Search Results for author:
Found 16 papers, 6 papers with code
Parameter-Covariance Maximum Likelihood Estimation
Linear time series modelling is dominated by the use of purely autoregressive models even though incorporating moving average components can greatly improve parsimony.
Stable and Transferable Hyper-Graph Neural Networks
To do so, we provide a framework for bounding the stability and transferability error of GNNs across arbitrary graphs via spectral similarity.
Robust Model Predictive Control of Time-Delay Systems through System Level Synthesis
In the proposed method, a time-varying feedback control policy is optimized such that the robust satisfaction of all constraints for the closed-loop system is guaranteed.
One-Shot Reachability Analysis of Neural Network Dynamical Systems
The arising application of neural networks (NN) in robotic systems has driven the development of safety verification methods for neural network dynamical systems (NNDS).
Differentiable Safe Controller Design through Control Barrier Functions
Learning-based controllers, such as neural network (NN) controllers, can show high empirical performance but lack formal safety guarantees.
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.
Learning Operators with Coupled Attention
Supervised operator learning is an emerging machine learning paradigm with applications to modeling the evolution of spatio-temporal dynamical systems and approximating general black-box relationships between functional data.
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.
AutoEKF: Scalable System Identification for COVID-19 Forecasting from Large-Scale GPS Data
The likelihood of the observations is estimated recursively using an Extended Kalman Filter and can be easily optimized using gradient-based methods to compute maximum likelihood estimators.
Data-Driven Methods for Present and Future Pandemics: Monitoring, Modelling and Managing
We provide a roadmap from the access to epidemiological data sources to the control of epidemic phenomena.
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
Stabilization of Complementarity Systems via Contact-Aware Controllers
We propose a control framework which can utilize tactile information by exploiting the complementarity structure of contact dynamics.
Robotics
Robust Deep Learning as Optimal Control: Insights and Convergence Guarantees
By interpreting the min-max problem as an optimal control problem, it has recently been shown that one can exploit the compositional structure of neural networks in the optimization problem to improve the training time significantly.
Contact-Aware Controller Design for Complementarity Systems
While many robotic tasks, like manipulation and locomotion, are fundamentally based in making and breaking contact with the environment, state-of-the-art control policies struggle to deal with the hybrid nature of multi-contact motion.
Robotics
Network Design for Controllability Metrics
In this setting, we first consider a feasibility problem consisting of tuning the edge weights such that certain controllability properties are satisfied.
Optimization and Control
