Decision-Making for Complex Systems Subjected to Uncertainties—A Probability Density Function Control Approach

Wang, Aiping; Wang, Hong

doi:10.1007/978-3-030-60990-0_23

Cited by 2 publications

(4 citation statements)

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“…For example, if the cost function is the standard one used in a LQG, the optimal control should be designed to ensure that the PDF of the cost function is made as narrow and as left as possible as shown in Figure 12. This would obviously minimize the cost function mean while ensuring a robust and reliable optimization effect 88,89 …”

Section: Influence To a Boarder Engineering Sciencesmentioning

confidence: 99%

“…There is a strong incentive for the control of particle populations in particulate processes. 23,46,[86][87][88][89][90] For instance, in processes that involve particle size distribution (PSD), the control of PSD (1) enables an inferential control of end-use properties that cannot be always measured or characterized and (2) facilitates the complex and non-convex end-use property control problem by providing a guarantee for the identification of a control move that would lead to the desired end-use property.…”

Section: Traffic Flow Control Issuesmentioning

confidence: 99%

“…This would obviously minimize the cost function mean while ensuring a robust and reliable optimization effect. 88,89 Additionally, the PDF shaping-based optimization can be linked with Bayesian optimization, 90 where one can formulate the cost function PDF based on its surrogate model and then use the PDF shape optimization instead to carry out the subsequent optimization exercises.…”

Section: Help In Stochastic Optimizationmentioning

confidence: 99%

“…The most common examples of distributed particulate processes are polymerization processes that involve a distribution of chain lengths of the polymeric entities; crystallization and granulation processes that involve a size distribution of the crystals; and dispersed phase systems including emulsions that involve a particle/droplet size distribution. There is a strong incentive for the control of particle populations in particulate processes 23,46,86–90 . For instance, in processes that involve particle size distribution (PSD), the control of PSD (1) enables an inferential control of end‐use properties that cannot be always measured or characterized and (2) facilitates the complex and non‐convex end‐use property control problem by providing a guarantee for the identification of a control move that would lead to the desired end‐use property.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Influence To a Boarder Engineering Sciencesmentioning

confidence: 99%