses the System Level Sy…
Synthesis to Deployment: Cyber-Physical Control Architectures
Shih-Hao Tseng, James Anderson
We consider the problem of how to deploy a controller to a (networked) cyber-physical system (CPS). Controlling a CPS is an involved task, and synthesizing a controller to respect…
Localized and Distributed H2 State Feedback Control
Jing Yu, Yuh-Shyang Wang, James Anderson
Distributed linear control design is crucial for large-scale cyber-physical systems. It is generally desirable to both impose information exchange (communication) constraints on th…
Explicit Distributed and Localized Model Predictive Control via System Level Synthesis
Carmen Amo Alonso, Nikolai Matni, James Anderson
An explicit Model Predictive Control algorithm for large-scale structured linear systems is presented. We base our results on Distributed and Localized Model Predictive Control (DL…
Worst-Case Sensitivity of DC Optimal Power Flow Problems
James Anderson, Fengyu Zhou, Steven H. Low
In this paper we consider the problem of analyzing the effect a change in the load vector can have on the optimal power generation in a DC power flow model. The methodology is base…
Safety-Critical Control Synthesis for network systems with Control Barrier Functions and Assume-Guarantee Contracts
Yuxiao Chen, James Anderson, Karan Kalsi +2
This paper aims at the safety-critical control synthesis of network systems such that the satisfaction of the safety constraints can be guaranteed. To handle the large state dimens…
Deployment Architectures for Cyber-Physical Control Systems
Shih-Hao Tseng, James Anderson
We consider the problem of how to deploy a controller to a (networked) cyber-physical system (CPS). Controlling a CPS is an involved task, and synthesizing a controller to respect…
The Optimal Power Flow Operator: Theory and Computation
Fengyu Zhou, James Anderson, Steven H. Low
Optimal power flow problems (OPFs) are mathematical programs used to determine how to distribute power over networks subject to network operation constraints and the physics of pow…
System Level Synthesis
James Anderson, John C. Doyle, Steven Low +1
This article surveys the System Level Synthesis framework, which presents a novel perspective on constrained robust and optimal controller synthesis for linear systems. We show how…
Sparsity Preserving Discretization With Error Bounds
James Anderson, Nikolai Matni, Yuxiao Chen
Typically when designing distributed controllers it is assumed that the state-space model of the plant consists of sparse matrices. However, in the discrete-time setting, if one be…
Differential Privacy of Aggregated DC Optimal Power Flow Data
Fengyu Zhou, James Anderson, Steven H. Low
We consider the problem of privately releasing aggregated network statistics obtained from solving a DC optimal power flow (OPF) problem. It is shown that the mechanism that determ…
System Level Synthesis with State and Input Constraints
Yuxiao Chen, James Anderson
This paper addresses the problem of designing distributed controllers with state and input constraints in the System Level Synthesis (SLS) framework. Using robust optimization, we…
Compositional Set Invariance in Network Systems with Assume-Guarantee Contracts
Yuxiao Chen, James Anderson, Karan Kalsi +2
This paper presents an assume-guarantee reasoning approach to the computation of robust invariant sets for network systems. Parameterized signal temporal logic (pSTL) is used to fo…
Distance to the Nearest Stable Metzler Matrix
James Anderson
This paper considers the non-convex problem of finding the nearest Metzler matrix to a given possibly unstable matrix. Linear systems whose state vector evolves according to a Metz…