Optimal control to improve reliability of demand responsive transport priority at signalized intersections considering the stochastic process

2022 ◽  
Vol 218 ◽  
pp. 108192
Author(s):  
Shidong Liang ◽  
Hu Zhang ◽  
Zhiming Fang ◽  
Shengxue He ◽  
Jing Zhao ◽  
...  
Keyword(s):  
Stochastic Process ◽  
Optimal Control ◽  
Signalized Intersections ◽  
Improve Reliability

A Sampling Approach to Modeling and Control of a Large-Size Robot Population

2010 ◽  
Author(s):  
Dejan Milutinovic´ ◽  
Devendra P. Garg
Keyword(s):  
Stochastic Process ◽  
Optimal Control ◽  
Close Relation ◽  
Modeling And Control ◽  
Stochastic Sampling ◽  
Large Size ◽  
Continuous Components ◽  
Estimation And Control ◽  
The Individual ◽  
And Control

Motivated by the close relation between estimation and control problems, we explore the possibility to utilize stochastic sampling for computing the optimal control for a large-size robot population. We assume that the individual robot state is composed of discrete and continuous components, while the population is controlled in a probability space. Utilizing a stochastic process, we can compute the state probability density function evolution, as well as use the stochastic process samples to evaluate the Hamiltonian defining the optimal control. The proposed method is illustrated by an example of centralized optimal control for a large-size robot population.

The Testing Filtering and Majorized Algorithm of a Kind of Nonstationary Stochastic Transmission System in Intelligence Control

2014 ◽  
Vol 926-930 ◽  
pp. 3581-3584
Author(s):  
Xiao Nan Xiao
Keyword(s):  
Stochastic Process ◽  
Optimal Control ◽  
Mathematical Models ◽  
Incomplete Data ◽  
Transmission System ◽  
Linear Filtering ◽  
Optimal Coding ◽  
Non Linear ◽  
Intelligence Control ◽  
Nonstationary Stochastic Process

In intelligence control, applying the method of optimal non-linear filtering and majorized algorithm, this paper discusses the optimal control of a kind of incomplete data and continuous nonstationary stochastic process; yields two optimal control mathematical models in these two situations; illustrates how to establish the optimal coding and decoding of the nonstationary stochastic process; and provides an effective and reliable approach for the optimal control of such a process.

scholarly journals On stochastic optimal control laws

1973 ◽  
Vol 52 ◽  
pp. 1-30 ◽  
Author(s):  
Makiko Nisio
Keyword(s):  
Stochastic Process ◽  
Optimal Control ◽  
Stochastic Optimal Control ◽  
Admissible Control ◽  
Optimal Controls ◽  
Initial Condition ◽  
Control Laws ◽  
The Matrix ◽  
Vector Valued ◽  
Admissible Controls

Let us begin by recalling the existence of optimal controls for a class of stochastic differential equationswith given initial condition X(0) = x, where B is an n-dimensional Brownian motion and the control U is a stochastic process. As admissible controls, let us allow all non-anticipative process U(t) = (U1(t),…Um(t)) ∈ Γ where Γ is a compact subset of Rm. We call Γ a control region. Assume that the matrix valued functional β and the n-vector valued α satisfy a Lipscitz condition in X and some growth condition. Then we have a unique solution Xu for an admissible control U.

Integrated Fractional white Noise as an Alternative to Multifractional Brownian Motion

2007 ◽  
Vol 44 (02) ◽  
pp. 393-408 ◽  
Author(s):  
Allan Sly
Keyword(s):  
Stochastic Process ◽  
Brownian Motion ◽  
White Noise ◽  
Gaussian Process ◽  
Discrete Data ◽  
Hölder Exponent ◽  
Scaling Properties ◽  
Multifractional Brownian Motion ◽  
Local Properties

Multifractional Brownian motion is a Gaussian process which has changing scaling properties generated by varying the local Hölder exponent. We show that multifractional Brownian motion is very sensitive to changes in the selected Hölder exponent and has extreme changes in magnitude. We suggest an alternative stochastic process, called integrated fractional white noise, which retains the important local properties but avoids the undesirable oscillations in magnitude. We also show how the Hölder exponent can be estimated locally from discrete data in this model.

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