Estimation of Large-Scale Multi-Rate Systems ⋆

model predictive control and estimation of large-scal

Robust stability conditions for remote SISO DMC controller in networked control systems

A two level hierarchy is employed in the design of Networked Control Systems (NCSs) with bounded random transmission delay. At the lower level a local controller is designed to stabilize the plant. At the higher level a remote controller with the Dynamic Matrix Control (DMC) algorithm is implemented to regulate the desirable set-point for the local controller. The conventional DMC algorithm is not applicable due to the unknown transmission delay in NCSs. To meet the requirements of a networked environment, a new remote DMC controller is proposed in this study. Two methods, maximum delayed output feedback and multi-rate sampling, are used to cope with the delayed feedback sensory data. Under the assumption that the closed-loop local system is described by one FIR model of an FIR model family, the robust stability problem of the remote DMC controller is investigated. Applying Jury's dominant coefficient lemma and some stability results of switching discrete-time systems with multiple delays; several stability criteria are obtained in the form of simple inequalities. Finally, some numerical simulations are given to demonstrate the theoretical results

A distributed model predictive control strategy for back-to-back converters

In recent years Model Predictive Control (MPC) has been successfully used for the control of power electronics converters with different topologies and for different applications. MPC offers many advantages over more traditional control techniques such as the ability to avoid cascaded control loops, easy inclusion of constraint and fast transient response. On the other hand, the controller computational burden increases exponentially with the system complexity and may result in an unfeasible realization on modern digital control boards. This paper proposes a novel Distributed Model Predictive Control, which is able to achieve the same performance of the classical Model Predictive Control whilst reducing the computational requirements of its implementation. The proposed control approach is tested on a AC/AC converter in a back-to-back configuration used for power flow management. Simulation results are provided and validated through experimental testing in several operating conditions

Non-centralized control for flow-based distribution networks: a game-theoretical insight

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/This paper solves a data-driven control problem for a flow-based distribution network with two objectives: a resource allocation and a fair distribution of costs. These objectives represent both cooperation and competition directions. It is proposed a solution that combines either a centralized or distributed cooperative game approach using the Shapley value to determine a proper partitioning of the system and a fair communication cost distribution. On the other hand, a decentralized non-cooperative game approach computing the Nash equilibrium is used to achieve the control objective of the resource allocation under a non-complete information topology. Furthermore, an invariant-set property is presented and the closed-loop system stability is analyzed for the non-cooperative game approach. Another contribution regarding the cooperative game approach is an alternative way to compute the Shapley value for the proposed specific characteristic function. Unlike the classical cooperative-games approach, which has a limited application due to the combinatorial explosion issues, the alternative method allows calculating the Shapley value in polynomial time and hence can be applied to large-scale problems.Peer ReviewedPostprint (author's final draft

Mitigation of communication failures in distributed model predictive control strategies

Information sharing among local controllers is the key feature of any Distributed Model Predictive Control (DMPC) strategy. This paper addresses the problem of communication failures in DMPC strategies and proposes a distributed solution to cope with them. The proposal consists in an information-exchange protocol that is based on distributed projection dynamics. By applying this protocol as a complementary plug-in to a DMPC strategy, the controllers improve the resilience against communication failures and relax the requirements of the communication network. Furthermore, a reconfiguration algorithm, which is a contingency procedure to maintain the connectivity of the network, and a discussion on the selection criteria of the information-sharing network are also presented. In order to demonstrate the performance and advantages of the proposed approach when it is applied to a DMPC strategy, a case study of a power-network control problem is provided.Peer ReviewedPostprint (author's final draft

Coordinating Multiple Model Predictive Controllers for Large-scale systems

Ph.DDOCTOR OF PHILOSOPH

Multi-objective model-free control based on population dynamics and cooperative games

This work solves a model-free resource allocation problem with two objectives. These objectives represent both cooperation and competition directions. It is proposed a solution that combines a centralized cooperative game approach using the Shapley value to determine a proper partitioning of the system, and a decentralized non-cooperative game approach using the Nash equilibrium to achieve the control objective by means of both partial and local information. Furthermore, invariant set and stability analysis are discussed for the noncooperative game approach. Another contribution regarding the cooperative game approach relies on a novel and alternative way to compute the Shapley value for the chosen characteristic function. This alternative computational way is proposed in order to mitigate the commonly high computational burden issue, which is associated to the combinatorial explosion associated to the cooperative game approach.Peer ReviewedPostprint (author's final draft