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Found 13 papers, 0 papers with code
A neural network-based approach to hybrid systems identification for control
We consider the problem of designing a machine learning-based model of an unknown dynamical system from a finite number of (state-input)-successor state data points, such that the model obtained is also suitable for optimal control design.
Generalized uncertain Nash games: Reformulation and robust equilibrium seeking -- Extended version
We consider generalized Nash equilibrium problems (GNEPs) with linear coupling constraints affected by both local (i. e., agent-wise) and global (i. e., shared resources) disturbances taking values in polyhedral uncertainty sets.
Counter-example guided inductive synthesis of control Lyapunov functions for uncertain systems
We propose a counter-example guided inductive synthesis (CEGIS) scheme for the design of control Lyapunov functions and associated state-feedback controllers for linear systems affected by parametric uncertainty with arbitrary shape.
Incentives and co-evolution: Steering linear dynamical systems with noncooperative agents
Modern socio-technical systems typically consist of many interconnected users and competing service providers, where notions like market equilibrium are tightly connected to the ``evolution'' of the network of users.
An active learning method for solving competitive multi-agent decision-making and control problems
To identify a stationary action profile for a population of competitive agents, each executing private strategies, we introduce a novel active-learning scheme where a centralized external observer (or entity) can probe the agents' reactions and recursively update simple local parametric estimates of the action-reaction mappings.
Tracking-based distributed equilibrium seeking for aggregative games
We propose fully-distributed algorithms for Nash equilibrium seeking in aggregative games over networks.
Robust stabilization of polytopic systems via fast and reliable neural network-based approximations
We consider the design of fast and reliable neural network (NN)-based approximations of traditional stabilizing controllers for linear systems with polytopic uncertainty, including control laws with variable structure and those based on a (minimal) selection policy.
Personalized incentives as feedback design in generalized Nash equilibrium problems
We investigate both stationary and time-varying, nonmonotone generalized Nash equilibrium problems that exhibit symmetric interactions among the agents, which are known to be potential.
Reliably-stabilizing piecewise-affine neural network controllers
A common problem affecting neural network (NN) approximations of model predictive control (MPC) policies is the lack of analytical tools to assess the stability of the closed-loop system under the action of the NN-based controller.
Learning equilibria with personalized incentives in a class of nonmonotone games
We consider quadratic, nonmonotone generalized Nash equilibrium problems with symmetric interactions among the agents.
Pursuing robust decisions in uncertain traffic equilibrium problems
We evaluate the robustness of agents' traffic equilibria in randomized routing games characterized by an uncertain network demand with a possibly unknown probability distribution.
Probabilistic stabilizability certificates for a class of black-box linear systems
We provide out-of-sample certificates on the controlled invariance property of a given set with respect to a class of black-box linear systems.
Optimization and Control Systems and Control Systems and Control
The optimal transport paradigm enables data compression in data-driven robust control
A new data-enabled control technique for uncertain linear time-invariant systems, recently conceived by Coulson et\ al., builds upon the direct optimization of controllers over input/output pairs drawn from a large dataset.
