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Found 23 papers, 13 papers with code
Combined Left and Right Temporal Robustness for Control under STL Specifications
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
Neural networks are known to be vulnerable to adversarial attacks, which are small, imperceptible perturbations that can significantly alter the network's output.
Learning to Control Linear Systems can be Hard
In this paper, we study the statistical difficulty of learning to control linear systems.
Adaptive Stochastic MPC under Unknown Noise Distribution
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
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
However, it is challenging to design LTV state feedback controllers in the face of model uncertainty whose effects are difficult to bound.
System Level Synthesis-based Robust Model Predictive Control through Convex Inner Approximation
We propose a robust model predictive control (MPC) method for discrete-time linear time-invariant systems with norm-bounded additive disturbances and model uncertainty.
Learning Region of Attraction for Nonlinear Systems
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
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
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
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.
Learning solutions to hybrid control problems using Benders cuts
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.
BayesRace: Learning to race autonomously using prior experience
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
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
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
NeurOpt: Neural network based optimization for building energy management and climate control
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.
Learning Q-network for Active Information Acquisition
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.
Large Scale Model Predictive Control with Neural Networks and Primal Active Sets
The approach combines an offline-trained fully-connected neural network with an online primal active set solver.
Probabilistic Verification and Reachability Analysis of Neural Networks via Semidefinite Programming
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
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
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.
Cloud-based MPC with Encrypted Data
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
This paper describes autonomous racing of RC race cars based on mathematical optimization.
Optimization and Control Robotics Systems and Control
