A traffic responsive control framework for signalized junctions based on hybrid traffic flow representation

The paper proposes a traffic responsive control framework based on a Model Predictive Control (MPC) approach. The framework focuses on a centralized method, which can simultaneously compute the network decision variables (i.e., the green timings at each junction and the offset of the traffic light plans of the network). Furthermore, the...

Optimal Control for Precision Irrigation of a Large‐Scale Plantation

Distributing water optimally is a complex problem that many farmers face yearly, especially in times of drought. In this work, we propose optimization‐based feedback control to improve crop yield and water productivity in agriculture irrigation for a plantation consisting of multiple fields. The interaction between soil, water, crop (sugarcane...

Efficient freeway MPC by parameterization of ALINEA and a speed-limited area

Freeway congestion can reduce the freeway throughput due to the capacity drop or due to blocking caused by spillback to upstream ramps. Research has shown that congestion can be reduced by the application of ramp metering and variable speed limits. Model predictive control is a promising strategy for the optimization of the ramp metering...

Model-based predictive control for bicycling in urban intersections

In this paper, a model predictive control approach for improving the efficiency of bicycling as part of intermodal transportation systems is proposed. Considering a dedicated bicycle lanes infrastructure, the focus in this paper is to optimize the dynamic interaction between bicycles and vehicles at the multimodal urban traffic intersections. In...

Time-Varying Scheme for Non-Centralized Model Predictive Control of Large-scale Systems

The Non-Centralized Model Predictive Control (NC-MPC) framework refers in this paper to any distributed, hierarchical, or decentralized model predictive controller (or a combination of them) the structure of which can change over time and the control actions of which are not obtained based on a centralized computation. Within this framework, we...

A non-iterative cascaded predictive control approach for control of irrigation canals

Irrigation canals transport water from water sources (such as large rivers and lakes) to water users (such as farmers). Irrigation canals are typically very large in nature, covering vast geographical areas, and involving a significant number of control actuators, such as pumps, gates, and locks. The control of such canals is aimed at...

Multi-Area Predictive Control for Combined Electricity and Natural Gas Systems

The optimal operation of an integrated electricity and natural gas system is investigated. The couplings between these two systems are modeled by energy hubs, which serve as interface between the loads and the transmission infrastructures. Previously, we have applied a distributed control scheme to a static three-hub benchmark system. In this...

Coordinated model predictive reach control for irrigation canals

Irrigation canals are large-scale systems, covering vast geographical areas, and consisting of many interconnected canal reaches that interact with control structures such as pumps and gates. The control of such irrigation canals is usually done in a manual way, in which a human operator travels along the irrigation canal to adjust the settings...

Model-based predictive control applied to multi-carrier energy systems

The optimal operation of an integrated electricity and natural gas infrastructure is investigated. The couplings between the electricity system and the gas system are modeled by so-called energy hubs, which represent the interface between the loads on the one hand and the transmission infrastructures on the other. To increase reliability and...

Model predictive control of fuel cell micro cogeneration systems

With the increasing application of distributed energy resources and information technologies in the electricity infrastructure, innovative possibilities for incorporating the demand side more actively in power system operation are enabled. At the residential level energy costs could be reduced with intelligent price-based control concepts ...

Economic advantages of applying model predictive control to distributed energy resources: The case of micro-CHP systems

The increasing presence of distributed energy resources, information and intelligence in the electricity infrastructure increases the possibilities for larger economic efficiency of power systems. This work shows the possible cost advantages of applying a model predictive control (MPC) strategy to residential energy systems. MPC can take future...

Multi-agent model predictive control for transportation networks with continuous and discrete elements

For several reasons, control of transportation networks like road traffic networks, power transmission networks, water distribution networks, etc. has to be done using a multi-agent control approach. We consider a multi-agent control approach in which each control agent uses Model Predictive Control (MPC) to determine its actions. Each control...

Challenges for process system engineering in infrastructure operation and control

The need for improving the operation and control of infrastructure systems has created a demand on optimization methods applicable in the area of complex sociotechnical systems operated by a multitude of actors in a setting of decentralized decision making. This paper briefly presents main classes of optimization models applied in PSE system...

Multi-agent model predictive control for transportation networks: Serial versus parallel schemes

We consider the control of large-scale transportation networks, like road traffic networks, power distribution networks, water distribution networks, etc. Control of these networks is often not possible from a single point by a single intelligent control agent; instead control has to be performed using multiple intelligent agents. We consider...

Experience-based model predictive control using reinforcement learning

Model predictive control (MPC) is becoming an increasingly popular method to select actions for controlling dynamic systems. TraditionallyMPC uses a model of the system to be controlled and a performance function to characterize the desired behavior of the system. The MPC agent finds actions over a finite horizon that lead the system into a...