Zobrazeno 1 - 10
of 149
pro vyhledávání: '"Krishnamoorthy Dinesh"'
Autor:
Dirza, Risvan, Varadarajan, Hari Prasad, Aas, Vegard, Skogestad, Sigurd, Krishnamoorthy, Dinesh
This paper considers the problem of steady-state real-time optimization (RTO) of interconnected systems with a common constraint that couples several units, for example, a shared resource. Such problems are often studied under the context of distribu
Externí odkaz:
http://arxiv.org/abs/2411.04676
Autor:
Krishnamoorthy, Dinesh
This paper introduces a model-free real-time optimization (RTO) framework based on unconstrained Bayesian optimization with embedded constraint control. The main contribution lies in demonstrating how this approach simplifies the black-box optimizati
Externí odkaz:
http://arxiv.org/abs/2402.18415
Application of nonlinear model predictive control (NMPC) to problems with hybrid dynamical systems, disjoint constraints, or discrete controls often results in mixed-integer formulations with both continuous and discrete decision variables. However,
Externí odkaz:
http://arxiv.org/abs/2401.12562
The application of supervised learning techniques in combination with model predictive control (MPC) has recently generated significant interest, particularly in the area of approximate explicit MPC, where function approximators like deep neural netw
Externí odkaz:
http://arxiv.org/abs/2401.12546
Autor:
van der Horst, Anne, Meere, Bas, Krishnamoorthy, Dinesh, Bakker, Saray, van de Vrande, Bram, Stoutjesdijk, Henry, Alonso, Marco, Torta, Elena
This paper presents a Bayesian optimization framework for the automatic tuning of shared controllers which are defined as a Model Predictive Control (MPC) problem. The proposed framework includes the design of performance metrics as well as the repre
Externí odkaz:
http://arxiv.org/abs/2311.01133
Autor:
Orrico, Christopher A., van Berkel, Matthijs, Bosman, Thomas O. S. J., Heemels, W. P. M. H., Krishnamoorthy, Dinesh
Model predictive control (MPC) is promising for fueling and core density feedback control in nuclear fusion tokamaks, where the primary actuators, frozen hydrogen fuel pellets fired into the plasma, are discrete. Previous density feedback control app
Externí odkaz:
http://arxiv.org/abs/2306.00415
This paper presents an adaptive horizon multi-stage model-predictive control (MPC) algorithm. It establishes appropriate criteria for recursive feasibility and robust stability using the theory of input-to-state practical stability (ISpS). The propos
Externí odkaz:
http://arxiv.org/abs/2305.19448
Bayesian optimization (BO) is a powerful black-box optimization framework that looks to efficiently learn the global optimum of an unknown system by systematically trading-off between exploration and exploitation. However, the use of BO as a tool for
Externí odkaz:
http://arxiv.org/abs/2303.14414
Autor:
Krishnamoorthy, Dinesh
This paper considers the problem of data generation for MPC policy approximation. Learning an approximate MPC policy from expert demonstrations requires a large data set consisting of optimal state-action pairs, sampled across the feasible state spac
Externí odkaz:
http://arxiv.org/abs/2303.05607
This work considers the problem of personalized dose guidance using Bayesian optimization that learns the optimum drug dose tailored to each individual, thus improving therapeutic outcomes. Safe learning using interior point method ensures patient sa
Externí odkaz:
http://arxiv.org/abs/2210.16944