Multirate Lyapunov-based distributed model predictive control of nonlinear uncertain systems

Heidarinejad, Mohsen; Liu, Jinfeng; Peña, David Muñoz de la; Davis, James F.; Christofides, Panagiotis D.

doi:10.1016/j.jprocont.2011.07.016

Cited by 41 publications

(24 citation statements)

References 23 publications

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“…Multirate control schemes are quite popular as they increase the flexibility in the quest for the desired properties (stability, optimality, constraints satisfaction) [13][14][15]. A multi-rate control approach is adopted in this paper with a quantification of the effect that the ratio of the two sampling rates has on the control of the system.…”

Section: Motivation and Backgroundmentioning

confidence: 99%

“…The maximal positive invariant set Ω(0) for the centralised control system was computed in 0.60s and its minimal representation comprised 12 linear inequalities. The associated MILPs P i N as in (15) were solved offline in 2.12s for subsystem 1 and 2.27s for subsystem 2 on average. The corresponding centralised computation required 6.33s on average.…”

Section: Centralised Versus Decentralised Controlmentioning

confidence: 99%

See 1 more Smart Citation

Decentralised Hierarchical Multi-rate Control of Large-Scale Drinking Water Networks

Sampathirao

Sopasakis

Bemporad

2016

Critical Information Infrastructures Security

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Abstract. We propose a decentralised hierarchical multi-rate control scheme for the control of large-scale systems with state and input constraints. The large-scale system is partitioned into sub-systems each one of which is locally controlled by a stabilizing linear controller which does not account for the prescribed constraints. A higher-level controller commands reference signals at a lower uniform sampling frequency so as to enforce linear constraints on the process variables. Worst-case subsystem interactions are modeled and accounted for in a robust manner. By optimally constraining the magnitude and rate of variation of the reference signals to each lower-level controller we prove that closed-loop stability is preserved and the fulfillment of the prescribed constraints is guaranteed. We apply the proposed methodology for the decentralised control of a quadraple-tank system, known as Johansson's system and we compare it to a centralised control approach.

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Section: Motivation and Backgroundmentioning

confidence: 99%

Section: Centralised Versus Decentralised Controlmentioning

confidence: 99%

Decentralised Hierarchical Multi-rate Control of Large-Scale Drinking Water Networks

Sampathirao

Sopasakis

Bemporad

2016

Critical Information Infrastructures Security

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“…Many developments in this area have been summarized in the recent monograph [26] . This is a flexible framework which allows for flat and tiered controller structures [24,61] , as well as asynchronous sensor information [62] , and process networks with multiple time-scales [63,64] . Additionally, this approach has been extended to allow for disruptions (e.g., noise or data losses) in the controller communication, by implementing a system which determines if the information received by a controller is reliable or not [65] .…”

Section: Cooperative Dmpcmentioning

confidence: 99%

Distributed control of chemical process networks

Tippett

Bao

2015

Int. J. Autom. Comput.

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Abstract:In this paper, we present a review of the current literature on distributed (or partially decentralized) control of chemical process networks. In particular, we focus on recent developments in distributed model predictive control, in the context of the specific challenges faced in the control of chemical process networks. The paper is concluded with some open problems and some possible future research directions in the area.

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“…A multirate system is labeled by the existence of multiple non-uniform sampling rates in terms of an overall period denoted by T for system operation, namely, a non-uniformly sampled-data system (NUS), which has been widely practiced in many industrial and chemical applications [2][3][4][5][6]. In the past two decades, multirate systems have been increasingly studied for model identification and control design [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…Ding et al [16] proposed a state-space identification algorithm for dual-rate systems by using a hierarchical identification strategy, and subsequently, derived a recursive state-space identification algorithm based on the recursive least-squares (RLS) approach for multirate sampling systems [17]. Despite the superiority of convergence and accuracy, the RLS algorithm [17] involved with relatively high computation complexity of 3 () On (where n is the number of sampled data), in comparison with the QR decomposition-based RLS (QRD-RLS) identification methods [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Recursive state-space identification of non-uniformly sampled-data systems using QR decomposition

Hou

Liu

Wang

2014

Proceeding of the 11th World Congress on Intelligent Control and Automation

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-A recursive least-squares (LS ) state-space identification method based on the QR decomposition is proposed for non-uniformly sampled-data systems. Both cases of measuring all states and only the output(s) are considered for model identification. For the case of state measurement, a QR decomposition-based recursive LS (QRD-RLS ) identification algorithm is given to estimate the state matrices. For the case of only output measurement, another identification algorithm is developed by combining the QRD-RLS approach with a hierarchical identification strategy. Both algorithms can guarantee fast convergence rate with low computation complexity. An illustrative example is shown to demonstrate the effectiveness of the proposed methods.

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Multirate Lyapunov-based distributed model predictive control of nonlinear uncertain systems

Cited by 41 publications

References 23 publications

Decentralised Hierarchical Multi-rate Control of Large-Scale Drinking Water Networks

Decentralised Hierarchical Multi-rate Control of Large-Scale Drinking Water Networks

Distributed control of chemical process networks

Recursive state-space identification of non-uniformly sampled-data systems using QR decomposition

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