25 papers · ranked by Valyu relevance
Luigi Marra, Onofrio Semeraro, Lionel Mathelin, Andrea Meilán-Vila + 1 more
This work presents a scalable control framework based on nonlinear Model Predictive Control for high-dimensional dynamical systems. The proposed approach addresses the key challenges of model scalability and partial observability by integrating data-driven reduced order modelling, control in a latent space, and state…
Hongluo Li, Hai Pang, Hongyang Xia, Yongxian Huang + 2 more
Autonomous driving, a transformative automotive technology, is currently a major research focus. Trajectory planning, one of the three core technologies for realizing autonomous driving, plays a decisive role in the performance of autonomous driving systems. The key challenge lies in planning an optimal trajectory…
Hirt, Sebastian, Suwanto, Valentinus + 6 more
Learning controller parameters from closed-loop data has been shown to improve closed-loop performance. Bayesian optimization, a widely used black-box and sample-efficient learning method, constructs a probabilistic surrogate of the closed-loop performance from few experiments and uses it to select informative…
Rong-Tzong Wu, Jiahao Ai, Tinko Bartels, Li + 1 more
The transition toward power grids with high renewable penetration demands context-aware decision making frameworks. Traditional operational paradigms, which rely on static optimization of history-based load forecasting, often fail to capture the complex nature of real-time operational conditions, such as…
Mohamed Ayman, Mahmoud A. Attia, Ahmed M. Asim
This paper presents an Adaptive Model Predictive Control (AMPC) strategy for robust load-frequency control (LFC) in single-area and double-area power systems under load variations, parameter uncertainty, and renewable energy disturbances. The controller integrates online system identification using Recursive Least…
Felix Brändle, Frank Allgöwer
Model predictive control (MPC) is a powerful control method that allows to directly include state and input constraints into the controller design. However, errors in the model, e.g., caused by unknown disturbances, can lead to constraint violation, loss of feasibility and deteriorate closed-loop performance. In this…
Huanhuan Ren, Chengzhi Su, Ranxiang Long, Gang Wang
This paper proposes a current model predictive control strategy for the permanent magnet synchronous motor (PMSM) based on a novel sliding mode observer to reduce the cost of PMSM and ensure good tracking performance. A super twisting sliding mode observer (STSMO) is designed to address the issues of high-frequency…
Mitrai, Ilias
— In this paper, we propose symbolic decision trees as surrogate models for approximating model predictive control laws. The proposed approach learns simultaneously the partition of the input domain (splitting logic) as well as local nonlinear expressions for predicting the control action leading to interpretable…
Liam Perreault, Idris Kempf, Kirill Sechkar, Jean-Baptiste Lugagne + 1 more
Cybergenetic gene expression control in bacteria enables applications in engineering biology, drug development, and biomanufacturing. AI-based controllers offer new possibilities for real-time, single-cell-level regulation but typically require large datasets and re-training for new systems. Data-enabled Predictive…
Solera-Rico, Alberto, Vila, Carlos Sanmiguel + 2 more
We present an efficient and realisable active flow control framework with few non-intrusive sensors. The method builds upon data-driven, reduced-order predictive models based on Long-Short-Term Memory (LSTM) networks and efficient gradient-based Model Predictive Control (MPC). The model uses only surface-mounted…
Authors not listed
Artificial intelligence (AI) is reshaping chemical engineering. Still, its role in safety-critical operations is limited because we rarely see tools that link physical models with data-driven methods. This study brings together three elements: physics-constrained neural networks, uncertainty quantification, and a…
Wenfei Yu, Dejie Bo, Yanbin Han, Jia Han + 4 more
Aiming at the issue of poor robustness in continuous control set model predictive current control (CCS-MPCC) algorithm, a class of model predictive controllers based on linear-nonlinear switching extended state observers has been designed. Theoretical derivations demonstrate the structural equivalence between the…
Magdy Meawad, Abdelsalam A. Ahmed, Mona I. Abdelkader, Yasser G. Dessouky + 1 more
This paper presents robust speed-control techniques for permanent-magnet synchronous motors (PMSMs) by integrating model-predictive and sliding-mode strategies. Two controllers are developed: a Sliding-Mode Speed Control-based Model Predictive Current Controller (SMSC-MPCC) using an Adaptive Sliding Mode Surface (ASMS)…
Weidong Jia, Kangping Sun, Xiang Dong, Dong Chen + 1 more
Semi-active suspension systems combine low power consumption, rapid response, and fail-safe operation by reverting to passive mode after control failure, making them important for intelligent chassis and vibration-control systems. With the development of intelligent actuators, nonlinear modeling, and advanced control…
Qiuxia Fan, Ruidong Wang, Zefeng Yan, Qianqian Zhang + 3 more
Low-frequency seismic disturbances significantly limit the performance of precision engineering systems and active vibration isolation platforms. Model predictive control (MPC) is widely applied in such systems due to its ability to handle multi-variable dynamics and constraints. However, its performance strongly…
Menno van Laarhoven, Alfredo Rates, Josiah B. Passmore, Shengling Shi + 3 more
Optogenetics enables experiments in out-of-equilibrium conditions to clarify biological mechanisms and quantify biophysical parameters. However, modelling and control techniques to study mammalian cell biology under optogenetic perturbation remain underutilised. Here, we benchmark these methods within mammalian cells…
Anne Richelle, David Andersson, Athanasios Antonakoudis, Jesper Jakobsson + 3 more
Digital twins of mammalian cell cultures hold great potential for predictive bioprocess modeling, yet their development is challenged by the nonlinear dynamics and metabolic complexity of these systems. We present a hybrid computational framework that integrates mechanistic and data-driven modeling to construct…
Mengjia Zhu, Oliver Pennington, Tararag Pincam, Mohammadamin Zarei + 4 more
Bioprocesses are critical for sustainable industrial development but face challenges from their inherent uncertainties that affect efficiency and scalability. This study in-troduces a worst-case operational space design framework, integrating symbolic optimization with scenario-based validation, to preemptively…
Serhii Bahdasariants, Lauren Parola, Kriti Kacker, Ariel K. Feldman + 3 more
Biological and robotic systems must solve two related computations to move: inverse dynamics, which determines the forces or torques needed to produce a desired movement, and forward dynamics, which maps applied forces to motion. Although these computations are coupled by the same equations of motion, they are usually…
Jun Kobayashi
We ask how a forward-model-based predictive state observer should set its sensory prediction-error correction gain during muscle-driven reaching, and whether that gain can be adapted from agent-available signals — innovation history and per-episode reaching outcome — rather than from swept oracle labels. We evaluate a…
Spencer T. Williams, Geng Li, Benjamin J. Fregly
Neural feedback is important for the control of movement, and multiple neurological disorders (e.g., stroke, cerebral palsy, Parkinson’s disease, incomplete spinal cord injury) are characterized by altered neural feedback. Researchers have created numerous computational neuromusculoskeletal models controlled by…
Authors not listed
Three-dimensional molecular generative models have emerged that produce de novo molecules both unconditionally and conditionally, e.g., within protein pockets. However, steering those models in a specific region of the chemical space that satisfies a set of desired properties remains challenging. In this study, we…
Authors not listed
Accurate extrapolation in data-scarce scientific systems remains a central challenge for machine intelligence. In microbial bioprocessing, kinetic parameters change non-monotonically with reactor volume due to interacting hydrodynamic, oxygen-transfer, and mixing effects, rendering classical empirical scaling laws…
Authors not listed
This paper formally defines an operational isomorphism between spectral damping in molecular vibronic systems and neuromodulatory control in biological sensory systems. Without asserting causal continuity or physical identity across scales, we show that both domains instantiate the same class of output-selective…
Authors not listed
Quantitative Structure-Activity Relationship (QSAR) modeling is a pillar of computational drug discovery. However, standard machine learning (ML) models are often confounded by the high-dimensional and intensely correlated nature of molecular descriptors. A model may identify a "bulk" property (e.g., molecular weight)…