Robust distributed model predictive control of linear systems with structured time-varying uncertainties

Zhang, Langwen; Xie, Wei; Wang, Jingcheng

doi:10.1080/00207179.2016.1250163

Cited by 17 publications

(14 citation statements)

References 25 publications

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“…To avoid this limitation, cooperative distributed schemes can be considered. Therefore, LMI‐based CDMPC algorithms are proposed for linear systems . Al‐Gherwi et al have derived CDMPC for linear systems with polytopic uncertainty, which incorporates the information of all states of other subsystems in computing each local input while optimizing a global objective function.…”

Section: Introductionmentioning

confidence: 99%

“…Since only the inputs of the system are actually decomposed and all states are fed back to each subsystem, this approach is more practical to control systems, which have no proper decomposition in states or have strong interaction among subsystems. A similar approach has been also developed for linear systems with state and input time delays, linear systems with actuator saturation, and linear systems with structured uncertainty . To reduce the computational demand of the method, a dual‐mode approach is suggested to transfer most of the needed computations to the offline part .…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, LMI-based CDMPC algorithms are proposed for linear systems. [20][21][22][23] Al-Gherwi et al 20 have derived CDMPC for linear systems with polytopic uncertainty, which incorporates the information of all states of other subsystems in computing each local input while optimizing a global objective function. Since only the inputs of the system are actually decomposed and all states are fed back to each subsystem, this approach is more practical to control systems, which have no proper decomposition in states or have strong interaction among subsystems.…”

Section: Introductionmentioning

confidence: 99%

“…A similar approach has been also developed for linear systems with state and input time delays, 21 linear systems with actuator saturation, 22 and linear systems with structured uncertainty. 23 To reduce the computational demand of the method, a dual-mode approach is suggested to transfer most of the needed computations to the offline part. 24 The other remedy to obtain a less computationally complex design is to remove the inner iterations in the cooperative scheme and optimize only one or some of the inputs at each sample time.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

LMI‐based cooperative distributed model predictive control for Lipschitz nonlinear systems

Adelipour

Haeri

2019

Optim Control Appl Methods

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Summary In this paper, a distributed model predictive control is proposed to control Lipschitz nonlinear systems. The cooperative distributed scheme is considered where a global infinite horizon objective function is optimized for each subsystem, exploiting the state and input information of other subsystems. Thus, each control law is obtained separately as a state feedback of all system's states by solving a set of linear matrix inequalities. Due to convexity of the design, convergence properties at each iteration are established. Additionally, the proposed algorithm is modified to optimize only one control input at a time, which leads to a further reduction in the computation load. Finally, two application cases are studied to show the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

LMI‐based cooperative distributed model predictive control for Lipschitz nonlinear systems

Adelipour

Haeri

2019

Optim Control Appl Methods

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“…It is, therefore, of significance to propose effective MPC strategies to guarantee robustness against parameter uncertainties, which give rise to robust MPC (RMPC) strategies. Extensive research results have been reported in the literature . It is worth noting that most existing MPC strategies, including the aforementioned ones, are based on the assumption that system states are entirely measurable.…”

Section: Introductionmentioning

confidence: 99%

Resilient RMPC for polytopic uncertain systems under TOD protocol: A switched system approach

Zhu

Song

Ding

2018

Intl J Robust & Nonlinear

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Summary This paper addresses the resilient robust model predictive control (RMPC) problem for a class of polytopic uncertain systems under the try‐once‐discard (TOD) protocol. At each transmission instant, only one sensor node obtains the privilege to access the shared communication network in order to prevent the data from collision. The measurements transmission order of the sensor nodes is orchestrated, and a switched system is constructed according to the underlying TOD protocol. The aim of this paper is to design a set of desired resilient dynamic output feedback controllers in the framework RMPC such that the switched system with the underlying TOD protocol is exponentially stable. By taking the influence of the TOD protocol into account, sufficient conditions are provided to guarantee the recursive feasibility of the RMPC strategy and the stability of the established closed‐loop system in terms of the average dwell time method. Moreover, the effective resilient controllers in RMPC framework are derived by solving an online constrained optimization problem. Finally, a network‐based direct current motor example is utilized to illustrate the effectiveness of the proposed model predictive control strategy.

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Subsystem decomposition of process networks for simultaneous distributed state estimation and control

Yin

Liu

2018

AIChE Journal

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An appropriate subsystem configuration is a prerequisite for a successful distributed control/state estimation design. Existing subsystem decomposition methods are not designed to handle simultaneous distributed estimation and control. In this article, we address the problem of subsystem decomposition of general nonlinear process networks for simultaneous distributed state estimation and distributed control based on community structure detection. A systematic procedure based on modularity is proposed. A fast folding algorithm that approximately maximizes the modularity is used in the proposed procedure to find candidate subsystem configurations. Two chemical process examples of different complexities are used to illustrate the effectiveness and applicability of the proposed approach.State variables in bold cannot be estimated using the available measurements of the corresponding subsystem. "NA" means that no variable is assigned to the corresponding subsystem.

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Robust distributed model predictive control of linear systems with structured time-varying uncertainties

Cited by 17 publications

References 25 publications

LMI‐based cooperative distributed model predictive control for Lipschitz nonlinear systems

LMI‐based cooperative distributed model predictive control for Lipschitz nonlinear systems

Resilient RMPC for polytopic uncertain systems under TOD protocol: A switched system approach

Subsystem decomposition of process networks for simultaneous distributed state estimation and control

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