Risk-Sensitive Optimal Control
E695667
Risk-Sensitive Optimal Control is a foundational work in control theory that develops methods for designing controllers that explicitly account for uncertainty and variability in system performance.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Risk-Sensitive Optimal Control canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T7853059 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Risk-Sensitive Optimal Control Context triple: [Peter Whittle, notableWork, Risk-Sensitive Optimal Control]
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A.
Introduction to Stochastic Control Theory
Introduction to Stochastic Control Theory is a foundational textbook that systematically develops the theory and methods for controlling dynamical systems under uncertainty using probabilistic and stochastic-process tools.
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B.
Foundations of a General Theory of Sequential Decision Functions
Foundations of a General Theory of Sequential Decision Functions is a seminal work in statistics that established the mathematical foundations of sequential analysis and optimal decision-making under uncertainty.
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C.
Hamilton’s maximum principle
Hamilton’s maximum principle is a fundamental analytical tool in geometric analysis that extends the classical maximum principle to tensor-valued quantities, playing a key role in studying the behavior of solutions to the Ricci flow and related geometric evolution equations.
-
D.
Pontryagin maximum principle
The Pontryagin maximum principle is a fundamental result in optimal control theory that provides necessary conditions for an optimal control process by characterizing optimal trajectories via a Hamiltonian maximization condition.
-
E.
Mathematical Theory of Optimal Processes
Mathematical Theory of Optimal Processes is a foundational work in control theory that systematically develops the mathematical principles of optimal control, including what is now known as Pontryagin’s maximum principle.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Risk-Sensitive Optimal Control Target entity description: Risk-Sensitive Optimal Control is a foundational work in control theory that develops methods for designing controllers that explicitly account for uncertainty and variability in system performance.
-
A.
Introduction to Stochastic Control Theory
Introduction to Stochastic Control Theory is a foundational textbook that systematically develops the theory and methods for controlling dynamical systems under uncertainty using probabilistic and stochastic-process tools.
-
B.
Foundations of a General Theory of Sequential Decision Functions
Foundations of a General Theory of Sequential Decision Functions is a seminal work in statistics that established the mathematical foundations of sequential analysis and optimal decision-making under uncertainty.
-
C.
Hamilton’s maximum principle
Hamilton’s maximum principle is a fundamental analytical tool in geometric analysis that extends the classical maximum principle to tensor-valued quantities, playing a key role in studying the behavior of solutions to the Ricci flow and related geometric evolution equations.
-
D.
Pontryagin maximum principle
The Pontryagin maximum principle is a fundamental result in optimal control theory that provides necessary conditions for an optimal control process by characterizing optimal trajectories via a Hamiltonian maximization condition.
-
E.
Mathematical Theory of Optimal Processes
Mathematical Theory of Optimal Processes is a foundational work in control theory that systematically develops the mathematical principles of optimal control, including what is now known as Pontryagin’s maximum principle.
- F. None of above. chosen
Statements (34)
| Predicate | Object |
|---|---|
| instanceOf |
book
ⓘ
scholarly monograph ⓘ |
| addresses |
model uncertainty in control systems
ⓘ
performance robustness ⓘ trade-off between performance and risk ⓘ |
| aimsTo |
improve reliability of controlled systems
ⓘ
incorporate risk preferences into control design ⓘ limit probability of poor performance outcomes ⓘ |
| appliesTo |
linear systems with noise
ⓘ
nonlinear stochastic systems ⓘ stochastic dynamical systems ⓘ |
| contributionTo |
robust control methodologies
ⓘ
theory of stochastic optimal control ⓘ |
| develops |
control laws that account for performance variability
ⓘ
methods for risk-sensitive controller design ⓘ optimization criteria that penalize risk ⓘ |
| field |
control theory
ⓘ
optimal control ⓘ stochastic control ⓘ |
| focusesOn |
design of controllers under uncertainty
ⓘ
risk-sensitive control ⓘ uncertainty in system performance ⓘ variability in system performance ⓘ |
| influenced |
applications in engineering systems
ⓘ
applications in finance and economics ⓘ subsequent research in risk-aware control ⓘ |
| isDescribedAs |
foundational work in risk-sensitive control theory
ⓘ
framework for optimal control under uncertainty ⓘ |
| relatedTo |
H-infinity control
ⓘ
risk-averse decision making ⓘ robust optimal control ⓘ |
| usesConcept |
exponential-of-integral performance index
ⓘ
risk-sensitive cost function ⓘ stochastic dynamic programming ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Risk-Sensitive Optimal Control Description of subject: Risk-Sensitive Optimal Control is a foundational work in control theory that develops methods for designing controllers that explicitly account for uncertainty and variability in system performance.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.