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#controltheory

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Manuel Baltieri

2025 is looking like a great year for work at the intersection of , , , and , this thread will be a very biased collection of works (in no specific order) I'm hoping to read as soon as possible!

Starting with:
"Logical Aspects of Virtual Double Categories"
mastoxiv.page/@arXiv_mathCT_bo

mastoxivarXiv math.CT bot (@arXiv_mathCT_bot@mastoxiv.page)Logical Aspects of Virtual Double Categories Hayato Nasu https://arxiv.org/abs/2501.17869 https://arxiv.org/pdf/2501.17869 https://arxiv.org/html/2501.17869 arXiv:2501.17869v1 Announce Type: new Abstract: This thesis deals with two main topics: virtual double categories as semantics environments for predicate logic, and a syntactic presentation of virtual double categories as a type theory. One significant principle of categorical logic is bringing together the semantics and the syntax of logical systems in a common categorical framework. This thesis is intended to propose a double-categorical method for categorical logic in line with this principle. On the semantic side, we investigate virtual double categories as a model of predicate logic and illustrate that this framework subsumes the existing frameworks properly. On the syntactic side, we develop a type theory called FVDblTT that is designed as an internal language for virtual double categories.
Continued thread
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Kernel Bob

You could think of a tach signal as a pulse density modulation. Each clock period is an implicit sample. If the tach is ticking at 1/200,000 the clock speed, then for 200,000 consecutive clocks, there are 0 samples, then there's a single 1 sample.

So I can run that through a low pass filter and get a very low amplitude speed signal? Are there feasible digital filters that work when the cutoff frequency is orders of magnitude below the sample rate?

#DSP#ControlTheory#PDM
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Kernel Bob

DSP and control theory tooters, how do you smooth a tach signal?

I'm measuring a sequence of tachometer pulses, and I want to turn that into a smoothed current speed number. Everything I know about DSP uses a fixed sampling interval, but these aren't fixed -- the interval is the signal.

This has to be a well known problem with well known solutions...

Thank you.

#DSP#ControlTheory#Tachometer
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ModConFlex

ModConFlex' first researchers started around August 2023. Our presently 13 #MSCA researchers are coming from fields of #engineering, #physics and/or #mathematics. They are hosted by eight different #universities for their PhD and meet at least once a year at our annual network and training events.

Their research focuses on a range of topics from #ArtificialIntelligence over #ControlTheory (applied to floating #WindTurbines or, more theoretical, port Hamiltonian systems) to #FlexibleAircraft.

World map showing the countries of origin of our 13 researchers and their 8 host universities within the ModConFlex MSCA project.
Public
ModConFlex

Last 2024 publication within the #ModConFlex project:

"LQR control for a system describing the interaction between a floating solid and the surrounding fluid" on #mathematics of #solid moving on the #sea and #control acting on the solid.

Co-authored by #ModConFlex #MSCA researcher Zhuo Xu and published in Mathematical Control and Related Fields:

aimsciences.org//article/doi/1

Congratulations to Zhuo for his first #publication !

#controltheory

#offshorewind
#floatingwindfarms

Mathematical Control and Related FieldsLQR control for a system describing the interaction between a floating solid and the surrounding fluidThis paper studies an infinite time horizon LQR optimal control problem for a system describing, within a linear approximation, the vertical oscillations of a floating solid, coupled with the motion of the free boundary fluid on which it floats. The fluid flow is described by a viscous version of the linearized Saint-Venant equations (shallow water regime). The major difficulty we face is that the domain occupied by the fluid is unbounded so that the system is not exponentially stable. This raises challenges in proving the wellposedness, requiring the combined use of analytic semigroup theory and an interpolation technique. The main contribution of this paper is that, in spite of the lack of exponential stabilizability, we could define a wellposed LQR problem for which a Riccati-based approach to design feedback controls can be implemented.
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Jef Allbright

A couple decades ago it occurred to me that the dynamics of disagreement often tend toward polarization rather than toward unification at a higher level of understanding.

Further, it seemed (to me) that this might be analogous to the behavior of a feedback loop out of control and going to one or the other of its extreme rails.

And further, this seemed (to me) to be the behavior of a system finding its lowest energy/information state, associated with lack of requisite variety.

This behavior seemed (to me) generalizable in principle across systems based on electronics, mechanisms, …, human individuals, and groups.

At that time I was active in a forum of Very Smart People, and when I ventured this observation/opinion it received no interest or engagement.

I'm wondering whether now, twenty years later, with polarization in politics being grossly apparent, whether anyone reading this might be able to recommend some sources of more extensive thinking on the question of such a general principle…?

#agreement#disagreement#polarization
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Dominic Boutet

#arxivfeed :

"Langevin algorithms for Markovian Neural Networks and Deep Stochastic control"
arxiv.org/abs/2212.12018

arXiv.orgLangevin algorithms for Markovian Neural Networks and Deep Stochastic controlStochastic Gradient Descent Langevin Dynamics (SGLD) algorithms, which add noise to the classic gradient descent, are known to improve the training of neural networks in some cases where the neural network is very deep. In this paper we study the possibilities of training acceleration for the numerical resolution of stochastic control problems through gradient descent, where the control is parametrized by a neural network. If the control is applied at many discretization times then solving the stochastic control problem reduces to minimizing the loss of a very deep neural network. We numerically show that Langevin algorithms improve the training on various stochastic control problems like hedging and resource management, and for different choices of gradient descent methods.
#MachineLearning#DeepLearning#ControlTheory
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Johannes Brust

Better late then never: #introduction
I'm a researcher at the German Research Center for Artificial Intelligence (@DFKI) interested in #ReinforcementLearning, #ControlTheory and #Robotics. My career so far included: Selling popcorn, cabinet making, mechanical engineering, database programming, mechatronics, controller design, reinforcement learning and robotics.
Looking forward to some nice and interesting discussions on any of these topics!

Public
Dominic Boutet

#arxivfeed :

"Intrinsic Motivation in Dynamical Control Systems"
arxiv.org/abs/2301.00005

#DynamicalSystems
#ControlTheory #MachineLearning #ArtificialIntelligence

arXiv.orgIntrinsic Motivation in Dynamical Control SystemsBiological systems often choose actions without an explicit reward signal, a phenomenon known as intrinsic motivation. The computational principles underlying this behavior remain poorly understood. In this study, we investigate an information-theoretic approach to intrinsic motivation, based on maximizing an agent's empowerment (the mutual information between its past actions and future states). We show that this approach generalizes previous attempts to formalize intrinsic motivation, and we provide a computationally efficient algorithm for computing the necessary quantities. We test our approach on several benchmark control problems, and we explain its success in guiding intrinsically motivated behaviors by relating our information-theoretic control function to fundamental properties of the dynamical system representing the combined agent-environment system. This opens the door for designing practical artificial, intrinsically motivated controllers and for linking animal behaviors to their dynamical properties.
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