Publications (50)
The Fragility of Learning LQG Controllers
Bruce D. Lee, Anastasios Tsiamis, Nikolai Matni +2
Learning methods are increasingly used to synthesize controllers from data, yet existing sample-complexity characterizations for continuous control are sharp only in the fully obse…
BayesRace: Learning to race autonomously using prior experience
Achin Jain, Matthew O'Kelly, Pratik Chaudhari +1
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.…
Computing the racing line using Bayesian optimization
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. The racing line defines the path follow…
Combined Left and Right Temporal Robustness for Control under STL Specifications
Alëna Rodionova, Lars Lindemann, Manfred Morari +1
Many modern autonomous systems, particularly multi-agent systems, are time-critical and need to be robust against timing uncertainties. Previous works have studied left and right t…
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…
Optimization-Based Autonomous Racing of 1:43 Scale RC Cars
Alexander Liniger, Alexander Domahidi, Manfred Morari
This paper describes autonomous racing of RC race cars based on mathematical optimization. Using a dynamical model of the vehicle, control inputs are computed by receding horizon b…
Randomized Solutions to Convex Programs with Multiple Chance Constraints
Georg Schildbach, Lorenzo Fagiano, Manfred Morari
The scenario-based optimization approach (`scenario approach') provides an intuitive way of approximating the solution to chance-constrained optimization programs, based on finding…
Learning to Control Linear Systems can be Hard
Anastasios Tsiamis, Ingvar Ziemann, Manfred Morari +2
In this paper, we study the statistical difficulty of learning to control linear systems. We focus on two standard benchmarks, the sample complexity of stabilization, and the regre…
Learning Q-network for Active Information Acquisition
Heejin Jeong, Brent Schlotfeldt, Hamed Hassani +3
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…
Efficient evaluation of mp-MIQP solutions using lifting
Alexander Fuchs, Daniel Axehill, Manfred Morari
This paper presents an efficient approach for the evaluation of multi-parametric mixed integer quadratic programming (mp-MIQP) solutions, occurring for instance in control problems…
A Data-driven Predictive Control Architecture for Train Thermal Energy Management
Ahmed Aboudonia, Johannes Estermann, Keith Moffat +2
We aim to improve the energy efficiency of train climate control architectures, with a focus on a specific class of regional trains operating throughout Switzerland, especially in…
Cloud-based MPC with Encrypted Data
Andreea B. Alexandru, Manfred Morari, George J. Pappas
This paper explores the privacy of cloud outsourced Model Predictive Control (MPC) for a linear system with input constraints. In our cloud-based architecture, a client sends her p…
Reach-SDP: Reachability Analysis of Closed-Loop Systems with Neural Network Controllers via Semidefinite Programming
Haimin Hu, Mahyar Fazlyab, Manfred Morari +1
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. Howe…
Policy-based reserves for power systems
Joseph Warrington, Paul Goulart, Sebastien Mariethoz +1
This paper introduces the concept of affine reserve policies for accommodating large, fluctuating renewable infeeds in power systems. The approach uses robust optimization with rec…
Layered Multirate Control of Constrained Linear Systems
Charis Stamouli, Anastasios Tsiamis, Manfred Morari +1
Layered control architectures have been a standard paradigm for efficiently managing complex constrained systems. A typical architecture consists of: i) a higher layer, where a low…
A note on the control of processes exhibiting input multiplicity
Robert J. Lovelett, Yorgos M. Psarellis, Ioannis G. Kevrekidis +1
Steady state multiplicity can occur in nonlinear systems, and this presents challenges to feedback control. Input multiplicity arises when the same steady state output values can b…
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…
NeurOpt: Neural network based optimization for building energy management and climate control
Achin Jain, Francesco Smarra, Enrico Reticcioli +2
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 d…
Control of an Architectural Cable Net Geometry
Yvonne R. Stürz, Manfred Morari, Roy S. Smith
Doubly curved thin concrete shells are very efficient building structures, suitable for light-weight construction because of their high structural stability. In the process of cons…
An algebraic geometry approach to nonlinear parametric optimization in control
Ioannis A. Fotiou, Philipp Rostalski, Bernd Sturmfels +1
We present a method for nonlinear parametric optimization based on algebraic geometry. The problem to be studied, which arises in optimal control, is to minimize a polynomial funct…
Real-time Optimization and Adaptation of the Crosswind Flight of Tethered Wings for Airborne Wind Energy
Aldo U. Zgraggen, Lorenzo Fagiano, Manfred Morari
Airborne wind energy systems aim to generate renewable energy by means of the aerodynamic lift produced by a wing tethered to the ground and controlled to fly crosswind paths. The…
Adaptive Stochastic MPC under Unknown Noise Distribution
Charis Stamouli, Anastasios Tsiamis, Manfred Morari +1
In this paper, we address the stochastic MPC (SMPC) problem for linear systems, subject to chance state constraints and hard input constraints, under unknown noise distribution. Fi…
Automatic crosswind flight of tethered wings for airborne wind energy: modeling, control design and experimental results
Lorenzo Fagiano, Aldo U. Zgraggen, Manfred Morari +1
An approach to control tethered wings for airborne wind energy is proposed. A fixed length of the lines is considered, and the aim of the control system is to obtain figure-eight c…
Automatic Retraction and Full Cycle Operation for a Class of Airborne Wind Energy Generators
Aldo U. Zgraggen, Lorenzo Fagiano, Manfred Morari
Airborne wind energy systems aim to harvest the power of winds blowing at altitudes higher than what conventional wind turbines reach. They employ a tethered flying structure, usua…
Dynamic vehicle redistribution and online price incentives in shared mobility systems
Julius Pfrommer, Joseph Warrington, Georg Schildbach +1
This paper considers a combination of intelligent repositioning decisions and dynamic pricing for the improved operation of shared mobility systems. The approach is applied to Lond…
Design of First-Order Optimization Algorithms via Sum-of-Squares Programming
Mahyar Fazlyab, Manfred Morari, Victor M. Preciado
In this paper, we propose a framework based on sum-of-squares programming to design iterative first-order optimization algorithms for smooth and strongly convex problems. Our start…
Analysis of Optimization Algorithms via Integral Quadratic Constraints: Nonstrongly Convex Problems
Mahyar Fazlyab, Alejandro Ribeiro, Manfred Morari +1
In this paper, we develop a unified framework able to certify both exponential and subexponential convergence rates for a wide range of iterative first-order optimization algorithm…
Stability Analysis of Complementarity Systems with Neural Network Controllers
Alp Aydinoglu, Mahyar Fazlyab, Manfred Morari +1
Complementarity problems, a class of mathematical optimization problems with orthogonality constraints, are widely used in many robotics tasks, such as locomotion and manipulation,…
Probabilistic Verification and Reachability Analysis of Neural Networks via Semidefinite Programming
Mahyar Fazlyab, Manfred Morari, George J. Pappas
Quantifying the robustness of neural networks or verifying their safety properties against input uncertainties or adversarial attacks have become an important research area in lear…
Learning to Track Dynamic Targets in Partially Known Environments
Heejin Jeong, Hamed Hassani, Manfred Morari +2
We solve active target tracking, one of the essential tasks in autonomous systems, using a deep reinforcement learning (RL) approach. In this problem, an autonomous agent is tasked…
Certified Invertibility in Neural Networks via Mixed-Integer Programming
Tianqi Cui, Thomas Bertalan, George J. Pappas +3
Neural networks are known to be vulnerable to adversarial attacks, which are small, imperceptible perturbations that can significantly alter the network's output. Conversely, there…
Embedded Online Optimization for Model Predictive Control at Megahertz Rates
Juan L. Jerez, Paul J. Goulart, Stefan Richter +3
Faster, cheaper, and more power efficient optimization solvers than those currently offered by general-purpose solutions are required for extending the use of model predictive cont…
Low-complexity method for hybrid MPC with local guarantees
Damian Frick, Angelos Georghiou, Juan L. Jerez +2
Model predictive control problems for constrained hybrid systems are usually cast as mixed-integer optimization problems (MIP). However, commercial MIP solvers are designed to run…
Safety Verification and Robustness Analysis of Neural Networks via Quadratic Constraints and Semidefinite Programming
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…
Temporal Robustness of Temporal Logic Specifications: Analysis and Control Design
Alëna Rodionova, Lars Lindemann, Manfred Morari +1
We study the temporal robustness of temporal logic specifications and show how to design temporally robust control laws for time-critical control systems. This topic is of particul…
Large Scale Model Predictive Control with Neural Networks and Primal Active Sets
Steven W. Chen, Tianyu Wang, Nikolay Atanasov +2
This work presents an explicit-implicit procedure to compute a model predictive control (MPC) law with guarantees on recursive feasibility and asymptotic stability. The approach co…
Learning -function approximations for hybrid control problems
Sandeep Menta, Joseph Warrington, John Lygeros +1
The main challenge in controlling hybrid systems arises from having to consider an exponential number of sequences of future modes to make good long-term decisions. Model predictiv…
Learning Lyapunov Functions for Hybrid Systems
Shaoru Chen, Mahyar Fazlyab, Manfred Morari +2
We propose a sampling-based approach to learn Lyapunov functions for a class of discrete-time autonomous hybrid systems that admit a mixed-integer representation. Such systems incl…
Time-Robust Control for STL Specifications
Alena Rodionova, Lars Lindemann, Manfred Morari +1
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. Signal Temp…
The Scenario Approach for Stochastic Model Predictive Control with Bounds on Closed-Loop Constraint Violations
Georg Schildbach, Lorenzo Fagiano, Christoph Frei +1
Many practical applications of control require that constraints on the inputs and states of the system be respected, while optimizing some performance criterion. In the presence of…
Learning Region of Attraction for Nonlinear Systems
Shaoru Chen, Mahyar Fazlyab, Manfred Morari +2
Estimating the region of attraction (ROA) of general nonlinear autonomous systems remains a challenging problem and requires a case-by-case analysis. Leveraging the universal appro…
Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks
Mahyar Fazlyab, Alexander Robey, Hamed Hassani +2
Tight estimation of the Lipschitz constant for deep neural networks (DNNs) is useful in many applications ranging from robustness certification of classifiers to stability analysis…
Learning Lyapunov Functions for Piecewise Affine Systems with Neural Network Controllers
Shaoru Chen, Mahyar Fazlyab, Manfred Morari +2
We propose a learning-based method for Lyapunov stability analysis of piecewise affine dynamical systems in feedback with piecewise affine neural network controllers. The proposed…
Fast AC Power Flow Optimization using Difference of Convex Functions Programming
Sandro Merkli, Alexander Domahidi, Juan Jerez +2
An effective means for analyzing the impact of novel operating schemes on power systems is time domain simulation, for example for investigating optimization-based curtailment of r…
Linear Controller Design for Chance Constrained Systems
Georg Schildbach, Paul Goulart, Manfred Morari
This paper is concerned with the design of a linear control law for linear systems with stationary additive disturbances. The objective is to find a state feedback gain that minimi…
A Prediction-Correction Algorithm for Real-Time Model Predictive Control
Santiago Paternain, Manfred Morari, Alejandro Ribeiro
In this work we adapt a prediction-correction algorithm for continuous time-varying convex optimization problems to solve dynamic programs arising from Model Predictive Control. In…
A Decomposition Method for Large Scale MILPs, with Performance Guarantees and a Power System Application
Robin Vujanic, Peyman Mohajerin Esfahani, Paul Goulart +2
Lagrangian duality in mixed integer optimization is a useful framework for problems decomposition and for producing tight lower bounds to the optimal objective, but in contrast to…
Robust Closed-loop Model Predictive Control via System Level Synthesis
Shaoru Chen, Han Wang, Manfred Morari +2
In this paper, we consider the robust closed-loop model predictive control (MPC) of a linear time-variant (LTV) system with norm bounded disturbances and LTV model uncertainty, whe…
On-line direct data driven controller design approach with automatic update for some of the tuning parameters
Marko Tanaskovic, Lorenzo Fagiano, Carlo Novara +1
This manuscript contains technical details of recent results developed by the authors on the algorithm for direct design of controllers for nonlinear systems from data that has the…
A Projected Gradient and Constraint Linearization Method for Nonlinear Model Predictive Control
Giampaolo Torrisi, Sergio Grammatico, Roy S. Smith +1
Projected Gradient Descent denotes a class of iterative methods for solving optimization programs. Its applicability to convex optimization programs has gained significant populari…