Zobrazeno 1 - 10
of 319
pro vyhledávání: '"Modares, Hamidreza"'
Direct Data-Driven Discounted Infinite Horizon Linear Quadratic Regulator with Robustness Guarantees
Autor:
Esmzad, Ramin, Modares, Hamidreza
This paper presents a one-shot learning approach with performance and robustness guarantees for the linear quadratic regulator (LQR) control of stochastic linear systems. Even though data-based LQR control has been widely considered, existing results
Externí odkaz:
http://arxiv.org/abs/2409.10703
Autor:
Esmzad, Ramin, Modares, Hamidreza
In this work, we introduce a novel gradient descent-based approach for optimizing control systems, leveraging a new representation of stable closed-loop dynamics as a function of two matrices i.e. the step size or direction matrix and value matrix of
Externí odkaz:
http://arxiv.org/abs/2409.10662
This paper presents a convex optimization-based solution to the design of state-feedback controllers for solving the linear quadratic regulator (LQR) problem of uncertain discrete-time systems with multiplicative noise. To synthesize a tractable solu
Externí odkaz:
http://arxiv.org/abs/2204.02883
Autor:
Tatari, Farzaneh, Modares, Hamidreza
This paper studies deterministic and stochastic fixed-time stability of autonomous nonlinear discrete-time (DT) systems. Lyapunov conditions are first presented under which the fixed-time stability of deterministic DT system is certified. Extensions
Externí odkaz:
http://arxiv.org/abs/2202.11225
Autor:
Modares, Hamidreza
Safe control of constrained linear systems under both epistemic and aleatory uncertainties is considered. The aleatory uncertainty characterizes random noises and is modeled by a probability distribution function (PDF) and the epistemic uncertainty c
Externí odkaz:
http://arxiv.org/abs/2202.04495
Autor:
Tatari, Farzaneh, Mustafa, Aquib, Mazouchi, Majid, Modares, Hamidreza, Panayiotou, Christos G., Polycarpou, Marios M.
This work presents a rigorous analysis of the adverse effects of cyber-physical attacks on the performance of multi-agent consensus with event-triggered control protocols. It is shown how a strategic malicious attack on sensors and actuators can dece
Externí odkaz:
http://arxiv.org/abs/2201.02997
Data-based safe gain-scheduling controllers are presented for discrete-time linear parameter-varying systems (LPV) with polytopic models. First, $\lambda$-contractivity conditions are provided under which safety and stability of the LPV systems are u
Externí odkaz:
http://arxiv.org/abs/2201.00707
A risk-averse preview-based $Q$-learning planner is presented for navigation of autonomous vehicles. To this end, the multi-lane road ahead of a vehicle is represented by a finite-state non-stationary Markov decision process (MDP). A risk assessment
Externí odkaz:
http://arxiv.org/abs/2112.03232
In this paper, a unified batch-online learning approach is introduced to learn a linear representation of nonlinear system dynamics using the Koopman operator. The presented system modeling approach leverages a novel incremental Koopman-based update
Externí odkaz:
http://arxiv.org/abs/2105.05903
This paper presents a model-free reinforcement learning (RL) algorithm to solve the risk-averse optimal control (RAOC) problem for discrete-time nonlinear systems. While successful RL algorithms have been presented to learn optimal control solutions
Externí odkaz:
http://arxiv.org/abs/2103.14606