Sentence examples for cost functional and from inspiring English sources

In the special case where b, g, (bar g), q, (bar q(cdot)), ρ, and (bar rho (cdot)) vanish, we denote the corresponding mean-field LQ problem, cost functional, and value function by Problem (MF-LQ 0, MF-LQ 0;u, and V 0(t,ξ), respectively.

The work shows steps towards a good selection of a cost functional and an analysis, based on recent stability results, shows why a cost function that is usual in other control methods is not suitable.

An interior point method with penalty function is used to incorporate constraints into a modified cost functional, and a Lyapunov based extremum seeking approach is used to compute the trajectory parameters.

This strategy does not care about the cost functional and it is therefore not optimal.

We analyze the properties of the cost functional and prove the convergence of the minimization process.

A suitable terminal cost functional and also an appropriate terminal region are utilized to achieve asymptotic stability.

We consider Optimal Control Problems (OCPs) with smooth cost functionals and apply a newly elaborated abstraction for the system dynamics under consideration.

In this context, the Hamilton Jacobi partial differential equation can be used for sequential solution of multi-games defined as n-person games with m controls, r cost functionals and multiple min, max, min max, etc., operators in fixed order of application and not creating multi-objective game problems.

In Sect. 4 we discuss the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Gamma $$\end{document} Γ -convergence of cost functionals and present the main result on the sensitivity of optimal solutions.

In this problem, the weighting matrices of the quadratic cost functional are indefinite, and the optimal control does have the so-called dual effect.

These algorithms differ in their functional models, cost functions, and regularization methods.