94 citations · 391 across the 65 of their papers we have counts for
26 papers · 1 filter
A Tutorial on the Non-Asymptotic Theory of System Identification
Ingvar Ziemann, Anastasios Tsiamis, Bruce Lee +3
This tutorial serves as an introduction to recently developed non-asymptotic methods in the theory of -- mainly linear -- system identification. We emphasize tools we deem particul…
Safety Filter Design for Neural Network Systems via Convex Optimization
Shaoru Chen, Kong Yao Chee, Nikolai Matni +2
With the increase in data availability, it has been widely demonstrated that neural networks (NN) can capture complex system dynamics precisely in a data-driven manner. However, th…
Performance-Robustness Tradeoffs in Adversarially Robust Control and Estimation
Bruce D. Lee, Thomas T. C. K. Zhang, Hamed Hassani +1
While methods can introduce robustness against worst-case perturbations, their nominal performance under conventional stochastic disturbances is often drastica…
The Fundamental Limitations of Learning Linear-Quadratic Regulators
Bruce D. Lee, Ingvar Ziemann, Anastasios Tsiamis +2
We present a local minimax lower bound on the excess cost of designing a linear-quadratic controller from offline data. The bound is valid for any offline exploration policy that c…
Learning-enhanced Nonlinear Model Predictive Control using Knowledge-based Neural Ordinary Differential Equations and Deep Ensembles
Kong Yao Chee, M. Ani Hsieh, Nikolai Matni
Nonlinear model predictive control (MPC) is a flexible and increasingly popular framework used to synthesize feedback control strategies that can satisfy both state and control inp…
Distributed Optimal Control of Graph Symmetric Systems via Graph Filters
Fengjun Yang, Fernando Gama, Somayeh Sojoudi +1
Designing distributed optimal controllers subject to communication constraints is a difficult problem unless structural assumptions are imposed on the underlying dynamics and infor…
Robust Model Predictive Control of Time-Delay Systems through System Level Synthesis
Shaoru Chen, Ning-Yuan Li, Victor M. Preciado +1
We present a robust model predictive control method (MPC) for discrete-time linear time-delayed systems with state and control input constraints. The system is subject to both poly…
Statistical Learning Theory for Control: A Finite Sample Perspective
Anastasios Tsiamis, Ingvar Ziemann, Nikolai Matni +1
This tutorial survey provides an overview of recent non-asymptotic advances in statistical learning theory as relevant to control and system identification. While there has been su…
Risk of Stochastic Systems for Temporal Logic Specifications
Lars Lindemann, Lejun Jiang, Nikolai Matni +1
The wide availability of data coupled with the computational advances in artificial intelligence and machine learning promise to enable many future technologies such as autonomous…
Performance-Robustness Tradeoffs in Adversarially Robust Linear-Quadratic Control
Bruce D. Lee, Thomas T. C. K. Zhang, Hamed Hassani +1
While methods can introduce robustness against worst-case perturbations, their nominal performance under conventional stochastic disturbances is often drastica…
Robust Model Predictive Control with Polytopic Model Uncertainty through System Level Synthesis
Shaoru Chen, Victor M. Preciado, Manfred Morari +1
We propose a robust model predictive control (MPC) method for discrete-time linear systems with polytopic model uncertainty and additive disturbances. Optimizing over linear time-v…
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…
Data-driven Distributed and Localized Model Predictive Control
Carmen Amo Alonso, Fengjun Yang, Nikolai Matni
Motivated by large-scale but computationally constrained settings, e.g., the Internet of Things, we present a novel data-driven distributed control algorithm that is synthesized di…
System Level Synthesis-based Robust Model Predictive Control through Convex Inner Approximation
Shaoru Chen, Nikolai Matni, Manfred Morari +1
We propose a robust model predictive control (MPC) method for discrete-time linear time-invariant systems with norm-bounded additive disturbances and model uncertainty. In our meth…
Learning Robust Output Control Barrier Functions from Safe Expert Demonstrations
Lars Lindemann, Alexander Robey, Lejun Jiang +3
This paper addresses learning safe output feedback control laws from partial observations of expert demonstrations. We assume that a model of the system dynamics and a state estima…
Adversarial Tradeoffs in Robust State Estimation
Thomas T. C. K. Zhang, Bruce D. Lee, Hamed Hassani +1
Adversarially robust training has been shown to reduce the susceptibility of learned models to targeted input data perturbations. However, it has also been observed that such adver…
Communication Topology Co-Design in Graph Recurrent Neural Network Based Distributed Control
Fengjun Yang, Nikolai Matni
When designing large-scale distributed controllers, the information-sharing constraints between sub-controllers, as defined by a communication topology interconnecting them, are as…
STL Robustness Risk over Discrete-Time Stochastic Processes
Lars Lindemann, Nikolai Matni, George J. Pappas
We present a framework to interpret signal temporal logic (STL) formulas over discrete-time stochastic processes in terms of the induced risk. Each realization of a stochastic proc…
Learning Robust Hybrid Control Barrier Functions for Uncertain Systems
Alexander Robey, Lars Lindemann, Stephen Tu +1
The need for robust control laws is especially important in safety-critical applications. We propose robust hybrid control barrier functions as a means to synthesize control laws t…
Learning Hybrid Control Barrier Functions from Data
Lars Lindemann, Haimin Hu, Alexander Robey +4
Motivated by the lack of systematic tools to obtain safe control laws for hybrid systems, we propose an optimization-based framework for learning certifiably safe control laws from…
Learning Control Barrier Functions from Expert Demonstrations
Alexander Robey, Haimin Hu, Lars Lindemann +4
Inspired by the success of imitation and inverse reinforcement learning in replicating expert behavior through optimal control, we propose a learning based approach to safe control…
Sample Complexity of Kalman Filtering for Unknown Systems
Anastasios Tsiamis, Nikolai Matni, George J. Pappas
In this paper, we consider the task of designing a Kalman Filter (KF) for an unknown and partially observed autonomous linear time invariant system driven by process and sensor noi…
Learning Sparse Dynamical Systems from a Single Sample Trajectory
Salar Fattahi, Nikolai Matni, Somayeh Sojoudi
This paper addresses the problem of identifying sparse linear time-invariant (LTI) systems from a single sample trajectory generated by the system dynamics. We introduce a Lasso-li…
A System Level Approach to Controller Synthesis
Yuh-Shyang Wang, Nikolai Matni, John C. Doyle
Biological and advanced cyberphysical control systems often have limited, sparse, uncertain, and distributed communication and computing in addition to sensing and actuation. Fortu…