Partitioning for distributed model predictive control of nonlinear processes

Rocha, Rosiane R.; Oliveira-Lopes, Luís Cláudio; Christofides, Panagiotis D.

doi:10.1016/j.cherd.2018.09.003

Cited by 16 publications

(5 citation statements)

References 9 publications

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“…[1][2][3] In order to deal with more complex systems, more studies have focused on distributed model predictive control (DMPC). [4][5][6] DMPC can deal with strong coupling problems, and solve problems with constraints. So it is applied to the problems of large scale systems in modern industry.…”

Section: Introductionmentioning

confidence: 99%

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Observer‐based event‐triggered distributed model predictive control for a class of nonlinear interconnected systems

Li,

Su,

Guo

2024

Intl J Robust & Nonlinear

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In this paper, an observer‐based event‐triggered distributed model predictive control method is proposed for a class of nonlinear interconnected systems with bounded disturbances, considering unmeasurable states. First of all, the state observer is constructed. It is proved that the observation error is bounded. Second, distributed model predictive controller is designed by using observed value. Meanwhile, the event‐triggered mechanism is set by using the error between the actual output and the predicted output. The setting of event‐triggered mechanism not only ensures the error between the actual output and the predicted output within a certain range, but also reduces the calculation amounts of solving the optimization problem. The states of each subsystem enter the terminal invariant set by distributed model predictive control, and then are stabilized in the invariant set under the action of output feedback control law. In addition, sufficient conditions are given to ensure the feasibility of the algorithm and the stability of the closed‐loop system. Finally, the numerical example is given, and the simulation results verify the effectiveness of the proposed algorithm.

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Section: Introductionmentioning

confidence: 99%

“…Centralized model predictive control (MPC) methods have been unable to deal with such problems 1–3 . In order to deal with more complex systems, more studies have focused on distributed model predictive control (DMPC) 4–6 …”

Section: Introductionmentioning

confidence: 99%

Observer‐based event‐triggered distributed model predictive control for a class of nonlinear interconnected systems

Li,

Su,

Guo

2024

Intl J Robust & Nonlinear

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“…This work contributed to the study of distributed controllers for nonlinear and large‐scale systems. [ 23 ] Jordanou et al proposed a practical NMPC method and applied it to the control of a four‐tank system and an oil production platform, showing its potential. [ 24 ] Wang et al presented a big‐data‐centric predictive control approach for nonlinear chemical processes.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, nonlinear model predictive control (NMPC) has become a new research hotspot. [23][24][25][26] Rocha et al proposed two types of distributed predictive control based on models: noncooperative locally linearized distributed MPC and cooperative locally linearized distributed MPC. This work contributed to the study of distributed controllers for nonlinear and large-scale systems.…”

Section: Introductionmentioning

confidence: 99%

Distributed nonlinear model predictive control for cobalt removal process in zinc hydrometallurgy considering error compensation modelling

Wang

Tang

et al. 2023

Can J Chem Eng

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To address the strong nonlinearity, uncertainty, and mutual coupling in the cobalt removal process of zinc hydrometallurgy, an algorithm based on an improved genetic algorithm (GA) backpropagation (BP) neural network combined with distributed nonlinear model predictive control (NMPC) is proposed. This method was applied to improve the quality of the purification solution and reduce the consumption of zinc powder, overcoming the challenges faced by the current cobalt removal process. First, a synergistic continuously stirred tank reactor (SCSTR) model was constructed for the dynamic cobalt removal process. Second, aiming at the problem that a single SCSTR model has difficulty describing the process accurately, based on the highly nonlinear mapping ability of data‐driven models, a method that organically integrates the SCSTR model and an error compensation model based on the GA‐BP neural network was proposed (GA‐BP‐SCSTR) to provide a more accurate online prediction of the process indicators. Then, a distributed NMPC architecture was developed using the GA‐BP‐SCSTR model, control vector parameterization (CVP) technique, and sequential quadratic programming (SQP) algorithm to achieve the coordinated control of the cobalt removal process. Finally, simulation results of an actual site showed that the prediction accuracy of the GA‐BP‐SCSTR model was higher than those of other models. The proposed predictive control method can maintain the outlet cobalt ion concentrations at the set values while achieving accurate control of the zinc powder addition. This approach can provide guidance for on‐site production and eliminate the blindness of manual experience control.

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“…In the past decade, with the advances of information technology, distributed control system (in particular, distributed model predictive control (DMPC)) has received significant attention and development. DMPC has been widely recognized as the control framework for the next-generation complex processes [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Distributed State Estimation Based Distributed Model Predictive Control

Zeng¹,

Liu²

2021

Mathematics

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In this work, we consider output-feedback distributed model predictive control (DMPC) based on distributed state estimation with bounded process disturbances and output measurement noise. Specifically, a state estimation scheme based on observer-enhanced distributed moving horizon estimation (DMHE) is considered for distributed state estimation purposes. The observer-enhanced DMHE ensures that the state estimates of the system reach a small neighborhood of the actual state values quickly and then maintain within the neighborhood. This implies that the estimation error is bounded. Based on the state estimates provided by the DMHE, a DMPC algorithm is developed based on Lyapunov techniques. In the proposed design, the DMHE and the DMPC are evaluated synchronously every sampling time. The proposed output DMPC is applied to a simulated chemical process and the simulation results show the applicability and effectiveness of the proposed distributed estimation and control approach.

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Partitioning for distributed model predictive control of nonlinear processes

Cited by 16 publications

References 9 publications

Observer‐based event‐triggered distributed model predictive control for a class of nonlinear interconnected systems

Observer‐based event‐triggered distributed model predictive control for a class of nonlinear interconnected systems

Distributed nonlinear model predictive control for cobalt removal process in zinc hydrometallurgy considering error compensation modelling

Distributed State Estimation Based Distributed Model Predictive Control

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