Stochastic Control
Stochastic Control - Related Videos
09:36
Direct Stochastic Optical Reconstruction Microscopy of Extracellular Vesicles in Three Dimensions
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Cited by 8 •
2021
Direct stochastic optical reconstruction microscopy (dSTORM) is used to bypass the typical diffraction limit of light microscopy and to view exosomes at the nanometer scale. It can be employed in both two and three dimensions to characterize exosomes.
14:23
Imaging Intermediate Filaments and Microtubules with 2-dimensional Direct Stochastic Optical Reconstruction Microscopy
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Cited by 6 •
2018
The overall goal of this methodology is to give the optimal experimental conditions from sample preparation to image acquisition and reconstruction in order to perform 2D dual color dSTORM images of microtubules and intermediate filaments in fixed cells...
00:06
Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro
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Cited by 5 •
2019
Galvanic vestibular stimulation in humans exhibits improvements in vestibular function. However, it is unknown how these effects occur. Here, we describe how to apply sinusoidal and stochastic electrical noise and evaluate appropriate stimulus amplitudes in individual medial vestibular nucleus neurons in the C57BL/6 mouse.
01:21
Control System Problem
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2024
In an open-loop system, such as a basic thermostat, the poles of the transfer function influence the system's response but do not determine its stability. However, when feedback is introduced to form a closed-loop system, such as an advanced thermostat that adjusts heating based on room temperature, stability is governed by the new poles of the closed-loop transfer function. When forming a closed-loop system, issues can arise if the poles cross into the unstable region, leading to potential...
14:09
High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip
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Cited by 7 •
2019
Chip-based super-resolution optical microscopy is a novel approach to fluorescence microscopy and offers advantages in cost effectiveness and throughput. Here, the protocols for chip preparation and imaging are shown for TIRF microscopy and localization-based super-resolution microscopy.
FAQs
Bellman equations connect a current decision with the future performance expected after that decision. Dynamic programming uses this relationship to evaluate alternative actions over time, while a feedback policy converts observed system information into the selected control. This structure lets decisions respond to changing conditions rather than relying on one fixed action sequence.
Stochastic processes provide a mathematical way to represent changing uncertainty, while observations supply the information available to the controller. When information is incomplete, the policy must base actions on what has been observed rather than on perfect knowledge of the system. This connection explains why sensing and feedback are central to applying stochastic control in engineering systems.
The essential distinction from deterministic control is that random influences affect both the system’s evolution and the quality of decisions. Consequently, performance is assessed using a defined measure that can include expected results, risk, and constraints. This framing is useful when an action that performs well on average may still create unacceptable variability or violate operating limits.
A practical formulation begins by specifying the system, its stochastic processes, available observations, control actions, and performance measure. The designer then uses dynamic programming and Bellman equations to derive or analyze a feedback policy. Finally, expected performance, risk, and constraint compliance can be considered together, allowing the selected strategy to be judged under the uncertainty represented by the model.
Engineers can apply stochastic control when resource allocation, estimation, motion planning, or system operation must continue despite noise or changing conditions. Representative settings include robots, autonomous vehicles, communication networks, and power grids. In each case, feedback decisions connect available observations with control actions while accounting for uncertain behavior and defined performance requirements.
Its main practical value is a policy for choosing actions over time, together with a way to judge the resulting performance under uncertainty. In engineering, that supports reliable operation rather than optimization based only on a known situation. The framework can inform local decisions, such as motion or allocation, and broader operation of interconnected systems such as networks or grids.