Optimal Control with Nonlinear Models: Recent Studies

1998 ◽  
pp. 174-192
Author(s):  
Ric D. Herbert
Keyword(s):  
Optimal Control ◽  
Nonlinear Models

Optimal control of large nonlinear models: An efficient method of policy search applied to the Dutch economy

1983 ◽  
Vol 5 (2) ◽  
pp. 253-270 ◽  
Author(s):  
Andries S. Brandsma ◽  
A.J. Hughes Hallett ◽  
Nico van der Windt
Keyword(s):  
Optimal Control ◽  
Efficient Method ◽  
Nonlinear Models ◽  
Policy Search

scholarly journals Moments method in problems of optimal control of nonlinear models of logistic type

2021 ◽  
Vol 16 ◽  
pp. 155
Author(s):  
S.V. Chernyshenko
Keyword(s):  
Optimal Control ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Logistic Model ◽  
Nonlinear Models ◽  
Mathematical Ecology ◽  
Moments Method

We investigate the problem of optimal control of two nonlinear models of mathematical ecology: logistic model and model of development in adversary environment. We consider four statements of optimal control problem, two criteria of quality (quadratic and linear ones). The solution is obtained either analytically, or numerically, by iterative approximations method.

Wave Energy Extraction Maximization Using Direct Transcription

2012 ◽  
Author(s):  
James T. Allison ◽  
Allen Kaitharath ◽  
Daniel R. Herber
Keyword(s):  
Optimal Control ◽  
Wave Energy ◽  
Nonlinear Models ◽  
Cost Effective ◽  
Ocean Waves ◽  
Open Loop ◽  
Energy Extraction ◽  
Optimal Control Strategy ◽  
Direct Transcription ◽  
Wave Energy Converters

Wave energy converters (WECs) extract energy from the motion of ocean waves. A variety of different WEC devices have been studied over the past several decades, with emphasis on cost-effective energy extraction. Active control has been shown to improve energy production significantly. Here we investigate energy extraction potential of a tethered heaving cylinder WEC using direct transcription (DT), an open-loop optimal control strategy. This enables direct inclusion of asymmetric constraints on power and tether force, practical considerations not considered in previous studies, and opens the door to WEC optimal control problems with more realistic nonlinear models and integration of control design with physical system design.

Grid-Connected of Photovoltaic Module Using Inverse Optimal Control

2013 ◽  
Vol 765-767 ◽  
pp. 1834-1839 ◽  
Author(s):  
Guang Ru Zhang ◽  
Dong Sheng Yang ◽  
Ting Liu ◽  
Jun Yan Zeng ◽  
Yu Kun Xu
Keyword(s):  
Optimal Control ◽  
Reactive Power ◽  
Control Method ◽  
Nonlinear Models ◽  
Dynamic Responses ◽  
Photovoltaic Module ◽  
Control Methods ◽  
Inverse Optimal Control ◽  
Digital Implementation ◽  
Pv Inverter

Switching converters and photovoltaic panels are nonlinear models, but existing PV inverter control methods are mostly based on the linearization control methods. The traditional linear methods are often difficult to eliminate static errors and the dynamic responses are not very satisfactory. In this paper, on the basis of nonlinear methods to control the switching converter, a mathematical model of discrete rotating coordinate system is given and an inverse optimal control method for single-phase grid-connected inverter control is proposed at the same time. This method achieved independent power control purposes due to implementing active and reactive power decomposition; and it is easier to use a microcontroller digital implementation for the discrete method. Simulation results show that this method can eliminate the static errors, and have a good dynamic response.

scholarly journals LQG Online Learning

2017 ◽  
Vol 29 (8) ◽  
pp. 2203-2291 ◽  
Author(s):  
Giorgio Gnecco ◽  
Alberto Bemporad ◽  
Marco Gori ◽  
Marcello Sanguineti
Keyword(s):  
Optimal Control ◽  
Online Learning ◽  
Nonlinear Models ◽  
Machine Learning Techniques ◽  
Parameter Vector ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian ◽  
Learning Framework ◽  
Learning Techniques ◽  
Lqg Optimal Control

Optimal control theory and machine learning techniques are combined to formulate and solve in closed form an optimal control formulation of online learning from supervised examples with regularization of the updates. The connections with the classical linear quadratic gaussian (LQG) optimal control problem, of which the proposed learning paradigm is a nontrivial variation as it involves random matrices, are investigated. The obtained optimal solutions are compared with the Kalman filter estimate of the parameter vector to be learned. It is shown that the proposed algorithm is less sensitive to outliers with respect to the Kalman estimate (thanks to the presence of the regularization term), thus providing smoother estimates with respect to time. The basic formulation of the proposed online learning framework refers to a discrete-time setting with a finite learning horizon and a linear model. Various extensions are investigated, including the infinite learning horizon and, via the so-called kernel trick, the case of nonlinear models.

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