Comparing Approaches to Distributed Control of Fluid Systems based on Multi-Agent Systems
Conventional control of fluid systems does not consider system-wide knowledge for optimising energy efficient operation. Distributed control of fluid systems combines reliable local control of components while using system-wide cooperation to ensure energy efficient operation. The presented work compares three approaches to distributed control based on multi-agent systems, distributed model predictive control (DMPC), multi-agent deep reinforcement learning (MADRL) and market mechanism design. These approaches were applied to a generic fluid system and evaluated with regard to functionality, energy efficient operation, modeling effort, reliability in the face of disruptions, and transparency of control decisions. All approaches were shown to fulfil the functionality, though a trade-off between functional quality and energy efficiency was identified. Increased modeling effort was shown to improve the performance slightly while a strong interdependence of information caused by excessive information sharing has proven to be disadvantageous. DMPC and partially observable MADRL were less sensitive to disruptions than market mechanism. In conclusion, agent-based control of fluid systems achieves greater energy efficiency than conventional methods, with values similar to centralized optimal control and thus represent a viable design approach of fluid system control.
Code (0)
등록된 구현이 없습니다.
Tasks
Deep Reinforcement LearningModel Predictive ControlSimilar Papers 제목 키워드 기반
Interpretable and Efficient Data-driven Discovery and Control of Distributed Systems
Effectively controlling systems governed by Partial Differential Equations (PDEs) is crucial in several fields of Applied Sciences and Engineering. These systems usually yield significant challenges to conventional contr…
Dimensionality ReductionReinforcement Learning (RL)Deep Reinforcement Learning for Online Control of Stochastic Partial Differential Equations
In many areas, such as the physical sciences, life sciences, and finance, control approaches are used to achieve a desired goal in complex dynamical systems governed by differential equations. In this work we formulate t…
Deep Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Distributed Design of Ultra Large-Scale Control Systems: Progress, Challenges, and Prospects
The transition from large centralized complex control systems to distributed configurations that rely on a network of a very large number of interconnected simpler subsystems is ongoing and inevitable in many application…
Deep Dynamical Modeling and Control of Unsteady Fluid Flows
The design of flow control systems remains a challenge due to the nonlinear nature of the equations that govern fluid flow. However, recent advances in computational fluid dynamics (CFD) have enabled the simulation of co…
Model Predictive ControlDynamic modeling and predictive control of a microfluidic system
Microfluidics, the study of fluids in microscopic channels, has led to important advances in fields as diverse as microelectronics, biotechnology and chemistry. Microfluidic research is primarily based on the use of micr…