/pdf/convex-analysisnoer-san-nojin-zhan-nituite-5dl2rbmoyr.pdf

Convex Analysisの二,三の進展について

http://pdclab.seas.ucla.edu/Publications/PDChristofides/PDChristofides_RScattolini_DMunozdelaPena_JLiu_CCE_2013_21_DMPC_A_Tutorial_Review_and_Future_Research_Directions.pdf

Distributed model predictive control: A tutorial review and future research directions

Industrial cyber-physical systems (CPSs) are large-scale, geographically dispersed, and life-critical systems, in which lots of sensors and actuators are embedded and networked together to facilitate real-time monitoring and closed-loop control. Their intrinsic features in geographic space and resources put forward to urgent requirements of reliability and scalability for designed filtering or control schemes. This paper presents a review of the state-of-the-art of distributed filtering and control of industrial CPSs described by differential dynamics models. Special attention is paid to sensor networks, manipulators, and power systems. For real-time monitoring, some typical Kalman-based distributed algorithms are summarized and their performances on calculation burden and communication burden, as well as scalability, are discussed in depth. Then, the characteristics of non-Kalman cases are further disclosed in light of constructed filter structures. Furthermore, the latest development is surveyed for distributed cooperative control of mobile manipulators and distributed model predictive control in industrial automation systems. By resorting to droop characteristics, representative distributed control strategies classified by controller structures are systematically summarized for power systems with the requirements of power sharing and voltage and frequency regulation. In addition, distributed security control of industrial CPSs is reviewed when cyber-attacks are taken into consideration. Finally, some challenges are raised to guide the future research.

/pdf/a-survey-on-model-based-distributed-control-and-filtering-oauddcvar8.pdf

A Survey on Model-Based Distributed Control and Filtering for Industrial Cyber-Physical Systems

Model-predictive control (MPC) is an optimization-based control technique that uses 1) a mathematical model of a system to predict the system's behavior over a given horizon, 2) an objective function that represents what system behavior is desirable, 3) a mathematical formalization of operational constraints that have to be satisfied, 4) measurements of the state of the system at each time step, and 5) any information regarding upcoming disturbances that may be available. This article surveyed and categorized 35 distributed MPC approaches. Subsequently, several of the insights gained from the survey were presented. This study provides a picture of what features have received more or less attention over the last years, bringing about potential research niches for new approaches.

Distributed Model Predictive Control: An Overview and Roadmap of Future Research Opportunities

Real traffic data and simulation analysis reveal that for some urban networks a well-defined Macroscopic Fundamental Diagram (MFD) exists, which provides a unimodal and low-scatter relationship between the network vehicle density and outflow. Recent studies demonstrate that link density heterogeneity plays a significant role in the shape and scatter level of MFD and can cause hysteresis loops that influence the network performance. Evidently, a more homogeneous network in terms of link density can result in higher network outflow, which implies a network performance improvement. In this article, we introduce two aggregated models, region- and subregion-based MFDs, to study the dynamics of heterogeneity and how they can affect the accuracy scatter and hysteresis of a multi-subregion MFD model. We also introduce a hierarchical perimeter flow control problem by integrating the MFD heterogeneous modeling. The perimeter flow controllers operate on the border between urban regions, and manipulate the percentages of flows that transfer between the regions such that the network delay is minimized and the distribution of congestion is more homogeneous. The first level of the hierarchical control problem can be solved by a model predictive control approach, where the prediction model is the aggregated parsimonious region-based MFD and the plant (reality) is formulated by the subregion-based MFDs, which is a more detailed model. At the lower level, a feedback controller of the hierarchical structure, tries to maximize the outflow of critical regions, by increasing their homogeneity. With inputs that can be observed with existing monitoring techniques and without the need for detailed traffic state information, the proposed framework succeeds to increase network flows and decrease the hysteresis loop of the MFD. Comparison with existing perimeter controllers without considering the more advanced heterogeneity modeling of MFD highlights the importance of such approach for traffic modeling and control.

Dynamics of heterogeneity in urban networks: aggregated traffic modeling and hierarchical control

http://lup.lub.lu.se/search/ws/files/4027400/3131655.pdf

Accelerated gradient methods and dual decomposition in distributed model predictive control

http://www.dcsc.tudelft.nl/~bdeschutter/pub/rep/10_063.pdf

An iterative scheme for distributed model predictive control using Fenchel's duality

https://www.dcsc.tudelft.nl/~bdeschutter/pub/rep/11_022.pdf

A dual decomposition-based optimization method with guaranteed primal feasibility for hierarchical MPC problems

We present a hierarchical model predictive control approach for large-scale systems based on dual decomposition. The proposed scheme allows coupling in both dynamics and constraints between the subsystems and generates a primal feasible solution within a finite number of iterations, using primal averaging and a constraint tightening approach. The primal update is performed in a distributed way and does not require exact solutions, while the dual problem uses an approximate subgradient method. Stability of the scheme is established using bounded suboptimality.

Minh Dang Doan

Papers

Accelerated gradient methods and dual decomposition in distributed model predictive control

An iterative scheme for distributed model predictive control using Fenchel's duality

A dual decomposition-based optimization method with guaranteed primal feasibility for hierarchical MPC problems

A distributed optimization-based approach for hierarchical model predictive control of large-scale systems with coupled dynamics and constraints

An improved distributed version of Han's method for distributed MPC of canal systems