Advanced Control Systems Optimization

Neueste Paper

• Safe Greenhouse Climate Control Using Lagrangian-Constrained PPO with Kolmogorov-Arnold Networks

  Hangzun Liu, Yuling Fan, Fang Tian, Zhilong Bie, Zaiwen Feng, Yongliang Qiao · 29. September 2026

  Greenhouse climate control balances economic return with maintaining temperature, humidity and CO2 within crop-adapted growth ranges. Conventional reinforcement learning (RL) greenhouse controllers use fixed reward penalties to limit climate constraint violations, yet such heuristic penalties cannot…

• Precision at Speed: Sample-Efficient Online Model-Based Reinforcement Learning for Hydraulic Excavator Control

  Claudio Canales, Fang Nan, Marco Hutter, Javier Ruiz-del-Solar · 28. September 2026

  Precise, high-speed control remains challenging for robots with complex actuation dynamics. Learning directly on hardware is further constrained by the cost of real-world interaction. We present an online model-based reinforcement learning framework that learns a probabilistic dynamics ensemble mode…

• DeepSPoC: A Deep Learning Based Sequential Propagation of Chaos

  Kai Du, Yongle Xie, Tao Zhou, Yuancheng Zhou · 23. September 2026

  Classical particle methods based on propagation of chaos (PoC) have been developed for solving mean-field stochastic differential equations and their associated nonlinear Fokker--Planck equations. However, direct PoC implementations are difficult to apply to high-dimensional problems because they re…

• Augmenting PID Control with Deep Reinforcement Learning: A Hybrid Approach to the Industrial Benchmark

  Zhengyang (Cissy), Gu, Joseph E. Hernandez, John Burtenshaw, Sean Scott, Thomas Cook, Chris Couch · 22. September 2026

  As industrial processes grow in complexity, traditional Proportional-Integral-Derivative (PID) controllers are often insufficient for handling their non-linear, multi-input dynamics. We propose using advanced Deep Reinforcement Learning (DRL) to prove its advantages in these complex environments. To…

• Adaptive Uncertainty-Aware Modeling and Stochastic Radial Basis Function Predictive Control for Personalized Fluid Resuscitation

  Elham Estiri, Hossein Mirinejad · 21. September 2026

  This paper presents a novel framework integrating Bayesian physiological modeling with optimal control strategies to achieve uncertainty-aware, personalized hemodynamic regulation during fluid resuscitation. An uncertainty-aware variational autoencoder state-space model (UVAE-SSM) was first develope…

• GEM-MPC: Balancing Exploration and Exploitation through Expert-Guided Planning

  Alvaro Serra-Gomez, Thomas Moerland · 21. September 2026

  Effective exploration in high-dimensional continuous control remains a central challenge in reinforcement learning. Planning-based methods address this by combining online planning with learned policies and value functions, but their components can become misaligned during training: learned sampling…

• Accelerating Reinforcement Learning via MPC Solver-Gradient Guidance for Weights-varying MPC

  Baha Zarrouki, Arslan Thobani, Jasper Hoffmann, Mattia Piccinini, Rudolf Reiter, Felix Jahncke, S\'ebastien Gros, Davide Scaramuzza, Johannes Betz · 2. September 2026

  In Model Predictive Control (MPC), cost-function weights shape closed-loop behavior, yet changing conditions often make fixed parametrizations suboptimal and motivate context-dependent online adaptation. Learning such policies is difficult because behavior depends implicitly on numerical MPC solutio…

• Communication Reduction via Semantic-Based Encoding in DMPC Using LSTMs

  Torben Schiz, Pedro H. J. Nardelli, Henrik Ebel · 19. August 2026

  The communication demands of distributed model prediction control (DMPC) can overwhelm even advanced wireless communication technologies as agents must exchange a significant amount of information at least once per time step. To semantically reduce communication demands, this work employs encoder-de…

• Consistent Model Chasing Is Minimax Optimal: The Exact Value of Scalar Adversarial Adaptive Control under Large Parametric Uncertainty

  Dimitar Ho · 17. August 2026

  We solve exactly a fundamental problem of adaptive control against adversarial disturbances: regulate the scalar system $x_{t+1} = ax_t + u_t + w_t$, $x_0=0$, $\|w\|_\infty \le 1$, where the constant pole $a \in [-\Delta, \Delta]$ is unknown in sign and magnitude and $\Delta$ is arbitrarily large. E…

• Forward Trajectory Steering for Hamilton-Jacobi Reachability Analysis

  Sungje Park, Stephen Tu · 13. August 2026

  Hamilton-Jacobi (HJ) reachability provides a mathematically rigorous framework for safe control of dynamical systems, but its practical application is bottlenecked by the computational complexity of solving Hamilton-Jacobi-Isaacs variational inequality PDEs in high dimensions. Physics-informed neura…

• Topological Feasibility Guarantees for Differentiable Predictive Control

  Guangyu Wu, J\'an Drgo\v{n}a · 12. August 2026

  Differentiable predictive control (DPC), a self-supervised learning approach for approximating explicit model predictive control (MPC) policies, offers significant computational advantages over online optimization-based MPC. However, feasibility guarantees, a core requirement for safe control, are c…

• Path Integral Value Matching for Linear Quadratic Stochastic Optimal Control

  Bangyan Liao, Chenglei Yu, Yuchen Yang, Chuanrui Wang, Zhisheng Song, Peidong Liu, Tailin Wu · 12. August 2026

  Linear Quadratic Stochastic Optimal Control (LQ-SOC) establishes a fundamental framework for steering noisy dynamical systems and has recently gained renewed interest in the machine learning community. However, current state-of-the-art policy-based methods suffer from prohibitive computational costs…

• Control-Oriented Scenario Tree Construction through Reinforcement Learning

  Fabio Pavirani, Bert Claessens, Pierre Pinson, Chris Develder · 11. August 2026

  Multistage stochastic model predictive control (MPC) handles uncertainty by optimizing over a scenario tree, a finite branching approximation of future outcomes constructed from sampled forecasts. To build such a tree, conventional methods focus on matching the underlying probability distribution---…

• Certified Feedforward Tracking for Unknown Nonlinear Systems via Invertible Neural Networks

  Berk Altiner, Rajasree Sarkar, Arunava Banerjee, Zongxuan Sun, Kenneth Kim · 10. August 2026

  In this paper, we address the certification of datadriven feedforward control for periodic tracking of unknown nonlinear systems under partial state measurements. To this end, we adopt an invertible neural network (INN) as a surrogate for the unknown system. This choice allows us to bypass solving a…

• A Systematic Review and Taxonomy of Reinforcement Learning-Model Predictive Control Integration for Linear Systems

  Mohsen Jalaeian Farimani, Roya Khalili Amirabadi, Davoud Nikkhouy, Malihe Abdolbaghi, Mahshad Rastegarmoghaddam, Shima Samadzadeh, Mahdi Ghane · 6. August 2026

  The integration of Model Predictive Control (MPC) and Reinforcement Learning (RL) has emerged as a promising paradigm for constrained decision-making and adaptive control. MPC offers structured optimization, explicit constraint handling, and established stability tools, whereas RL provides data-driv…

• Learning-Based Stochastic Optimal Control with Infinite-Horizon Probabilistic Constraints

  Francesco Cordiano, Kanghui He, Bart De Schutter · 4. August 2026

  In this paper, we consider stochastic optimal control problems with infinite-horizon joint chance constraints. By means of an appropriate state augmentation, we reformulate the original problem as a constrained Markov decision process, in which both the cost and the constraint function exhibit an ad…

• Self-Adaptive Learning and Model Predictive Control for Tracking Unknown Dynamics with No Regret

  Atharva Navsalkar, Hongyu Zhou, Vasileios Tzoumas · 30. Juli 2026

  We propose a self-adaptive online learning for control method for tracking unknown target dynamics. The target dynamics can exhibit switching behavior, particularly, a mixture of structured, random, and/or adversarial motion. Such challenging target tracking scenarios arise in applications of dynami…

• Amortising Trajectory Optimisation for Residual MPC via Implicit Contact Differentiation

  Daniel Layeghi, Thomas Corb\`{e}res, Calum Arnott, Aditya Kamireddypalli, Hashim Al-Obaidi, Steve Tonneau, Michael Mistry · 29. Juli 2026

  Differentiable simulation can accelerate contact-rich trajectory optimisation by exposing local sensitivities of task outcomes to controls. Existing approaches either use finite differences, which are expensive and step-size sensitive; differentiate iterative contact solvers by unrolling automatic d…

• dtControl2+$\varepsilon$: Trading Optimality for Explainability in MDPs via Decision Trees

  Tereza Kinsk\'a, Jan K\v{r}et\'insk\'y, Tobias Meggendorfer, Sabine Rieder, Maximilian Weininger · 29. Juli 2026

  Over the past decade, decision trees have been used to represent controllers (a.k.a. policies) in an explainable way, with dtControl2 as a current state-of-the-art tool. However, for systems that are large or have many corner cases, even such representations tend to be too complex and not human-comp…

• An LLM-Driven Workflow for Automated Process Control Strategy Generation and Tuning from Dynamic Process Models

  Ari Luna Rueda, Eike Cramer, Klaus Hellgardt, Mehmet Mercang\"oz · 24. Juli 2026

  We present a structured large-language-model-driven workflow for automated multi-variable control design from dynamic process models. The workflow decomposes the design task into constrained code-generation steps: plant-interface construction, normalization, manipulated-variable controlled-variable …

• Pick-to-Learn Calibration of an MPC Policy for an Origin-to-Destination Flight Problem

  Marco C. Campi, Simone Garatti · 20. Juli 2026

  This paper illustrates the Pick-to-Learn methodology applied to the calibration of a Model Predictive Control policy. While developed around a specific example, the presentation is meant to highlight a methodology of broad applicability. The example concerns an aircraft traveling from an origin poin…

• Modeling and Control of Deep Sign-Definite Dynamics with Application to Hybrid Powertrain Control

  Teruki Kato, Ryotaro Shima, Kenji Kashima · 17. Juli 2026

  Data-driven control increasingly relies on deep models for complex systems whose first-principles models are difficult to obtain. For reliable deployment, however, learned dynamics should respect physical structure and lead to tractable optimal control. We introduce sign constraints, namely sign res…

• Adapting Generalist Vehicle Models for High-Speed MPC Across Terrains

  Rwik Rana, Jesse Quattrociocchi, Christian Ellis, Nathan Tsoi, Garrett Warnell, Joydeep Biswas · 16. Juli 2026

  High-speed off-road autonomy requires precise closed-loop control for a target vehicle while remaining robust across changing terrains. Recent forward kinodynamic (FKD) prediction foundation models suggest a promising path, starting from a generalist model and specializing it to the target platform.…

• Learning-enabled Acceleration of Scenario-based Model Predictive Control

  Trinh Tran, Binh Nguyen, Truong X. Nghiem · 15. Juli 2026

  Scenario-based model predictive control (SBMPC) is a variant of model predictive control (MPC) that explicitly accounts for uncertainty by optimizing control actions over multiple predicted scenarios. However, its computational complexity increases rapidly with the number of scenarios and prediction…

• Conformal Predictive Programming for Chance Constrained Optimization

  Yiqi Zhao, Xinyi Yu, Matteo Sesia, Jyotirmoy V. Deshmukh, Lars Lindemann · 10. Juli 2026

  We propose conformal predictive programming (CPP), a framework to solve chance constrained optimization problems, i.e., optimization problems with constraints that are functions of random variables. CPP utilizes samples from these random variables along with the quantile lemma - central to conformal…