Search Results for author: Manfred Morari

Found 23 papers, 13 papers with code

Combined Left and Right Temporal Robustness for Control under STL Specifications

no code implementations8 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.

Certified Invertibility in Neural Networks via Mixed-Integer Programming

no code implementations27 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.

Network Pruning

Learning to Control Linear Systems can be Hard

no code implementations27 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.

Adaptive Stochastic MPC under Unknown Noise Distribution

no code implementations3 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.


Temporal Robustness of Temporal Logic Specifications: Analysis and Control Design

no code implementations29 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.

Robust Model Predictive Control with Polytopic Model Uncertainty through System Level Synthesis

2 code implementations21 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.

Model Predictive Control

System Level Synthesis-based Robust Model Predictive Control through Convex Inner Approximation

1 code implementation10 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.

Model Predictive Control

Learning Region of Attraction for Nonlinear Systems

no code implementations2 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.

Time-Robust Control for STL Specifications

1 code implementation6 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.

Learning Lyapunov Functions for Hybrid Systems

no code implementations22 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

Learning to Track Dynamic Targets in Partially Known Environments

1 code implementation17 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.

Navigate Reinforcement Learning (RL)

Learning solutions to hybrid control problems using Benders cuts

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.

Model Predictive Control

BayesRace: Learning to race autonomously using prior experience

1 code implementation10 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.


Reach-SDP: Reachability Analysis of Closed-Loop Systems with Neural Network Controllers via Semidefinite Programming

1 code implementation16 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.

Computing the racing line using Bayesian optimization

2 code implementations12 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.


NeurOpt: Neural network based optimization for building energy management and climate control

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.

energy management Management +1

Learning Q-network for Active Information Acquisition

2 code implementations23 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.

reinforcement-learning Reinforcement Learning (RL)

Large Scale Model Predictive Control with Neural Networks and Primal Active Sets

no code implementations23 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.

Model Predictive Control

Probabilistic Verification and Reachability Analysis of Neural Networks via Semidefinite Programming

1 code implementation9 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.

Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks

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).

Safety Verification and Robustness Analysis of Neural Networks via Quadratic Constraints and Semidefinite Programming

4 code implementations4 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.

Computational Efficiency

Cloud-based MPC with Encrypted Data

1 code implementation27 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

Optimization-Based Autonomous Racing of 1:43 Scale RC Cars

5 code implementations20 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

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