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Proposal for a Distributed Intelligent Control Architecture Based on Heterogeneous Modular Devices
Abstract
The main objective of the thesis is to specify, design, characterize, and validate a distributed intelligent control architecture. This architecture has as main requirements the support for the heterogeneity of sensors and actuators and the inclusion within the industry 4.0 paradigm. In addition, it must provide a level of intelligence appropriate to the work environment and the processing capabilities of each module.
The management of people and vehicles’ mobility is an aspect of continuous study due to its contribution to pollution. Traffic light control determines the queues that can form at crossroads. Usually, this control is not adapted to the existing traffic at a specific time since the adaptation implies knowing the pedestrians and vehicles that are circulating at all times. The article proposes using intelligent method that allow the detection of vehicles and changing access times to the intersection depending on the circumstances to solve this problem. A simulation has been carried out to validate the system, generating loads in MatLab and simulating the control with Simulink. A traffic light cycle with varying times depending on pedestrians and vehicles load has been simulated and has been compared with a fixed time cycle simulation. In this article, Op and Sat indicators are proposed to measure optimisation of the control algorithm on the state of the crossing. Using these indicators verified that it is possible to optimise the waiting time, almost independently of the traffic load in the best of cases.KeywordsSmart citiesIntelligent traffic controlTraffic parameters
This paper presents a novel smart street lighting (SmSL) system in which energy consumption by a group of street lighting poles is minimized based on Brute-Force search algorithm. While outdoor lighting imposes considerable cost, maintenance, safety, and environmental issues; utilization of advanced street lighting system with energy saving, autonomous fault detection, and monitoring capabilities benefits all the players involved including municipalities and distribution companies. The proposed SmSL has a hierarchical platform. The segment or intermediate controller determines the scheduling, switching, and dimming level of each pole based on the proposed optimization subroutine and transmits the controller set points to the local pole controller through Power Line Communications (PLC). Optimization of street lighting electrical energy is achieved by minimizing a cost function, considering operational constraints, ambient luminance, and local traffic flow. The local controller acts as an actuator and applies the received commands. The controller inherently responses to lamp fault. Moreover, pole electrical parameters and status of the lamp and its capacitor is transmitted to the intermediate controller. The supervisory controller which is installed on a server in distribution center monitors the whole system and sends appropriate commands such as minimum required luminance in the area to the segment controller based on WiMAX wireless communications. The whole system is developed and implemented in a pilot street. The experimental results show considerable energy saving with the proposed SmSL and reduced maintenance costs.
This paper introduces an intelligent control system for traffic signal applications, called Fuzzy Intelligent Traffic Signal (FITS) control. It provides a convenient and economic approach to improve existing traffic light infrastructure. The control system is programmed on an intermediate hardware device capable of receiving messages from signal controller hardware as well as overriding traffic light indications during real-time operations. Signal control and optimization toolboxes are integrated into the embedded software in the FITS hardware device. A fuzzy logic based control has been implemented in FITS. In order to evaluate the effects of FITS system, this study attempts to develop a computational framework to evaluate FITS system using microscopic traffic simulation. A case study is carried out, comparing different commonly used signal control strategies with the FITS control approach. The simulation results show that the control system has the potential to improve traffic mobility, compared to all of the tested signal control strategies, due to its ability in generating flexible phase structures and making intelligent timing decisions. In addition, the effects of detector malfunction are also investigated in this study. The experiment results show that FITS exhibits superior performance than several other controllers when a few detectors are out-of-order due to its self-diagnostics feature.
Traffic congestion is a severe problem in many modern cities around the world. To solve the problem, we have proposed a framework for a dynamic and automatic traffic light control expert system combined with a simulation model, which is composed of six submodels coded in Arena to help analyze the traffic problem. The model adopts interarrival time and interdeparture time to simulate the arrival and leaving number of cars on roads. In the experiment, each submodel represents a road that has three intersections. The simulation results physically prove the efficiency of the traffic system in an urban area, because the average waiting time of cars at every intersection is sharply dropped when the red light duration is 65 s and the green light time duration is 125 s. Meanwhile, further analysis also shows if we keep the interarrival time of roads A, B, and C, and change that of roads D, E, and F from 1.7 to 3.4 s and the interdeparture times at the three intersections on roads A, B, and C are equal to 0.6 s, the total performance of the simulation model is the best. Finally, according to the data collected from RFID readers and the best, second and third best traffic light durations generated from the simulation model, the automatic and dynamic traffic light control expert system can control how long traffic signals should be for traffic improvement.