SIP-Based Single Neuron Stochastic Predictive Control for Non-Gaussian Networked Control Systems with Uncertain Metrology Delays

Zhao, Yalan; Ren, Ming; Cheng, Lan; Gong, Mingyue

doi:10.3390/e20070494

Cited by 3 publications

(2 citation statements)

References 33 publications

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“…Similarly, the iterative learning design for non-Gaussian stochastic system has also been given by Zhou, Yue, Zhang, and Wang (2014). To avoid the weights of mean-value and entropy, survival information potential (SIP) is used to replace the MEE criterion which has been introduced by Xu, Zhao, Ren, Cheng, and Gong (2018).…”

Section: Data-based Pdf Controlmentioning

confidence: 99%

An introductory survey of probability density function control

Ren

Zhang

2019

Systems Science & Control Engineering

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Probability density function (PDF) control strategy investigates the controller design approaches where the random variables for the stochastic processes were adjusted to follow the desirable distributions. In other words, the shape of the system PDF can be regulated by controller design.Different from the existing stochastic optimization and control methods, the most important problem of PDF control is to establish the evolution of the PDF expressions of the system variables. Once the relationship between the control input and the output PDF is formulated, the control objective can be described as obtaining the control input signals which would adjust the system output PDFs to follow the pre-specified target PDFs. Motivated by the development of data-driven control and the state of the art PDF-based applications, this paper summarizes the recent research results of the PDF control while the controller design approaches can be categorized into three groups: (1) system model-based direct evolution PDF control; (2) model-based distribution-transformation PDF control methods and (3) data-based PDF control. In addition, minimum entropy control, PDF-based filter design, fault diagnosis and probabilistic decoupling design are also introduced briefly as extended applications in theory sense.

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Section: Data-based Pdf Controlmentioning

confidence: 99%

An introductory survey of probability density function control

Ren

Zhang

2019

Systems Science & Control Engineering

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“…R 1 and R 2 are weights that correspond to generalized correntropy and control inputs. can be obtained using the oversampling method [28], i.e. dividing the time period, from time instant k to the next time instant k+1, into N time periods for oversampling.…”

Section: A Generalized Correntropy Based Performance Indexmentioning

confidence: 99%

Generalized Correntropy Predictive Control for Waste Heat Recovery Systems Based on Organic Rankine Cycle

et al. 2019

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Organic Rankine cycle (ORC) is one of the most promising technologies to recover energy from low temperature waste heat. The heat sources usually experience fluctuations in temperature and mass flow rate, which makes it difficult to obtain satisfactory control performances of ORC systems. In this paper, a single neuron adaptive multi-step predictive control scheme is developed for an ORC based waste heat recovery (WHR) system with non-Gaussian disturbances. Since the non-Gaussian disturbances existed in WHR system follow heavy-tailed distribution, generalized correntropy is adopted as the performance index to characterize the system uncertainties. The weights of the single neuron controller are updated by optimizing the performance function. The whole implementation procedures of the proposed control strategy for the WHR are presented. As a contrast, the performances of the minimum error entropy (MEE) based controller and the mean square error (MSE) based controller are also tested. The proposed control scheme is confirmed to be more effective through simulations. INDEX TERMS ORC, waste heat recovery, generalized correntropy, single neuron adaptive multi-step predictive control, non-Gaussian.

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Two‐layer dynamic economic nonlinear model predictive control for a lithium‐ion battery charge process with random disturbances

Guo,

Ren,

Zhang

et al. 2024

Can J Chem Eng

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The charging process of lithium‐ion batteries is necessary for normal operation, and improper lithium‐ion battery charging strategy can cause side reactions, significant temperature rise, performance degradation, and safety concerns. This paper proposes a two‐layer dynamic economic nonlinear model predictive control economic model predictive control (EMPC) method for lithium‐ion battery charge management in which the non‐Gaussian random noise of voltage and current signal are taken into account. In the two‐layer EMPC, the upper layer finds the optimal reference trajectory, and the power in the future prediction horizon is chosen as an economic indicator to optimize the upper layer. The lower layer tracks the optimal trajectory, and model predictive control based on generalized correntropy is used. The economic cost of the system and the dynamic changes of the process are considered to greatly reduce the computational complexity and realize rapid battery charge management. Finally, the simulation results show that the tracking error of the two‐layer EMPC method based on generalized correntropy is stable around 0, while the tracking error of the two‐layer EMPC method based on MSE is stable around 0.003. The average control action times of the proposed two‐layer EMPC and single‐layer EMPC are 0.0052 and 0.0267 s, respectively. It is verified that the proposed method performs better for the lithium‐ion battery charging process with random disturbances.

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SIP-Based Single Neuron Stochastic Predictive Control for Non-Gaussian Networked Control Systems with Uncertain Metrology Delays

Cited by 3 publications

References 33 publications

An introductory survey of probability density function control

An introductory survey of probability density function control

Generalized Correntropy Predictive Control for Waste Heat Recovery Systems Based on Organic Rankine Cycle

Two‐layer dynamic economic nonlinear model predictive control for a lithium‐ion battery charge process with random disturbances

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