Top Tweets for #OptimalControl
How to Guide Your Flow: Few-Step Alignment via Flow Map Reward Guidance
👥 Jerry Y. Huang, Justin Lin, Sheel Shah et al.
#AIResearch #MachineLearning #GenerativeModels #OptimalControl
🔗 https://t.co/SWXgLgoN44
[Series 5 | Machine Learning & Data-Driven Control | #4]
#NeuralNetworks #OptimalControl #ReinforcementLearning #DataDrivenControl
🧠⚙️ 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
🧠⚙️ 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)
![CTT_Journal's tweet photo. [Series 5 | Machine Learning & Data-Driven Control | #4]
#NeuralNetworks #OptimalControl #ReinforcementLearning #DataDrivenControl
🧠⚙️ 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
🧠⚙️ 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)
[Series 5 | Machine Learning & Data-Driven Control | #3]
#DataDrivenControl #LQR #OptimalControl #MachineLearning
📉🎯 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.
🧐🧐🧐
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
📉🎯 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.
🧐🧐🧐
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)
![CTT_Journal's tweet photo. [Series 5 | Machine Learning & Data-Driven Control | #3]
#DataDrivenControl #LQR #OptimalControl #MachineLearning
📉🎯 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.
🧐🧐🧐
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)
Controlled State Transfer in Central Spin Models
✏️ Martiros Khurshudyan
🔗 https://t.co/14We9Gzno1
Viewed: 1933; Cited: 4
#mdpisymmetry #optimalcontrol #quantumsystems
@ice_csic
@MdpiPhysci
"Optimal Control of Entity-Based Systems via Koopman Representations With Product Density Observables," by Madeline Blischke; João P. Hespanha
Date: 18 March 2026
Link: https://t.co/5ifolSIona
#optimalcontrol #nonlinearsystems #games #costfunctions #controlsystems #ojcsys
Sixty Years of the Maximum Principle in #OptimalControl: Historical Roots and Content Classification
✏️ Roman Chertovskih et al.
🔗 https://t.co/35a0q8JJMC
Viewed: 6303; Cited: 8
#mdpisymmetry #bibliometricanalysis
@UPorto
@ComSciMath_Mdpi
TrendToKnow AI: Beyond Test-Time Training: Learning to Reason via Hardware-Efficient Optimal Control
👥 Peihao Wang, Shan Yang, Xijun Wang et al.
#AIResearch #MachineLearning #OptimalControl #NeuralNetworks
Provided by TrendToKnow AI
🔗 https://t.co/LKJEWWPS4X
#NewPaperOnline
📖 Nonzero-Sum Game-Based Modular Manipulator Optimal Tracking Control: Performance-Index Function Without Control Input
👉 https://t.co/k6xg6vxvQs
✍ By Bing Ma, Zebin Ji, Yi Qin, Xinye Zhu and Tianjiao An
#Robotics #optimalcontrol
Feedback very welcome!
Congratulations to Miguel Ibáñez for the huge effort! #StatisticalPhysics #Thermodynamics #OptimalControl #NonEquilibrium #PhysicsEducation #BrownianMotion
Our paper formulates chemotherapy design under tumor heterogeneity as a constrained optimal-control problem, solved via hybrid QPSO + local refinement.
Presented at CDC 2025 (Rio, Brazil), now published.
Read: https://t.co/fH0y3caaSk
#OptimalControl #Optimization #IEEE
TrendToKnow AI: Maximum Principle of Optimal Probability Density Control
👥 Nathan Gaby & Xiaojing Ye
#AIResearch #OptimalControl #DeepLearning #MultiAgentSystems
Provided by TrendToKnow AI
🔗 https://t.co/LKJEWWPS4X
Zorin OS est-il vraiment le remplaçant idéal de Windows 11 ? Interface, rapidité, fluidité... On fait le test complet ! La réponse en vidéo https://t.co/1jcFGDoOSM #ZorinOS #Windows11 #Linux #Informatique #Tech #Windows #OpenSource #os #free #ubuntu #optimalcontrol
@ZorinOS
Zorin OS est-il vraiment le remplaçant idéal de Windows 11 ? Interface, rapidité, fluidité... On fait le test complet ! La réponse en vidéo https://t.co/1jcFGDoOSM #ZorinOS #Windows11 #Linux #Informatique #Tech #Windows #OpenSource #os #free #ubuntu #optimalcontrol
@ZorinOS
Check out our latest work, "Actor-Critic Model Predictive Control: Differentiable Optimization meets Reinforcement Learning for Agile Flight," published in the IEEE Transactions on Robotics, where we reconcile #OptimalControl and #ReinforcementLearning, achieving the same super-human performance, but with superior generalizability, as our previous model-free deep RL! Code released!
PDF: https://t.co/qsYVvT4J1W
Code: https://t.co/MCvDypTGGx
Full Video: https://t.co/73Wk5cQAal
Model-free #ReinforcementLearning (RL) is known for its strong task performance and flexibility in optimizing general reward formulations. On the other hand, #ModelPredictiveControl (MPC) provides robustness, constraint handling, and powerful online replanning capabilities. In this work, we extend our previous AC-MPC paper (Romero, ICRA'24) by taking a deeper look at how both approaches can be unified. We introduce and extend Actor-Critic Model Predictive Control (AC-MPC), a framework that embeds a differentiable MPC inside an Actor-Critic RL architecture. This integration allows the MPC-based actor to perform short-term predictive optimization, while the critic facilitates long-horizon learning and exploration. We conduct a comprehensive study that highlights AC-MPC’s key advantages:
- Better out-of-distribution generalization, both against unknown disturbances and changes in the quadrotor dynamics
- Improved sample efficiency
- A novel empirical analysis uncovering a relationship between the critic’s value function and the MPC cost function, providing deeper insight into their interplay. We validate our method in simulation and the real world on a quadcopter flying at superhuman speeds of up to 21 m/s, matching state-of-the-art model-free RL performance, and retaining the predictive structure of MPC for more reliable out-of-distribution behavior.
Reference:
Actor-Critic Model Predictive Control: Differentiable Optimization meets Reinforcement Learning for Agile Flight
IEEE Transactions on Robotics (T-RO), 2025
PDF: https://t.co/qsYVvT4J1W
Full Video: https://t.co/73Wk5cQAal
Code: https://t.co/MCvDypTGGx
Kudos to @roaguiangel, @EliJalbout, @realyunlong!
@UZH_en @UZH_Science @UZHspacehub @AUTOASSESS_EU @ERC_Research @UZH_ai
"Constrained Path Planning for Soft Continuum Robots with Bernstein Surfaces," by Maxwell Hammond; Ean Lovett; Vincenzo Pugliese; Venanzio Cichella; Caterina Lamuta
Date: 2 Oct 2025
Link: https://t.co/5cchFADkFq
#optimalcontrol #pathplanning #robotics #controlsystems #ojcsys
"VisioPath: Vision-Language Enhanced Model Predictive Control for Safe Autonomous Navigation in Mixed Traffic," by Shanting Wang; Panagiotis Typaldos; Chenjun Li; Andreas A. Malikopoulos
Date: 10 Oct 2025
Link: https://t.co/nztjrA4YdY
#trajectoryplanning #optimalcontrol #ojcsys
Develop Dynamic Optimization for National Economic Planning (Optimal Control Theory). Visa Sponsorship. Apply now: https://t.co/2l50797M6b #OptimalControl #Policy
This video demonstrates the neural network solution to the Ramsey–Cass–Koopmans model. Each frame represents an epoch of the optimization process.
Code:
https://t.co/EG7K8ApU9o
#OptimalControl #DeepLearning
@MahdiKahou @jlperla In addition to traditional methods, using PINN, we can solve the Ramsey ODE model too. How do you compare Ridgeless Kernel to PINN?
"Mutual Support by Sensor-Attacker Team for a Passive Target," by Prajakta Surve; Shaunak D. Bopardikar; Alexander Von Moll; Isaac Weintraub; David W. Casbeer
Date: 19 Sept 2025
Link: https://t.co/usdby3TThI
#gametheory #optimalcontrol #sensors #controlsystems #ojcsys
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