On convexity of the robust freeway network control problem in the presence of prediction and model uncertainty

2020 ◽  
Vol 134 ◽  
pp. 167-190
Author(s):  
Marius Schmitt ◽  
John Lygeros
Keyword(s):  
Control Problem ◽  
Model Uncertainty ◽  
Network Control

Urban Oversaturated Traffic Network Control Based on Preference Multi-Objective Compatible Optimization Control

2011 ◽  
Vol 317-319 ◽  
pp. 1373-1384 ◽  
Author(s):  
Juan Chen ◽  
Chang Liang Yuan
Keyword(s):  
Control Problem ◽  
Control Algorithm ◽  
Control Method ◽  
Network Control ◽  
Feasible Region ◽  
Traffic Network ◽  
Total Delay ◽  
Multi Objective ◽  
Optimization Control ◽  
Objective Control

To solve the traffic congestion control problem on oversaturated network, the total delay is classified into two parts: the feeding delay and the non-feeding delay, and the control problem is formulated as a conflicted multi-objective control problem. The simultaneous control of multiple objectives is different from single objective control in that there is no unique solution to multi-objective control problems(MOPs). Multi-objective control usually involves many conflicting and incompatible objectives, therefore, a set of optimal trade-off solutions known as the Pareto-optimal solutions is required. Based on this background, a modified compatible control algorithm(MOCC) hunting for suboptimal and feasible region as the control aim rather than precise optimal point is proposed in this paper to solve the conflicted oversaturated traffic network control problem. Since it is impossible to avoid the inaccurate system model and input disturbance, the controller of the proposed multi-objective compatible control strategy is designed based on feedback control structure. Besides, considering the difference between control problem and optimization problem, user's preference are incorporated into multi-objective compatible control algorithm to guide the search direction. The proposed preference based compatible optimization control algorithm(PMOCC) is used to solve the oversaturated traffic network control problem in a core area of eleven junctions under the simulation environment. It is proved that the proposed compatible optimization control algorithm can handle the oversaturated traffic network control problem effectively than the fixed time control method.

scholarly journals Explicit Solution for a Network Control Problem in the Large Deviation Regime

2004 ◽  
Vol 46 (1/2) ◽  
pp. 159-176 ◽  
Author(s):  
Rami Atar ◽  
Adam Shwartz ◽  
Paul Dupuis
Keyword(s):  
Control Problem ◽  
Explicit Solution ◽  
Large Deviation ◽  
Network Control

scholarly journals Chance-Constrained Based Voltage Control Framework to Deal with Model Uncertainties in MV Distribution Systems

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 5161
Author(s):  
Bashir Bakhshideh Zad ◽  
Jean-François Toubeau ◽  
François Vallée
Keyword(s):  
Control Problem ◽  
Confidence Level ◽  
Model Uncertainty ◽  
Distribution Systems ◽  
Optimization Problem ◽  
Linear Optimization ◽  
Voltage Control ◽  
Model Uncertainties ◽  
Linear Optimization Problem ◽  
Control Framework

In this paper, a chance-constrained (CC) framework is developed to manage the voltage control problem of medium-voltage (MV) distribution systems subject to model uncertainty. Such epistemic uncertainties are inherent in distribution system analyses given that an exact model of the network components is not available. In this context, relying on the simplified deterministic models can lead to insufficient control decisions. The CC-based voltage control framework is proposed to tackle this issue while being able to control the desired protection level against model uncertainties. The voltage control task disregarding the model uncertainties is firstly formulated as a linear optimization problem. Then, model uncertainty impacts on the above linear optimization problem are evaluated. This analysis defines that the voltage control problem subject to model uncertainties should be modelled with a joint CC formulation. The latter is accordingly relaxed to individual CC optimizations using the proposed methods. The performance of proposed CC voltage control methods is finally tested in comparison with that of the robust optimization. Simulation results confirm the accuracy of confidence level expected from the proposed CC voltage control formulations. The proposed technique allows the system operators to tune the confidence level parameter such that a tradeoff between operation costs and conservatism level is attained.

scholarly journals Online Self-Organizing Network Control with Time Averaged Weighted Throughput Objective

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Zhicong Zhang ◽  
Shuai Li ◽  
Xiaohui Yan
Keyword(s):  
Reinforcement Learning ◽  
Control Problem ◽  
Decision Model ◽  
Learning Algorithm ◽  
Queueing Network ◽  
Network Control ◽  
Learning Ability ◽  
Control Decision ◽  
Self Organizing ◽  
Reinforcement Learning Algorithm

We study an online multisource multisink queueing network control problem characterized with self-organizing network structure and self-organizing job routing. We decompose the self-organizing queueing network control problem into a series of interrelated Markov Decision Processes and construct a control decision model for them based on the coupled reinforcement learning (RL) architecture. To maximize the mean time averaged weighted throughput of the jobs through the network, we propose a reinforcement learning algorithm with time averaged reward to deal with the control decision model and obtain a control policy integrating the jobs routing selection strategy and the jobs sequencing strategy. Computational experiments verify the learning ability and the effectiveness of the proposed reinforcement learning algorithm applied in the investigated self-organizing network control problem.

Trend Growth and Robust Monetary Policy

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Kohei Hasui
Keyword(s):  
Monetary Policy ◽  
Robust Control ◽  
Control Problem ◽  
Productivity Growth ◽  
Model Uncertainty ◽  
Macroeconomic Fluctuations ◽  
Policy Studies ◽  
Trend Growth

AbstractRecent monetary policy studies have shown that the trend productivity growth has non-trivial implications for monetary policy. This paper investigates how trend growth alters the effect of model uncertainty on macroeconomic fluctuations by introducing a robust control problem. We show that an increase in trend growth reduces the effect of the central bank’s model uncertainty and, hence, mitigates the large macroeconomic fluctuations. Moreover, the increase in trend growth contributes to bringing the economy into determinacy regions even if larger model uncertainty exists. These results indicate that trend growth contributes to stabilizing the economy in terms of both variance and determinacy when model uncertainty exists.

OPTIMAL STOCHASTIC CONTROL PROBLEM UNDER MODEL UNCERTAINTY WITH NONENTROPY PENALTY

2017 ◽  
Vol 20 (03) ◽  
pp. 1750015
Author(s):  
WAHID FAIDI ◽  
ANIS MATOUSSI ◽  
MOHAMED MNIF
Keyword(s):  
Differential Equation ◽  
Control Problem ◽  
Stochastic Control ◽  
Model Uncertainty ◽  
General Framework ◽  
Backward Stochastic Differential Equation ◽  
Terminal Condition ◽  
Penalty Term ◽  
Stochastic Control Problem ◽  
Continuous Filtration

In this paper, a stochastic control problem under model uncertainty with general penalty term is studied. Two types of penalties are considered. The first one is of type [Formula: see text]-divergence penalty treated in the general framework of a continuous filtration. The second one called consistent time penalty is studied in the context of a Brownian filtration. In the case of consistent time penalty, we characterize the value process of our stochastic control problem as the unique solution of a class of quadratic backward stochastic differential equation with unbounded terminal condition.

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