no code implementations • 8 Jun 2023 • Alëna Rodionova, Lars Lindemann, Manfred Morari, George J. Pappas

Many modern autonomous systems, particularly multi-agent systems, are time-critical and need to be robust against timing uncertainties.

no code implementations • 27 Jan 2023 • Tianqi Cui, Thomas Bertalan, George J. Pappas, Manfred Morari, Ioannis G. Kevrekidis, Mahyar Fazlyab

Neural networks are known to be vulnerable to adversarial attacks, which are small, imperceptible perturbations that can significantly alter the network's output.

no code implementations • 27 May 2022 • Anastasios Tsiamis, Ingvar Ziemann, Manfred Morari, Nikolai Matni, George J. Pappas

In this paper, we study the statistical difficulty of learning to control linear systems.

no code implementations • 3 Apr 2022 • Charis Stamouli, Anastasios Tsiamis, Manfred Morari, George J. Pappas

Then, we employ this benchmark controller to derive a novel robustly stable adaptive SMPC scheme that learns the necessary noise statistics online, while guaranteeing time-uniform satisfaction of the unknown reformulated state constraints with high probability.

no code implementations • 29 Mar 2022 • Alëna Rodionova, Lars Lindemann, Manfred Morari, George J. Pappas

We study the temporal robustness of temporal logic specifications and show how to design temporally robust control laws for time-critical control systems.

2 code implementations • 21 Mar 2022 • Shaoru Chen, Victor M. Preciado, Manfred Morari, Nikolai Matni

However, it is challenging to design LTV state feedback controllers in the face of model uncertainty whose effects are difficult to bound.

1 code implementation • 10 Nov 2021 • Shaoru Chen, Nikolai Matni, Manfred Morari, Victor M. Preciado

We propose a robust model predictive control (MPC) method for discrete-time linear time-invariant systems with norm-bounded additive disturbances and model uncertainty.

no code implementations • 2 Oct 2021 • Shaoru Chen, Mahyar Fazlyab, Manfred Morari, George J. Pappas, Victor M. Preciado

Estimating the region of attraction (ROA) of general nonlinear autonomous systems remains a challenging problem and requires a case-by-case analysis.

1 code implementation • 6 Apr 2021 • Alena Rodionova, Lars Lindemann, Manfred Morari, George J. Pappas

We present a robust control framework for time-critical systems in which satisfying real-time constraints robustly is of utmost importance for the safety of the system.

no code implementations • 22 Dec 2020 • Shaoru Chen, Mahyar Fazlyab, Manfred Morari, George J. Pappas, Victor M. Preciado

By designing the learner and the verifier according to the analytic center cutting-plane method from convex optimization, we show that when the set of Lyapunov functions is full-dimensional in the parameter space, our method finds a Lyapunov function in a finite number of steps.

Optimization and Control

1 code implementation • 17 Jun 2020 • Heejin Jeong, Hamed Hassani, Manfred Morari, Daniel D. Lee, George J. Pappas

In particular, we introduce Active Tracking Target Network (ATTN), a unified RL policy that is capable of solving major sub-tasks of active target tracking -- in-sight tracking, navigation, and exploration.

no code implementations • L4DC 2020 • Sandeep Menta, Joseph Warrington, John Lygeros, Manfred Morari

Hybrid control problems are complicated by the need to make a suitable sequence of discrete decisions related to future modes of operation of the system.

1 code implementation • 10 May 2020 • Achin Jain, Matthew O'Kelly, Pratik Chaudhari, Manfred Morari

Autonomous race cars require perception, estimation, planning, and control modules which work together asynchronously while driving at the limit of a vehicle's handling capability.

1 code implementation • 16 Apr 2020 • Haimin Hu, Mahyar Fazlyab, Manfred Morari, George J. Pappas

There has been an increasing interest in using neural networks in closed-loop control systems to improve performance and reduce computational costs for on-line implementation.

2 code implementations • 12 Feb 2020 • Achin Jain, Manfred Morari

A good racing strategy and in particular the racing line is decisive to winning races in Formula 1, MotoGP, and other forms of motor racing.

Robotics

no code implementations • L4DC 2020 • Achin Jain, Francesco Smarra, Enrico Reticcioli, Alessandro D'Innocenzo, Manfred Morari

Model predictive control (MPC) can provide significant energy cost savings in building operations in the form of energy-efficient control with better occupant comfort, lower peak demand charges, and risk-free participation in demand response.

2 code implementations • 23 Oct 2019 • Heejin Jeong, Brent Schlotfeldt, Hamed Hassani, Manfred Morari, Daniel D. Lee, George J. Pappas

In this paper, we propose a novel Reinforcement Learning approach for solving the Active Information Acquisition problem, which requires an agent to choose a sequence of actions in order to acquire information about a process of interest using on-board sensors.

no code implementations • 23 Oct 2019 • Steven W. Chen, Tianyu Wang, Nikolay Atanasov, Vijay Kumar, Manfred Morari

The approach combines an offline-trained fully-connected neural network with an online primal active set solver.

1 code implementation • 9 Oct 2019 • Mahyar Fazlyab, Manfred Morari, George J. Pappas

In this context, we discuss two relevant problems: (i) probabilistic safety verification, in which the goal is to find an upper bound on the probability of violating a safety specification; and (ii) confidence ellipsoid estimation, in which given a confidence ellipsoid for the input of the neural network, our goal is to compute a confidence ellipsoid for the output.

1 code implementation • NeurIPS 2019 • Mahyar Fazlyab, Alexander Robey, Hamed Hassani, Manfred Morari, George J. Pappas

The resulting SDP can be adapted to increase either the estimation accuracy (by capturing the interaction between activation functions of different layers) or scalability (by decomposition and parallel implementation).

4 code implementations • 4 Mar 2019 • Mahyar Fazlyab, Manfred Morari, George J. Pappas

Certifying the safety or robustness of neural networks against input uncertainties and adversarial attacks is an emerging challenge in the area of safe machine learning and control.

1 code implementation • 27 Mar 2018 • Andreea B. Alexandru, Manfred Morari, George J. Pappas

We propose protocols for two cloud-MPC architectures motivated by the current developments in the Internet of Things: a client-server architecture and a two-server architecture.

Optimization and Control Cryptography and Security Systems and Control

5 code implementations • 20 Nov 2017 • Alexander Liniger, Alexander Domahidi, Manfred Morari

This paper describes autonomous racing of RC race cars based on mathematical optimization.

Optimization and Control Robotics Systems and Control

Cannot find the paper you are looking for? You can
Submit a new open access paper.

Contact us on:
hello@paperswithcode.com
.
Papers With Code is a free resource with all data licensed under CC-BY-SA.