A novel Hα control strategy for design of a robust dynamic routing algorithm in traffic networks

Abstract

In this paper novel centralized and decentralized routing control strategies based on minimization of the worst-case queuing length are proposed. The centralized routing problem is formulated as an H∞ optimal control problem to achieve a robust routing performance in presence of multiple and unknown fast time-varying network delays. Unlike similar previous work in the literature the delays in the queuing model are assumed to be unknown and time-varying. A Linear Matrix Inequality (LMI) constraint is obtained to design a delay-dependent H∞ controller. The physical constraints that are present in the network are then expressed as LMI feasibility conditions. Our proposed centralized routing scheme is then reformulated in a decentralized frame work. This modification yields an algorithm that obtains the "fastest route", increases the robustness against multiple unknown time-varying delays, and enhances the scalability of the algorithm to large scale traffic networks. Simulation results are presented to illustrate and demonstrate the effectiveness and capabilities of our proposed novel dynamic routing strategies.

The Switching Message Estimator for Network-Based Motion Control Systems

“…Furthermore, the least-square approach with a different M applied to a different averaging SDQ shows that applying LSE (5,3) to compensate for the low missing data rate presents the best motion accuracy as the averaging SDQ is less than one, but it becomes the worst as the averaging SDQ increases larger than 1.5, as shown in Figure 12. Moreover, LSE(3, 2) is more suitable for the situation as the averaging SDQ is between 1 and 2.…”

“…However, network-introduced time delay is unavoidable and the data dropout of NCS becomes significant as node and data length increase or as the system sampling time decreases with a limited network bandwidth. Recently, coping with the network-introduced delay on NCS has been widely studied using various approaches, such as robust H ∞ control [3], passive control [4], and predictive control [5]. These methods handle the stochastic network delay with the assumption that the delay is either relatively small or similar to the sampling time in NCS.…”

A decentralized H<inf>&#x221E;</inf> routing control strategy for mobile networked multi-agents

“…In [3], the dynamic routing problem was defined as a team optimization problem and an approximate solution based on neural networks was obtained. In [4], the authors have introduced robust centralized as well as decentralized routing control strategies for networks with a fixed topology based on the minimization of the worst-case queueing length, which is related to the queueing delays.…”

“…Therefore, the dynamics of the network characterizing the traffic flow will become time varying. In this paper, the approach introduced in [4] is generalized to mobile networks. To achieve this goal, the mobile network routing model is described as a Markovian jump linear system (MJLS).…”

“…The queueing dynamics considered in [4] was derived based on the fluid flow conservation principle. Each state of the subsystem (node) represents a queue corresponding to a given destination node.…”