Top Tweets for #DataDrivenControl
๐ข Call for Papers | Special Issue in Computation
๐ค Guest Editor: Mehran Bahmani
๐
Submission deadline: 31 May 2027
๐ Learn more and submit your manuscript:
https://t.co/o4sh4Alqoc
#Computation #KoopmanOperator #DataDrivenControl

[Series 5 | Machine Learning & Data-Driven Control | #6]
#AutonomousVehicles #ReinforcementLearning #EnergyEfficiency #DataDrivenControl
๐โก How can autonomous driving be made more energy-efficient in urban environments?
This work leverages Proximal Policy Optimization (PPO) to optimize driving behaviors at traffic signals, on-ramps, and in dense traffic, achieving improved energy efficiency.
Title: Design of energy-saving driving strategy based on proximal policy optimization considering urban transport information
Authors: Qifang Liu, Dazhen Sun, Haowen Chen, Dongzi Li & Ping Wang
Full Text: https://t.co/7cD8z3XaOr
![CTT_Journal's tweet photo. [Series 5 | Machine Learning & Data-Driven Control | #6]
#AutonomousVehicles #ReinforcementLearning #EnergyEfficiency #DataDrivenControl
๐โก How can autonomous driving be made more energy-efficient in urban environments?
This work leverages Proximal Policy Optimization (PPO) to optimize driving behaviors at traffic signals, on-ramps, and in dense traffic, achieving improved energy efficiency.
Title: Design of energy-saving driving strategy based on proximal policy optimization considering urban transport information
Authors: Qifang Liu, Dazhen Sun, Haowen Chen, Dongzi Li & Ping Wang
Full Text: https://t.co/7cD8z3XaOr](https://pbs.twimg.com/media/HGyGo-IbMAAkvYf.png)
[Series 5 | Machine Learning & Data-Driven Control | #5]
#NeuralNetworks #ControlSystems #DeepLearning #DataDrivenControl
๐ง โ๏ธ
Can neural networks outperform traditional model-based controllers?
This comparative study shows that feedforward and recurrent neural networks can surpass original analytical controller designs in overall control performance.
๐๐
Title: Application of feedforward and recurrent neural networks for model-based control systems
Authors: Marek Krok, Wojciech P. Hunek, Szymon Mielczarek, Filip Buchwald, Adam Kolender
Full text: https://โhttps://t.co/7V3FXoFgGU
![CTT_Journal's tweet photo. [Series 5 | Machine Learning & Data-Driven Control | #5]
#NeuralNetworks #ControlSystems #DeepLearning #DataDrivenControl
๐ง โ๏ธ
Can neural networks outperform traditional model-based controllers?
This comparative study shows that feedforward and recurrent neural networks can surpass original analytical controller designs in overall control performance.
๐๐
Title: Application of feedforward and recurrent neural networks for model-based control systems
Authors: Marek Krok, Wojciech P. Hunek, Szymon Mielczarek, Filip Buchwald, Adam Kolender
Full text: https://โhttps://t.co/7V3FXoFgGU](https://pbs.twimg.com/media/HGsWe4vXgAALGWR.png)
[Series 5 | Machine Learning & Data-Driven Control | #4]
#NeuralNetworks #OptimalControl #ReinforcementLearning #DataDrivenControl
๐ง โ๏ธ How can we build a self-learning controller for unknown multi-input systems?
This work develops a data-based adaptive neural dynamic programming approach with integral reinforcement, avoiding the need to solve complex HJB equations.
Title: Data-based neural controls for an unknown continuous-time multi-input system with integral reinforcement
Authors: Yongfeng Lv, Jun Zhao, Wan Zhang, Huimin Chang
Full Text: https://t.co/W6sEKJi1Ho
![CTT_Journal's tweet photo. [Series 5 | Machine Learning & Data-Driven Control | #4]
#NeuralNetworks #OptimalControl #ReinforcementLearning #DataDrivenControl
๐ง โ๏ธ How can we build a self-learning controller for unknown multi-input systems?
This work develops a data-based adaptive neural dynamic programming approach with integral reinforcement, avoiding the need to solve complex HJB equations.
Title: Data-based neural controls for an unknown continuous-time multi-input system with integral reinforcement
Authors: Yongfeng Lv, Jun Zhao, Wan Zhang, Huimin Chang
Full Text: https://t.co/W6sEKJi1Ho](https://pbs.twimg.com/media/HGnSuf2XwAAaJmR.png)
[Series 5 | Machine Learning & Data-Driven Control | #3]
#DataDrivenControl #LQR #OptimalControl #MachineLearning
๐๐ฏ Can optimal controllers be designed with far less experimental data?
This work proposes a data-driven LQR method that reduces the required data amount by half, while maintaining strong control performance.
๐ง๐ง๐ง
Title: Reduction of data amount in data-driven design of linear quadratic regulators
Authors: Shinsaku Izumi, Xin Xin
More at: https://t.co/hxlO3AoEBs
![CTT_Journal's tweet photo. [Series 5 | Machine Learning & Data-Driven Control | #3]
#DataDrivenControl #LQR #OptimalControl #MachineLearning
๐๐ฏ Can optimal controllers be designed with far less experimental data?
This work proposes a data-driven LQR method that reduces the required data amount by half, while maintaining strong control performance.
๐ง๐ง๐ง
Title: Reduction of data amount in data-driven design of linear quadratic regulators
Authors: Shinsaku Izumi, Xin Xin
More at: https://t.co/hxlO3AoEBs](https://pbs.twimg.com/media/HGig9lgWkAAH9MS.png)
Sharing our recent work,ย โSCALARFEDLQR: Scalar Federated Learning for Linear Quadratic Regulatorโ, led by my PhD student M. Reza Rostami, with collaboration from Shahriar Talebi (UCLA).
ArXiv link: https://t.co/GlyI3fxZqL
#LQR #DataDrivenControl #FederatedLearning #EmbeddedAI

Fast data-driven iterative learning control for linear system with output disturbance
#DataDrivenControl #ControlTheory #LinearSystems #OutputDisturbance
International Robotics and Automation Awards
Visit Us: https://t.co/tsUXfhL8k8
Nomination:https://t.co/SHfgLi28AO
๐ Distributed & Data-Driven Optimization
CTT Regular Issues 2023โ2024 โ Part II: Advanced Control Theory and Methods
#ControlTheory #Optimization #DistributedSystems #DataDrivenControl
This set of papers formulates control-related problems explicitly in the framework of optimization, covering online decision-making, gradient-free optimization, data-driven LQR design, and sparse model identification under practical constraints.
๐น Distributed regularized online optimization using forwardโbackward splitting (Yuan et al., 2023)
Proposes a distributed forwardโbackward splitting algorithm with fixed and adaptive step sizes, achieving regret bounds comparable to centralized methods.
๐ Full Text: https://t.co/nsJIq2bb3T
๐น Random gradient-free method for online distributed optimization with strongly pseudoconvex cost functions (Yan et al., 2024)
Develops a gradient-free distributed algorithm based on multi-point estimators, with sublinear expected dynamic regret under time-varying objectives.
๐ Full Text: https://t.co/PKvQidFKmd
๐น Reduction of data amount in data-driven design of linear quadratic regulators (Izumi & Xin, 2024)
Introduces a simplified data-driven LQR design that significantly reduces required experimental data, with analysis under measurement noise.
๐ Full Text: https://t.co/Jhucx4au8t
๐น Variable projection algorithms with sparse constraint for separable nonlinear models (Xu et al., 2024)
Presents gradient- and trust-region-based variable projection methods that handle sparsity constraints in separable nonlinear model identification.
๐ Full Text: https://t.co/sV4rEP3WMk
๐ก These papers explore how optimization-based and data-driven methods can be leveraged to address distributed, online, and data-limited control problems.

PhD Student in Automatic Control
๐Linkรถping, Sweden
Apply now: https://t.co/paGnrz09Oh
#PhD #AutomaticControl #Sweden #ResearchHires #DataDrivenControl #Robotics #AutonomousSystems
80โญ๏ธ on a tiny repo on data-driven control I put out during my PhD!
PyDeePC is a tiny Python implementation of Data-enabled Predictive Control. It is model-free, quick to try, easy to learn. Feedback welcome!
https://t.co/xSqEOd6p8b
#MPC #ControlTheory #DataDrivenControl

"Cautious Optimization via Data Informativity," by Jaap Eising; Jorge Cortรฉs
Date: 22 Sept 2025
Link: https://t.co/qgnNgudx3N #optimization #nonlinearsystems #noise #datadrivencontrol #controlsystems #ojcsys

๐ #CallForReading Explore the Special Issue "Power Electronic Converter and Its Control".
๐ https://t.co/47kHiuu2vu
#RobustPredictiveControl #MPC #DataDrivenControl #ModelessControl #PredictiveControl #AIinControl #ArtificialIntelligence
#mdpienergies #openaccess

Data-driven control against false data injection attacks
#DataDrivenControl #SmartSecurity #CyberDefense #AIForCybersecurity
International Robotics and Automation Awards
Visit Us:https://t.co/tsUXfhL8k8
Nomination:https://t.co/SHfgLi28AO
Need to control nonlinear systems from partial data? Our Koopman-based bilinear HMM (trained via neural EM) drives output regulation & MPCโno physics needed. See you at #ACC2025!
#DataDrivenControl #KoopmanOperators #MPC #SciML

#mdpienergies #callforreading
๐ We are happy to invite you to read Special Issue "Intelligent Control for Future Systems".
๐ https://t.co/J2az4Ozz1f
#intelligentcontrol #computationalintelligence #datadrivencontrol #nonlinearcontrolsystems #fractionalordersystems

New data-driven approach controls complex systems without models, using only input-output data. Maximizes stability & handles nonlinear dynamics better than traditional methods. #DataDrivenControl #ComplexSystems #ML
Read more: https://t.co/VFh9rt0f9G
๐ข Tutorial 3 at #ACC2023: Data-Driven Control
Organizers:
๐ธ Anuradha Annaswamy, MIT
๐ธ Yan Wang, Ford Research & Advanced Engineering
Explore the history & recent advances in data-driven control!
#ControlSystems #DataDrivenControl #Engineering
Data-Driven (Reinforcement Learning-Based) Control
https://t.co/Y3NdgKYH0T
#reinforcementlearning #ai #controlsystems #datadrivencontrol #controltheory #industrialengineering #industry40 #ArtificialIntelligence

#Article
Data-Driven Tuning of PID Controlled #PiezoelectricUltrasonicMotor: https://t.co/QfRNb5iNFy
#ultrasonicmotor #PIDcontroller #datadrivencontrol #iterativefeedbacktuning #geneticalgorithm #LuusJaakola #actuators #openaccess
@KITKarlsruhe @MDPIEngineering

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![CTT_Journal's tweet photo. [Series 5 | Machine Learning & Data-Driven Control | #6]
#AutonomousVehicles #ReinforcementLearning #EnergyEfficiency #DataDrivenControl
๐โก How can autonomous driving be made more energy-efficient in urban environments?
This work leverages Proximal Policy Optimization (PPO) to optimize driving behaviors at traffic signals, on-ramps, and in dense traffic, achieving improved energy efficiency.
Title: Design of energy-saving driving strategy based on proximal policy optimization considering urban transport information
Authors: Qifang Liu, Dazhen Sun, Haowen Chen, Dongzi Li & Ping Wang
Full Text: https://t.co/7cD8z3XaOr](https://pbs.twimg.com/media/HGyGo8Ja4AAi9IE.png)
![CTT_Journal's tweet photo. [Series 5 | Machine Learning & Data-Driven Control | #6]
#AutonomousVehicles #ReinforcementLearning #EnergyEfficiency #DataDrivenControl
๐โก How can autonomous driving be made more energy-efficient in urban environments?
This work leverages Proximal Policy Optimization (PPO) to optimize driving behaviors at traffic signals, on-ramps, and in dense traffic, achieving improved energy efficiency.
Title: Design of energy-saving driving strategy based on proximal policy optimization considering urban transport information
Authors: Qifang Liu, Dazhen Sun, Haowen Chen, Dongzi Li & Ping Wang
Full Text: https://t.co/7cD8z3XaOr](https://pbs.twimg.com/media/HGyGo8FW8AAG-wb.png)
![CTT_Journal's tweet photo. [Series 5 | Machine Learning & Data-Driven Control | #5]
#NeuralNetworks #ControlSystems #DeepLearning #DataDrivenControl
๐ง โ๏ธ
Can neural networks outperform traditional model-based controllers?
This comparative study shows that feedforward and recurrent neural networks can surpass original analytical controller designs in overall control performance.
๐๐
Title: Application of feedforward and recurrent neural networks for model-based control systems
Authors: Marek Krok, Wojciech P. Hunek, Szymon Mielczarek, Filip Buchwald, Adam Kolender
Full text: https://โhttps://t.co/7V3FXoFgGU](https://pbs.twimg.com/media/HGsWe3XXIAAOuIS.png)
![CTT_Journal's tweet photo. [Series 5 | Machine Learning & Data-Driven Control | #5]
#NeuralNetworks #ControlSystems #DeepLearning #DataDrivenControl
๐ง โ๏ธ
Can neural networks outperform traditional model-based controllers?
This comparative study shows that feedforward and recurrent neural networks can surpass original analytical controller designs in overall control performance.
๐๐
Title: Application of feedforward and recurrent neural networks for model-based control systems
Authors: Marek Krok, Wojciech P. Hunek, Szymon Mielczarek, Filip Buchwald, Adam Kolender
Full text: https://โhttps://t.co/7V3FXoFgGU](https://pbs.twimg.com/media/HGsWe3OW4AAGEgw.png)
![CTT_Journal's tweet photo. [Series 5 | Machine Learning & Data-Driven Control | #4]
#NeuralNetworks #OptimalControl #ReinforcementLearning #DataDrivenControl
๐ง โ๏ธ How can we build a self-learning controller for unknown multi-input systems?
This work develops a data-based adaptive neural dynamic programming approach with integral reinforcement, avoiding the need to solve complex HJB equations.
Title: Data-based neural controls for an unknown continuous-time multi-input system with integral reinforcement
Authors: Yongfeng Lv, Jun Zhao, Wan Zhang, Huimin Chang
Full Text: https://t.co/W6sEKJi1Ho](https://pbs.twimg.com/media/HGnSuf2WYAAByjS.png)
![CTT_Journal's tweet photo. [Series 5 | Machine Learning & Data-Driven Control | #4]
#NeuralNetworks #OptimalControl #ReinforcementLearning #DataDrivenControl
๐ง โ๏ธ How can we build a self-learning controller for unknown multi-input systems?
This work develops a data-based adaptive neural dynamic programming approach with integral reinforcement, avoiding the need to solve complex HJB equations.
Title: Data-based neural controls for an unknown continuous-time multi-input system with integral reinforcement
Authors: Yongfeng Lv, Jun Zhao, Wan Zhang, Huimin Chang
Full Text: https://t.co/W6sEKJi1Ho](https://pbs.twimg.com/media/HGnSuf2WQAAWcLQ.png)
![CTT_Journal's tweet photo. [Series 5 | Machine Learning & Data-Driven Control | #3]
#DataDrivenControl #LQR #OptimalControl #MachineLearning
๐๐ฏ Can optimal controllers be designed with far less experimental data?
This work proposes a data-driven LQR method that reduces the required data amount by half, while maintaining strong control performance.
๐ง๐ง๐ง
Title: Reduction of data amount in data-driven design of linear quadratic regulators
Authors: Shinsaku Izumi, Xin Xin
More at: https://t.co/hxlO3AoEBs](https://pbs.twimg.com/media/HGig9lgWgAA8FpK.png)














