Probability Density Function Control for Stochastic Nonlinear Systems using Monte Carlo Simulation

Zhang, Qichun; Wang, Hong

doi:10.1016/j.ifacol.2020.12.1857

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…For example, [13] presents a vector-based converted distance metric to achieve data-driven PDF tracking control. Similarly, the data-driven PID controller was obtained based on the PDF vectorisation in [14]. To further simplify the expression of the distance, a histogram-based pseudostate description has been developed using the Monte-Carlo simulation [15].…”

Section: Pdf Control With Euclidean Distancementioning

confidence: 99%

Recent Advances in Non-Gaussian Stochastic Systems Control Theory and Its Applications

Zhang

Zhou

2022

IJNDI

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Survey/review study Recent Advances in Non-Gaussian Stochastic Systems Control Theory and Its Applications Qichun Zhang 1, and Yuyang Zhou 2,* 1 Department of Computer Science, University of Bradford, Bradford BD7 1DP, United Kingdom 2 School of Computing, Engineering and the Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT, United Kingdom * Correspondence: y.zhou@napier.ac.uk Received: 24 October 2022 Accepted: 18 November 2022 Published: 22 December 2022 Abstract: Non-Gaussian randomness widely exists in complex dynamical systems, in which the traditional mean-variance index cannot fully reflect the systematic characteristics. To improve the performance of control design subjected to non-Gaussian noises, stochastic distribution control (SDC) theory was proposed in the 1990s, where the output probability density function (PDF) has been investigated as an additional system variable. Following this framework, SDC has been extended to other research subjects in control systems such as filter design, fault diagnosis, and so on. It shows that SDC supplies an important solution to enhance the accuracy of system design, which is further beneficial to almost all the topics subject to non-Gaussian randomness. Meanwhile, the theoretical results of the SDC have been applied to several practical industrial applications. As data science raises based on the development of industrial artificial intelligence, SDC has been further developed recently focusing on data-driven design and multi-agent systems. To explore the new challenges with the evolution of SDC, e.g. unknown system models, unknown noise distributions, strong non-stationary transient dynamics, stability analysis and industrial applications, this survey summarises the most recent published results in the last 5 years of SDC work in terms of modelling, control, filtering, fault diagnosis, and industrial applications. Based on the technical analysis, potential future work is discussed in the end.

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Section: Pdf Control With Euclidean Distancementioning

confidence: 99%

Recent Advances in Non-Gaussian Stochastic Systems Control Theory and Its Applications

Zhang

Zhou

2022

IJNDI

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“…This increases the computational load for the realization of SDC in practice, and further effort is therefore needed to investigate how effective computation can be obtained. For example, the recent study on using data‐driven approach with Monte Carlo simulation showed a promising result for stochastic system control in this perspective 104 …”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

Survey on stochastic distribution systems: A full probability density function control theory with potential applications

Wang

2021

Optim Control Appl Methods

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Complex systems seen either in general engineering practice or economics are subjected to ever increased uncertainties that are mostly represented as random variables or parameters, and the characteristics of random variables are represented by their probability density functions (PDFs). Controlling their PDFs means to shape their stochastic distributions and in general it would provide a full treatment for system analysis and operational control and optimization. This leads to the development of stochastic distribution control (SDC) systems theory in the past decades, where the original aim of the controller design is to realize a shape control of the distributions of certain random variables in their PDFs sense for some engineering processes. Indeed, once the PDFs of these random variables or parameters are used to describe their distribution characters, the control task is to obtain control signals so that the output PDFs of stochastic systems are made to follow their target PDFs. The subject of SDC was initially originated for non-Gaussian stochastic control systems design but has found a wide spectrum of applications in general systems in terms of data-driven modeling, analysis, signal processing (filtering), data mining via multivariable statistics, decision-making (optimization) for systems subjected to uncertainties and even in economics. In this context, SDC constitutes an effective primer tool for complex system analysis, control and operational optimizations. In this review paper, a detailed survey of the developments on the research of SDC systems will be made together with their wide spectrum applications and future perspectives.

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State Estimation for Stochastic Non-linear Systems via Backstepping Design

Zhang

Yin

2021

2021 26th International Conference on Automation and Computing (ICAC)

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Probability Density Function Control for Stochastic Nonlinear Systems using Monte Carlo Simulation

Cited by 4 publications

References 10 publications

Recent Advances in Non-Gaussian Stochastic Systems Control Theory and Its Applications

Recent Advances in Non-Gaussian Stochastic Systems Control Theory and Its Applications

Survey on stochastic distribution systems: A full probability density function control theory with potential applications

State Estimation for Stochastic Non-linear Systems via Backstepping Design

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