Reconfigurable distributed model predictive control

Tippett, Michael James; Bao, Jie

doi:10.1016/j.ces.2015.01.040

Cited by 12 publications

(8 citation statements)

References 37 publications

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“…8,15−18 It is because that it not only inherits the virtues of widely appreciated centralized Model Predictive Control (MPC), e.g., explicitly handling constraints, good optimization performance, etc., 19−23 but also has the advantages of distributed control, e.g., structural flexibility, good fault tolerance ability, etc. 8,15,17,18,24 Many DMPC methods have been proposed in the literature; DMPCs designed for addressing different types of problems. In refs 15, 18, and 27, the authors proposed a DMPC to address the problem of topological change in the existing control schemes.…”

Section: ■ Introductionmentioning

confidence: 99%

Data-Driven Distributed Model Predictive Control of Continuous Nonlinear Systems with Gaussian Process

Zheng

Zhang

et al. 2022

Ind. Eng. Chem. Res.

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This work explores the design of distributed model predictive control (DMPC) systems using Gaussian process (GP) models to predict the nonlinear dynamic behavior for nonlinear processes with unknown dynamics. Specifically, the DMPC is designed and analyzed concerning closed-loop stability and performance properties based on the Lyapunov techniques. First, the GP model used in the DMPC is developed and updated in a distributed manner where each subsystem only considers its physically interacted states except its own states to get a sufficiently accurate model with a relatively smaller data set and achieve efficient real-time computation time. Then, a Lyapunov constraint, which is related to the model mismatch quantified by the GP model, is developed to guarantee the stability of the proposed DMPC system at a given confidence level. Meanwhile, a mechanism for triggering the update of the GP’s data set and the Lyapunov constraint is proposed that keeps the recursive feasibility of the DMPC system and the improvement of the steady-state performance. Finally, using an ethylbenzene production process as an example, the simulation results demonstrate the effectiveness of the proposed DMPC system.

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Section: ■ Introductionmentioning

confidence: 99%

Data-Driven Distributed Model Predictive Control of Continuous Nonlinear Systems with Gaussian Process

Zheng

Zhang

et al. 2022

Ind. Eng. Chem. Res.

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“…The coupling dynamics are taken into account in the form of time-varying terminal sets. In [12], by characterizing the local controller supply rates as linear functions of all possible network interconnection topologies, local controllers can reconfigure their local dissipativity properties to account for any known interconnection topology changes. In [13], the effect of subsystems joining or leaving the overall system is analyzed based on dissipativity property to give a reconfiguration strategy that ensures the overall stability during closed-loop operation.…”

Section: Introductionmentioning

confidence: 99%

Distributed model predictive control with guaranteed performance for reconfigurable power flow systems based on passivity

You

2020

Asian Journal of Control

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“…For example, the design of DMPC for nonlinear systems [15,16], DMPC for uncertain systems [15,17], DMPC for networked systems with time delay [18], a decentralized optimization algorithm for solving DMPC [19], the design of cooperative strategies for improving the performance of DMPC [20], the design of an event-based communication DMPC for reducing the load on the communication network [21], as well as the design of a DPMC control structure [22]. Among these algorithms, several DMPC algorithms relate to the purpose of improving the closed-loop optimization performance while considering the information connectivity [5,21,[23][24][25][26]. Information connectivity is considered because it directly affects the structural flexibility and error tolerance ability.…”

Section: Introductionmentioning

confidence: 99%

“…This method is able to improve the flexibility and fault tolerance ability of the control network [37]. References [25,37] proposed reconfigurable DMPC and plug-and-play DMPC based on dissipative theory, which focus on the problem of how to design a DMPC which allows the addition or deletion of subsystems without any change in existing controllers. It can be seen that the optimization performance of the entire system and structural flexibility are two conflicting key points in DMPC design.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Strong Neighbor-Based Optimization for Distributed Model Predictive Control Systems

Gao

Zheng

2018

Mathematics

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This paper considers a class of large-scale systems which is composed of many interacting subsystems, and each of them is controlled by an individual controller. For this type of system, to improve the optimization performance of the entire closed-loop system in a distributed framework without the entire system's information or too-complicated network information, connectivity is always an important topic. To achieve this purpose, a distributed model predictive control (DMPC) design method is proposed in this paper, where each local model predictive control (MPC) considers the optimization performance of its strong coupling subsystems and communicates with them. A method to determine the strength of the coupling relationship based on the closed-loop system's performance and subsystem network connectivity is proposed for the selection of each subsystem's neighbors. Finally, through integrating the steady-state calculation, the designed DMPC is able to guarantee the recursive feasibility and asymptotic stability of the closed-loop system in the cases of both tracking set point and stabilizing system to zeroes. Simulation results show the efficiency of the proposed DMPC.

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Reconfigurable distributed model predictive control

Cited by 12 publications

References 37 publications

Data-Driven Distributed Model Predictive Control of Continuous Nonlinear Systems with Gaussian Process

Data-Driven Distributed Model Predictive Control of Continuous Nonlinear Systems with Gaussian Process

Distributed model predictive control with guaranteed performance for reconfigurable power flow systems based on passivity

Enhancing Strong Neighbor-Based Optimization for Distributed Model Predictive Control Systems

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