Decentralized receding horizon control for large scale dynamically decoupled systems

In this paper we consider a linear system structured into physically coupled subsystems and propose an innovative distributed control scheme capable to guarantee asymptotic stability and satisfaction of constraints on system inputs and states. Our method hinges on the availability of a decentralized stabilizing regulator for the unconstrained system and provides a two-layer controller for each subsystem. Upper controllers receive planned state trajectories from neighboring subsystems and exploit the notion of tubes [12] for achieving robustness of stability with respect to coupling. Lower controllers generate planned trajectories using Model Predictive Control (MPC) independently of the other subsystems. The proposed control scheme is arguably easier to design and apply than existing distributed controllers with similar features. A comparison of the proposed approach with existing centralized and distributed MPC regulators is conducted using an illustrative example.

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“…As an example, most existing decentralized MPC schemes [9,14,8,23,1] rely on the assumption of small coupling between subsystems.…”

Section: Introductionmentioning

confidence: 99%

Tube-based distributed control of linear constrained systems

Riverso

Ferrari-Trecate

2012

Automatica

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“…A recent collection of existing algorithms [1] listed 35 different approaches. In addition, recent works on the topic include as significant examples [2], [3], [4], [10], [13], [5], [7], [8]. While many applications can be considered, such as the control of power networks [6] or electrical vehicle charging control [9], this article concentrates its attention on the control of water delivery canals.…”

Section: A Motivation and Reviewmentioning

confidence: 99%

D-ADMM based distributed MPC with input-output models

Costa

Lemos

Mota

2014

2014 IEEE Conference on Control Applications (CCA)

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Abstract-This article presents a distributed model predictive controller (MPC) based on linear models that use input/output plant data and D-ADMM optimization. The use of input/output models has the advantage of not requiring a Kalman filter to estimate the plant state. The D-ADMM algorithm solves the optimization problem associated to a cost function that is the sum of the control agents private costs, being a modification of the Alternating Direction of Multipliers (ADMM) algorithm that requires no central node and implies a significant reduction in the communication among adjacent nodes. The distributed MPC is obtained for the special case of a linear graph. An application to distributed control of a water delivery canal is presented to illustrate the algorithm.

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“…Thus, two research projects (HD-MPC and WIDE) are currently being carried out in Europe, both focused on the development of decentralised and distributed MPC techniques. Few works have been recently published in this area; see, e.g., [5,6,7,8,9,10], among others. However, there is a prior problem to be solved: the system decomposition into subsystems.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical and decentralised model predictive control of drinking water networks: application to Barcelona case study

Ocampo‐Martínez

Barcelli

Puig

et al. 2012

IET Control Theory Appl.

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In this paper, a hierarchical and decentralised model predictive control (DMPC) strategy for drinking water networks (DWN) is proposed. The DWN is partitioned in a set of subnetworks using a partitioning algorithm that makes use of the topology of the network, historic information about the actuator usage and heuristics. A suboptimal DMPC strategy was derived, which consists in a set of MPC controllers, whose prediction model is a plant partition, where each element solves its control problem in a hierarchical order. A comparative simulation study between centralised MPC (CMPC) and DMPC approaches is developed using a case study, which consists in an aggregate version of the Barcelona DWN. Results have shown the effectiveness of the proposed DMPC approach in terms of the scalability of computations with an acceptable admissible loss of performance in all the considered scenarios.

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Decentralized receding horizon control for large scale dynamically decoupled systems

Cited by 356 publications

References 19 publications

Tube-based distributed control of linear constrained systems

Tube-based distributed control of linear constrained systems

D-ADMM based distributed MPC with input-output models

Hierarchical and decentralised model predictive control of drinking water networks: application to Barcelona case study

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