A Novel Multi‐Step Model Predictive Control for Multi‐Input Systems

Zhang, Langwen; Wang, Jingcheng

doi:10.1002/asjc.901

Cited by 8 publications

(6 citation statements)

References 21 publications

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“…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 . 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%

“…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. 25 This paper develops an LMI-based CDMPC for constrained Lipschitz nonlinear systems. The control input for each subsystem is computed in parallel, exploiting all states of the system, while minimizing a copy of the global infinite horizon objective function.…”

Section: Introductionmentioning

confidence: 99%

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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%

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

Adelipour

Haeri

2019

Optim Control Appl Methods

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“…Another effective method is networked predictive control (NPC), which can actively compensate for the delay and packet dropouts . The main idea of predictive control is to predict the future control inputs of the system, and then select the corresponding control input according to the current information of the delay and packet dropouts .…”

Section: Introductionmentioning

confidence: 99%

Wide‐area Stabiliser on Sliding Mode Control for Cross‐area Power Systems with Random Delay and Packet Dropouts

Chen

2018

Asian Journal of Control

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In recent years, with the development of different types of new energy sources, cross-area power systems (CAPS), whose purpose is to maintain reasonable distribution and power sharing among different areas, have gained great attention. In this paper, aimed at CAPS with random delay and packet dropouts in the process of data transmission, a novel wide-area stabiliser on sliding mode control (SMC) is designed. Such stabilisers mainly includes two parts: the design of the wide-area controller and compensation of the communication constraints. First, a new sliding mode switching function is proposed for designing the wide-area controller. Then, the networked predictive control (NPC) algorithm based on the designed controller is used to compensate the network-induced delay and packet dropouts in CAPS. Furthermore, the stability of the close-loop error system is proved. Finally, one three-area power system is employed to demonstrate the effectiveness of the proposed methods. The results verify that the stabiliser designed in this paper is reasonable and effective.

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“…Model Predictive Control (MPC), as one of the advanced control methods [1,2], has made considerable applications in complex industrial processes such as oil refining, chemical, metallurgical, power [3,4], machining processes [5][6][7], and complex systems such as artificial pancreas (AP) systems [8]. It is noted that the MPC algorithms can meet design requirements with good performance during the early operation period.…”

Section: Introductionmentioning

confidence: 99%

Data‐Driven Performance Monitoring for Model Predictive Control Using a mahalanobis distance based overall index

Zhang

et al. 2018

Asian Journal of Control

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This paper proposes a data-driven approach for model predictive control (MPC) performance monitoring. It explores the I/O data of the MPC system. First, to evaluate the MPC performance and capture the fluctuation of the process variables, we present an overall performance index based on Mahalanobis distance (MDBI) with its deduced benchmark. The Mahalanobis distance can better characterize the change of the process variable in both principal component space and residual space. As the proper vectors of the two spaces are orthogonal, the MDBI eliminates the correlation between the process variables while considering the variables' characteristics in both spaces simultaneously, which helps evaluate the MPC performance more effectively with fewer monitoring parameters. Furthermore, for the MPC performance diagnosis, we use the MDBI as inputs and construct a support vector machine (SVM) pattern classifier. The classifier can achieve a higher accuracy when recognizing four common performance degradation patterns and determine the root cause of performance degradation. The results of simulations on the Wood-Berry distillation column process and experiments on NIAT multifunctional experiment platform illustrate the effectiveness of the proposed performance assessment/diagnosis strategies.

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A Novel Multi‐Step Model Predictive Control for Multi‐Input Systems

Cited by 8 publications

References 21 publications

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

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

Wide‐area Stabiliser on Sliding Mode Control for Cross‐area Power Systems with Random Delay and Packet Dropouts

Data‐Driven Performance Monitoring for Model Predictive Control Using a mahalanobis distance based overall index

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