Top Tweets for #OptimalControl
🤖 How can multi-agent systems achieve optimal consensus under changing communication networks?
A new study in the @OAE_IR journal presents an adaptive dynamic programming approach to optimal consensus control.
🔗 https://t.co/aATLY3sIKh
#MultiAgentSystems #OptimalControl
📘 New from Springer Math: Optimal Control in Random Environments by Daniel Hernández-Hernández & Joshué Heli Ricalde-Guerrero is now available.
🔗 https://t.co/EfQo4pWZAv
#SpringerMath #OptimalControl #MeanFieldGames
📢 Nonlinear Optimal Control for Spacecraft Rendezvous and Docking Using Symplectic Numerical Method
by Zhengtao Wei, et al.
👉 https://t.co/DbmF80AtEq
#rendezvousanddocking #optimalcontrol #superquadraticobstaclemodel
#symplecticnumericalmethod #airbearingexperiment
New paper on fusion of #reinforcementlearning and #optimalcontrol. I had a minor role in this excellent work by @arma1978 and his PhD student Yashdeep Chaudhary but still enjoyed being part of it: check it out!
https://t.co/fZ4Afo0XtZ
In SIAM News, Wei Kang, Liang Xu, and Qi Gong explore how #DataAssimilation and #OptimalControl can be unified by a common philosophy of leveraging #MachineLearning for #DynamicalSystems and using system-theoretic insights to improve ML. Read more here! https://t.co/G3NjSzTQUT
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
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
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?
"Integrating Reinforcement Learning and Model Predictive Control for Mixed-Logical Dynamical Systems," by Caio Fabio Oliveira da Silva, Azita Dabiri, and Bart De Schutter
Pub Date: 21 August 2025
Link: https://t.co/moFN2EyGCC
#reinforcementlearning #hybridsystems #optimalcontrol
#Article
📜 A Non-Linear Offset-Free Model Predictive Control Design Approach
by Haoran Zhang and Emmanuel Prempain
https://t.co/T41URfSjI5
@uniofleicester
@MDPIEngineering
#nonlinearmodelpredictivecontrol #optimalcontrol #integralcontrol #realtimecontrol #watertankprocess
🔥 Read our Paper
📚 Inducing Optimality in Prescribed Performance Control for Uncertain Euler–Lagrange Systems
🔗 https://t.co/3YpcVXSMSA
👨🔬 by Christos Vlachos et al.
#adaptivedynamicprogramming #optimalcontrol
#NextatBIRS-@CMO_oaxaca Mathematical Analysis of Adversarial Machine Learning, August 17- 22, 2025 https://t.co/dvuhGoVojJ
#DataScience #PDE #Calculus #OptimalControl #Optimization #MachineLearning
#NextatBIRS Efficient and Reliable Deep Learning Methods and their Scientific Applications, June 22 - 27, 2025 https://t.co/QEQL5dvmaN
#MachineLearning #Calculus #OptimalControl #Optimization #NumericalAnalysis #AI
@NSF-MPS #DMSFunded @Innovation_AB @NSERC_CRSNG @CRSNG_NSERC
モデル予測制御を用いた卓球ロボット
飛んでくるボールの速度と軌道を素早く推定し、ボールに様々な回転をかけて狙った場所に打ち返す
https://t.co/Qi9ZkKus5U
#RobotArm #MPC #HighSpeedPrecision #OptimalControl #algorithm #TableTennis #PingPong #MIT
The April issue of SIAM News is now available! In this special issue on #ComputationalScience and #engineering, Sebastian Reich focuses on one aspect of the #DigitalTwin paradigm: the synergy between #DataAssimilation and #OptimalControl. Check it out! https://t.co/N46Na6aCdZ
[1/4] A project from a while ago is out: we design a new algorithm based on hybrid minimum principle for optimal control in rigid-body dynamics with collisions https://t.co/tn3kwQuUko #OptimalControl #HybridSystems #PhysicsSimulation #ReinforcementLearning
![JiequnH's tweet photo. [1/4] A project from a while ago is out: we design a new algorithm based on hybrid minimum principle for optimal control in rigid-body dynamics with collisions https://t.co/tn3kwQuUko #OptimalControl #HybridSystems #PhysicsSimulation #ReinforcementLearning](https://pbs.twimg.com/media/GmBdlt3WoAAkzKp.jpg)
![JiequnH's tweet photo. [1/4] A project from a while ago is out: we design a new algorithm based on hybrid minimum principle for optimal control in rigid-body dynamics with collisions https://t.co/tn3kwQuUko #OptimalControl #HybridSystems #PhysicsSimulation #ReinforcementLearning](https://pbs.twimg.com/media/GmBdkGMWUAAaucB.jpg)
![JiequnH's tweet photo. [1/4] A project from a while ago is out: we design a new algorithm based on hybrid minimum principle for optimal control in rigid-body dynamics with collisions https://t.co/tn3kwQuUko #OptimalControl #HybridSystems #PhysicsSimulation #ReinforcementLearning](https://pbs.twimg.com/media/GmBdiTrWMAABwSm.jpg)
📍Let us warmly welcome Prof. Taekyun Kim as the new member of our ADECP's Editorial Board.👏
Prof. Taekyun Kim
Kwangwoon University
South Korea
Interests: #numbertheory, #optimalcontrol, probability
https://t.co/zweGNDSJEP
#Article
📜 Novel Adaptive Magnetic Springs for Reliable Industrial Variable Stiffness Actuation
by Branimir Mrak, et al.
https://t.co/Yl4mdBg5BN
@FlandersMake
#codesign #mechanismdesign #optimalcontrol #industryapplications #parallelelasticactuators #variablestiffness
Reducing Tyre Wear Emissions of Automated Articulated Vehicles through Trajectory Planning
https://t.co/eaZ3lOkoEh
@tudelft @KTHuniversity
#trajectoryplanning; #optimalcontrol; #articulatedvehicles
@Un_spectre @baptistebonhom5 @melex_sior @camtioucha Le gars quand il va réaliser que Marx, Cockshott, Cottrell, Guzman, Kantorovitch étaient des universitaires.
Il va la sortir de son gros cerveau, la planification socialiste. Team #OptimalControl
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