scholarly journals Predictive Real-Time Control Optimization of a Stormwater Management System*

2019 ◽  
Author(s):  
S. Shishegar ◽  
S. Duchesne ◽  
G. Pelletier
Keyword(s):  
Real Time ◽  
Stormwater Management ◽  
Management System ◽  
Real Time Control ◽  
Time Control ◽  
Control Optimization

scholarly journals On the Use of a Real-Time Control Approach for Urban Stormwater Management

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2842
Author(s):  
Mario Maiolo ◽  
Stefania Anna Palermo ◽  
Anna Chiara Brusco ◽  
Behrouz Pirouz ◽  
Michele Turco ◽  
...  
Keyword(s):  
Real Time ◽  
Stormwater Management ◽  
Risk Mitigation ◽  
Real Time Control ◽  
Urban Stormwater ◽  
Real Time System ◽  
Control Approach ◽  
Urban Stormwater Management ◽  
Time Control ◽  
Control Configuration

The real-time control (RTC) system is a valid and cost-effective solution for urban stormwater management. This paper aims to evaluate the beneficial effect on urban flooding risk mitigation produced by applying RTC techniques to an urban drainage network by considering different control configuration scenarios. To achieve the aim, a distributed real-time system, validated in previous studies, was considered. This approach uses a smart moveable gates system, controlled by software agents, managed by a swarm intelligence algorithm. By running the different scenarios by a customized version of the Storm Water Management Model (SWMM), the findings obtained show a redistribution of conduits filling degrees, exploiting the whole system storage capacity, with a significant reduction of node flooding and total flood volume.

Real-time Control of Pig Growth through an Integrated Management System

2007 ◽  
Vol 96 (2) ◽  
pp. 257-266 ◽  
Author(s):  
D.J. Parsons ◽  
D.M. Green ◽  
C.P. Schofield ◽  
C.T. Whittemore
Keyword(s):  
Real Time ◽  
Management System ◽  
Integrated Management ◽  
Real Time Control ◽  
Integrated Management System ◽  
Time Control

scholarly journals Research for Nonlinear Model Predictive Controls to Laterally Control Unmanned Vehicle Trajectory Tracking

2020 ◽  
Vol 10 (17) ◽  
pp. 6034 ◽  
Author(s):  
Kegang Zhao ◽  
Chengxia Wang ◽  
Guoquan Xiao ◽  
Haolin Li ◽  
Jie Ye ◽  
...  
Keyword(s):  
Real Time ◽  
Nonlinear Model ◽  
Trajectory Tracking ◽  
Autonomous Driving ◽  
Real Time Control ◽  
Unmanned Vehicle ◽  
Time Control ◽  
Control Optimization ◽  
Vehicle Trajectory ◽  
Rolling Optimization

The autonomous driving is rapid developing recently and model predictive controls (MPCs) have been widely used in unmanned vehicle trajectory tracking. MPCs are advantageous because of their predictive modeling, rolling optimization, and feedback correction. In recent years, most studies on unmanned vehicle trajectory tracking have used only linear model predictive controls to solve MPC algorithm shortcomings in real time. Previous studies have not investigated problems under conditions where speeds are too fast or trajectory curvatures change rapidly, because of the poor accuracy of approximate linearization. A nonlinear model predictive control optimization algorithm based on the collocation method is proposed, which can reduce calculation load. The algorithm aims to reduce trajectory tracking errors while ensuring real-time performance. Monte Carlo simulations of the uncertain systems are carried out to analyze the robustness of the algorithm. Hardware-in-the-loop simulation and actual vehicle experiments were also conducted. Experiment results show that under i7-8700, the calculation time is less than 100 ms, and the mean square error of the lateral deviation is maintained at 10−3 m2, which proves the proposed algorithm can meet the requirement of real time and accuracy in some particular situations. The unmanned vehicle trajectory tracking method provided in this article can meet the needs of real-time control.

Model for Selection of Stormwater Control Alternatives

1977 ◽  
Vol 12 (1) ◽  
pp. 1-26
Author(s):  
R. Bedrosyan ◽  
J. Ganczarczyk
Keyword(s):  
Real Time ◽  
Urban Areas ◽  
Stormwater Management ◽  
Cost Effective ◽  
Real Time Control ◽  
Planning Stage ◽  
Time Control ◽  
Combined Sewer ◽  
Overall Performance ◽  
Management Alternatives

Abstract In urban centres, the degree of pollution from combined sewer overflows and stormwater runoff is becoming ever more noticeable and critical. Among the many stormwater management alternatives for combined sewer overflow abatement, remote monitoring/real-time control is emerging as a cost-effective method in large urban centres. Its advantage is that it does not require additional facilities, but uses the existing system more effectively. A general computer model is needed in the planning stage, which can estimate the overall performance of real-time control and other stormwater management alternatives, without employing expensive, single-event models like WREM. The well-established and most widely used initial planning model STORM is unable to provide an overall system description. Therefore, a new programme, named RAFFI, was devised which can be combined with STORM. The RAFFI is a simple model which provides hourly information on flows and pollutants (BOD and SS) diverted into receiving waters from various overflow points and treatment and by-pass operations at the plant. The model can simulate the overall behaviour of existing and proposed systems in multi-basin urban areas for single storms, or for many storms over a long period, such as one year. In-system storage can be simulated by assuming that the interceptor acts as a common reservoir which can store flows from all watersheds, while each trunk can store flows only from the watershed that it serves. Dynamic regulators can be simulated according to a built-in control strategy, which changes the flow capacity of diversion pipes and outfalls, based on plant, trunk and interceptor flow and storage values at each hour. Capacities for primary, secondary or by-pass operations at the plant can be varied during storms. Treatment of overflows at each outfall can also be simulated. The model is equally applicable to separate storm sewer systems. The objective was not to produce yet another new model, but to integrate programme RAFFI with STORM, in order to increase its capabilities in the initial planning stage. By using STORM-RAFFI, a decision-maker can estimate the overall performance of an existing system during wet

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