Coupling Degree Clustering-Based Distributed Model Predictive Control Network Design

Zheng, Yongjun; Wei, Yongsong; Li, Shaoyuan

doi:10.1109/tase.2017.2780444

Cited by 50 publications

(26 citation statements)

References 35 publications

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“…With the awareness of the distinctions among the affections of different strength of couples, Zheng et al proposed a method for systems with strong coupling subsystems, which clusters strong connected subsystems and controls each cluster with iterative cooperative DMPC; the coordination among clusters is conducted by noniterative DMPC. It focuses on the design of stabilized DMPC with nonglobal communication for systems with strong couplings.…”

Section: Introductionmentioning

confidence: 99%

Enhanced information reconfiguration for distributed model predictive control for cyber‐physical networked systems

Wei

Zheng

2019

Intl J Robust & Nonlinear

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Summary This paper considers a class of cyber‐physical networked systems, which are composed of many interacted subsystems, and are controlled in a distributed framework. The operating point of each subsystem changes with the varying of working conditions or productions, which may cause the change of the interactions among subsystems correspondingly. How to adapt to this change with good closed‐loop optimization performance and appropriate information connections is a problem. To solve this problem, the impaction of a subsystem's control action on the performance of related closed‐loop subsystems is first deduced for measuring the coupling among subsystems. Then, a distributed model predictive control (MPC) for tracking, whose subsystems online reconfigure their information structures, is proposed based on this impaction index. When the operating points changed, each local MPC calculates the impaction indices related to its structural downstream subsystems. If and only if the impaction index exceeds a defined bound, its behavior is considered by its downstream subsystem's MPC. The aim is to improve the optimization performance of entire closed‐loop systems and avoid the unnecessary information connections among local MPCs. Besides, contraction constraints are designed to guarantee that the overall system converges to the set points. The stability analysis is also provided. Simulation results show that the proposed impaction index is reasonable along with the efficiency of the proposed distributed MPC.

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Section: Introductionmentioning

confidence: 99%

Enhanced information reconfiguration for distributed model predictive control for cyber‐physical networked systems

Wei

Zheng

2019

Intl J Robust & Nonlinear

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“…In the holistic approach to achieve industrial energy reduction, the clustering analysis has been shown as a very useful tool to understand the operating conditions for a sub-process in the production line [20]. Numerous clustering methods have been investigated in industrial applications.…”

Section: Introductionmentioning

confidence: 99%

Analysis of energy saving potentials in intelligent manufacturing: A case study of bakery plants

Wang

Gan

et al. 2019

Energy

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To address the global challenge of the climate change, more strict legislations worldwide on carbon emission reductions have put energy intensive industries under immense pressure to improve the energy efficiency. Due to the lack of technical support and financial incentives, a range of technical and economic barriers still exist for small-medium enterprises (SMEs). This paper first introduces a point energy technology, which is developed for SMEs to improve the insight of the energy usage in the manufacturing processes and installed in a local bakery. Statistical analysis of electricity consumption data over a sevenday period is conducted, including the identification of operational modes for individual processing units using an enhanced clustering method and the voltage unbalance conditions associated with these identified modes. Two technical strategies, namely electrical load allotment and voltage unbalance minimisation, are then proposed, which could attain more than 800 kwh energy saving during this period and the current unbalance could be reduced to less than 10%. In

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“…At each control interval an MPC algorithm computes the future control actions by minimizing an objective function over a finite prediction horizon according to the historical information and future response of the process model [3]. This type of control algorithm has the ability to handle multiple interacting variables, constraints, large delay and complex dynamic processes [4].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Method to Assess MPC Performance Based on Multi-Step Slow Feature Analysis

et al. 2019

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Due to the wide application of model predictive control (MPC) in industrial processes, the assessment of MPC performance is essential to ensure product quality and improve energy efficiency. Recently, the slow feature analysis (SFA) algorithm has been successfully applied to assess the performance of MPC. However, the disadvantage of the traditional SFA-based predictable index is that it can only extract one-step predictable information in the monitored variables. In order to better mine the predictable information contained in the monitored variables with large lag, an enhanced method to assess MPC performance based on multi-step SFA (MSSFA) is proposed. Based on the relationship between the slowness of slow features (SFs) and data predictability, an MSSFA model SFA(τ) is built through extending the temporal derivatives of the SFs from one step to multiple steps to extract multi-step predictable information in the monitored variables, which is used to construct a multi-step predictable index. Then, the predictable information in the SFs is further extracted for enhancing the multi-step predictable index to improve its sensitivity to performance changes. The effectiveness of the proposed method has been verified through two process simulation examples.

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Coupling Degree Clustering-Based Distributed Model Predictive Control Network Design

Cited by 50 publications

References 35 publications

Enhanced information reconfiguration for distributed model predictive control for cyber‐physical networked systems

Enhanced information reconfiguration for distributed model predictive control for cyber‐physical networked systems

Analysis of energy saving potentials in intelligent manufacturing: A case study of bakery plants

An Enhanced Method to Assess MPC Performance Based on Multi-Step Slow Feature Analysis

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