Learning-based model predictive control for Markov decision processes

We propose the use of Model Predictive Control (MPC) for controlling systems described by Markov decision processes. First, we consider a straightforward MPC algorithm for Markov decision processes. Then, we propose value functions, a means to deal with issues arising in conventional MPC, e.g., computational requirements and sub-optimality of...

Multi-Agent Model Predictive Control of Transportation Networks

We consider multi-agent, or distributed, control of transportation networks, like traffic, water, and power networks. These networks typically have a large geographical span, modular structure, and a large number of components that require control. We discuss the necessity of a multi-agent control setting in which multiple agents control parts...

Efficient implementation of serial multi-agent model predictive control by parallelization

We discuss an extension of a scheme recently proposed for multi-agent control of large-scale networks, like power networks, road traffic networks, water networks, etc. The original scheme uses serial sequences of agent interactions that under some assumptions make agents locally choose actions that are globally optimal. However, some weaknesses...

Least-cost model predictive control of residential energy resources when applying ?mCHP

With an increasing use of distributed energy resources and intelligence in the electricity infrastructure, the possibilities for minimizing costs of household energy consumption increase. Technology is moving toward a situation in which households manage their own energy generation and consumption, possibly in cooperation with each other. As a...

Supervisory hybrid model predictive control for voltage stability of power networks

Emergency voltage control problems in electric power networks have stimulated the interest for the implementation of online optimal control techniques. Briefly stated, voltage instability stems from the attempt of load dynamics to restore power consumption beyond the capability of the transmission and generation system. Typically, this situation...

Adaptive prediction model accuracy in the control of residential energy resources

With the increasing use of distributed energy resources and intelligence in the electricity infrastructure, the possibilities for minimizing costs of household energy consumption increase. Technology is moving toward a situation in which automated energy management systems could control domestic energy generation, storage, and consumption. In...

Coordination in urban water supply networks using distributed model predictive control

Urban water supply networks are large-scale systems that transport potable water over vast geographical areas to millions of consumers. A safe and efficient operation of these networks is crucial, as without it living in today’s cities would be impossible. To achieve an adequate operation, these networks are equipped with actuators like pumps...

Linking water and energy objectives in lowland areas through the application of model predictive control

Unlike mountainous areas, lowland areas have limited potential to generate energy from water flows. Instead, for water systems in lowland areas that continuously need to pump water out of the system, the focus should be set to saving energy in the present water management. This paper gives an introduction into possibilities to manage water...

Cooperative distributed predictive control for collision-free vehicle platoons

The rapidly developing computing and communication technologies improve the autonomy of individual vehicles on the one hand and facilitate the coordination among vehicles on the other. In the context of dynamic speed management, this study considers a platoon of intelligent vehicles that are required to maintain desired inter-vehicle spaces and...