Distributed Model Predictive Control for Reconfigurable Systems With Network Connection

Hou, Bei; Li, Shaoyuan; Zheng, Yongjun

doi:10.1109/tase.2021.3058298

Cited by 16 publications

(7 citation statements)

References 31 publications

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“…A DMPC-based coalitional control strategy is developed, in which the reconfiguration of the communication network is jointly decided depending on local string stability criteria. This is different from the DMPC algorithm in [30], where the network topology changes by inserting or removing certain agents, or the robust, min-max DMPC algorithm in [27,28], in which the coalitions are formed when the local feasibility of the optimization problem is lost due to the fact that a terminal constraint is not fulfilled.…”

Section: Introductionmentioning

confidence: 99%

Coalitional Distributed Model Predictive Control Strategy with Switching Topologies for Multi-Agent Systems

Maxim,

Pauca,

Caruntu

2024

Electronics

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Controlling multi-agent systems (MASs) has attracted increased interest within the control community. Since the control challenge consists of the fact that each agent has limited local capabilities, our adopted solution is tailored so that a group of such entities works together and shares resources and information to fulfill a given task. In this work, we propose a coalitional control solution using the distributed model predictive control (DMPC) framework, suitable for a multi-agent system. The methodology has a switching mechanism that selects the best communication topology for the overall system. The proposed control algorithm was validated in simulation using a homogeneous vehicle platooning application with longitudinal dynamics. The available communication topologies were specifically tailored taking into account the information flow between adjacent vehicles. The obtained results show that when the platoon’s string stability is risked, the algorithm switches between different communication topologies. The resulting coalitions between vehicles ensure an increase in the overall stability of the entire system and prove the efficacy of our proposed methodology.

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Section: Introductionmentioning

confidence: 99%

Coalitional Distributed Model Predictive Control Strategy with Switching Topologies for Multi-Agent Systems

Maxim,

Pauca,

Caruntu

2024

Electronics

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“…The MPC applies the first element of this optimal control sequence to the plant. Many MPC algorithms have been designed for different systems. − Recently, to design MPC involving machine learning-based or data-driven models has been a topic of interest. ,− Considering that there exists an unknown mismatch between the estimated states and the real states when updating the model online, how to keep the MPC feasible and the closed-loop system stable with enhanced performance is one of the problems in machine learning-based MPC design.…”

Section: Introductionmentioning

confidence: 99%

Implementable Stability Guaranteed Lyapunov-Based Data-Driven Model Predictive Control with Evolving Gaussian Process

Zhang

Zheng

2022

Ind. Eng. Chem. Res.

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In this paper, we integrate the blueonline-updating Gaussian Process (GP) models into the Model Predictive Control (MPC) design of nonlinear systems with time-varying dynamics to obtain a more accurate data-based prediction of state, and more relaxed constraints. Specifically, considering that the GP inferred from the Bayesian framework provides both the predictive value and the estimation of uncertainties, a mechanism of updating the predictive model and the stability constraint based on the Lyapunov techniques is designed according to the online estimation of the mismatch between the prediction by GP and that of the real system. It keeps the feasibility of the designed MPC system. Besides, the stability of the closed-loop system is proven to be guaranteed under a certain probability. Applying the designed method to a chemical process shows the efficiency of the proposed data-driven MPC.

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“…To control large-scale networked systems, the distributed model predictive control (DMPC), which coordinates multiple MPCs that communicate to calculate the optimal input trajectories in a distributed manner, has been an important content of collaborative networked control system. ,− 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., − but also has the advantages of distributed control, e.g., structural flexibility, good fault tolerance ability, etc. ,,,, …”

Section: Introductionmentioning

confidence: 99%

“…Besides, there were many DMPCs designed for addressing different types of problems. In refs , , and , the authors proposed a DMPC to address the problem of topological change in the existing control schemes. Some DMPCs, e.g.…”

Section: Introductionmentioning

confidence: 99%

“…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%

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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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Distributed Model Predictive Control for Reconfigurable Systems With Network Connection

Cited by 16 publications

References 31 publications

Coalitional Distributed Model Predictive Control Strategy with Switching Topologies for Multi-Agent Systems

Coalitional Distributed Model Predictive Control Strategy with Switching Topologies for Multi-Agent Systems

Implementable Stability Guaranteed Lyapunov-Based Data-Driven Model Predictive Control with Evolving Gaussian Process

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

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