Article
Automation & Control Systems
Yajie Bao, Kimberly J. Chan, Ali Mesbah, Javad Mohammadpour Velni
Summary: This paper presents a learning-based, adaptive-scenario-tree model predictive control approach for uncertain nonlinear systems. Bayesian neural networks (BNNs) are used to learn the model uncertainty, and adaptive scenarios are generated based on this learned uncertainty. The proposed approach improves the robust control performance.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Construction & Building Technology
Tomas Pippia, Jesus Lago, Roel De Coninck, Bart De Schutter
Summary: This article presents a new approach that combines a stochastic scenario-based MPC controller with a nonlinear building model, which can accurately capture building dynamics and enhance control action robustness through multiple external disturbance realizations.
ENERGY AND BUILDINGS
(2021)
Article
Automation & Control Systems
Edwin Gonzalez, Javier Sanchis, Jose Vicente Salcedo, Miguel Andres Martinez
Summary: This paper proposes a new MPC method called conditional scenario-based model predictive control (CSB-MPC) for discrete-time linear systems with parametric uncertainties and/or additive disturbances. A primary set of equiprobable scenarios is generated at each control period and approximated to a new reduced set of conditional scenarios with their respective probabilities of occurrence. The method penalizes predicted states and inputs in the cost function based on the probabilities associated with the uncertainties. The results of two numerical examples demonstrate the better performance of CSB-MPC in terms of not transgressing state constraints even with fewer scenarios compared to standard scenario-based MPC.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Review
Agronomy
Ritesh G. Jain, Karl E. Robinson, Sassan Asgari, Neena Mitter
Summary: RNA interference (RNAi) is a promising approach for managing hemipteran pests, but commercial implementation faces challenges such as limited knowledge about dsRNA uptake mechanisms and RNAi gene functions. This review highlights recent progress in RNAi-based studies aimed at reducing insect populations, viral transmission, and insecticide resistance in hemipteran pests. It also explores potential solutions to improve RNAi-mediated management of hemipteran insects, including in silico approaches and formulation of dsRNA effector to minimize off-target effects and improve broad-spectrum pest control.
PEST MANAGEMENT SCIENCE
(2021)
Article
Agriculture, Multidisciplinary
Lukas Petrich, Georg Lohrmann, Fabio Martin, Albert Stoll, Volker Schmidt
Summary: Managing weeds in grassland presents challenges as different control strategies have varying resource requirements and treatment efficiency. The effectiveness of wide tractor-based systems with section control and small agricultural robots in weed control depends on the distribution of weeds in the field. In addition, incomplete knowledge of weed locations poses a challenge. This study investigated and evaluated different treatment strategies for the control of Colchicum autumnale using real and simulated data.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Article
Engineering, Electrical & Electronic
Di Ao, Jialin Li, Lin Zhang, Jinlong Hong
Summary: This research proposes an active safety & stability controller for recovering the vehicle to safety states after an initial impact. The controller regulates the course angle and lateral deviation to ensure the vehicle returns to its original driving path. The controller performs well in low- and high-speed collision scenarios and demonstrates fault-tolerance capabilities.
IET INTELLIGENT TRANSPORT SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Zhanyi Hu, Zeyu Yang, Jin Huang, Zhihua Zhong
Summary: This paper addresses the longitudinal motion control problem during lane-changing process subject to time-varying uncertainties by utilizing a robust controller based on Udwadia-Kalaba approach and Lyapunov stability theory. The research applies bilateral inequality constraints to ensure safe and efficient lane-changing, with a proposed diffeomorphism method transforming bounded states to unbounded ones to enable the UK approach to handle both equality and bilateral inequality constraints. Numerical experiments confirm the effectiveness of the proposed controller under different traffic demands.
IET INTELLIGENT TRANSPORT SYSTEMS
(2021)
Article
Pharmacology & Pharmacy
Kangyuan Guo, Zhanchun Feng, Shanquan Chen, Ziqi Yan, Zhiming Jiao, Da Feng
Summary: This research evaluated the safety of antipsychotic drugs based on real-world data. The study found that most adverse drug reactions (ADRs) occurred within 3 months of treatment and recommended close observation during this period. The symptoms caused by typical and atypical antipsychotic drugs were different, with dyskinesia being more common in typical antipsychotics. Risk factors for serious ADRs included low-level hospitals, psychiatric hospitals, youth, and old age. The study also identified four off-label signals through signal mining, which should be given special attention.
FRONTIERS IN PHARMACOLOGY
(2022)
Review
Materials Science, Multidisciplinary
Anthony R. Bunsell, Alain Thionnet
Summary: The increasing use of advanced composite materials in a variety of structures has highlighted the importance of quantifying damage accumulation to prevent failure, especially in extreme situations. This review discusses how fibre failure dominates damage accumulation in composite structures, with the viscoelastic nature of the matrix inducing time effects. By linking damage at the fibre level to overall structure reliability, a multi-scale approach can be used to quantitatively model damage accumulation.
PROGRESS IN MATERIALS SCIENCE
(2022)
Article
Energy & Fuels
Martin A. Alarcon, Rodrigo G. Alarcon, Alejandro H. Gonzalez, Antonio Ferramosca
Summary: The heavy reliance on fossil fuels for electricity generation in the world necessitates the need for electric generation from renewable resources to reduce greenhouse gas emissions. However, renewable resources exhibit random and intermittent behavior. Therefore, new management and control tools are needed to integrate these resources into the current electricity system. Microgrids, with their effective control systems, have become a solution to this problem. An optimal control structure, consisting of two Model Predictive Control strategies, is proposed for a microgrid Energy Management System.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2023)
Article
Automation & Control Systems
Jinghan Cui, Xiangjie Liu, Tianyou Chai
Summary: This article proposes a stable scenario-based economic model-predictive control strategy that incorporates more uncertainty information by employing an augmented prediction model with a scenario tree. It also utilizes a trained deep neural network as an approximation function to make online implementation tractable. The feasibility and stability of this approximate scenario-based economic model-predictive control are addressed theoretically and its effectiveness is demonstrated through an application on wind energy conversion systems.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Pharmacology & Pharmacy
George A. Mystridis, Georgios C. Batzias, Ioannis S. Vizirianakis
Summary: This article constructs PBPK models for DOX using systems pharmacology approach and analyses eight plausible models based on existing data and two clinical case studies. The validation of the models supports their design and provides a basis for further research.
Article
Robotics
Prithvi Akella, Aaron D. Ames
Summary: This paper introduces an approach to safety-critical verification using barrier functions. The method verifies a system's ability to maintain the positivity of a candidate barrier function at discrete-time intervals by evaluating states and solving randomized linear programs. The authors showcase the results by verifying the robotarium simulator, identifying counterexamples for its hardware counterpart, and verifying the safety of a multi-agent quadrupedal system.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Thermodynamics
Milad Karimshoushtari, Mojtaba Kordestani, Sina Shojaei, Bilge Kagan Donmez, Muzamil Rashid, Feisel Weslati, Kamal Bouyoucef
Summary: This paper proposes three advanced Model-Based Control (MBC) strategies for the cabin active heating thermal system of an EV, and demonstrates through experiments that these strategies have good performance in reference tracking and energy consumption, providing significant advancements in thermal system control of Electrified Vehicles.
Article
Computer Science, Artificial Intelligence
Le Nguyen Hoai Nam
Summary: This paper proposes methods to aggregate profiles of group members by incorporating latent factor matrices, improving the reflection of group members' interests. Experimental results show that this approach can enhance group recommendation performance.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Nuclear Science & Technology
J. T. Scoville, M. D. Boyer, B. J. Crowley, N. W. Eidietis, C. J. Pawley, J. M. Rauch
FUSION ENGINEERING AND DESIGN
(2019)
Article
Physics, Fluids & Plasmas
M. D. Boyer, S. Kaye, K. Erickson
Article
Nuclear Science & Technology
D. Mueller, S. H. Hahn, N. Eidietis, J. G. Bak, M. D. Boyer, D. A. Humphreys, A. W. Hyatt, Y. M. Jeon, H. S. Kim, M. Walker
FUSION ENGINEERING AND DESIGN
(2019)
Article
Physics, Fluids & Plasmas
M. D. Boyer, K. G. Erickson, B. A. Grierson, D. C. Pace, J. T. Scoville, J. Rauch, B. J. Crowley, J. R. Ferron, S. R. Haskey, D. A. Humphreys, R. Johnson, R. Nazikian, C. Pawley
Article
Physics, Fluids & Plasmas
K. J. Montes, C. Rea, R. S. Granetz, R. A. Tinguely, N. Eidietis, O. M. Meneghini, D. L. Chen, B. Shen, B. J. Xiao, K. Erickson, M. D. Boyer
Article
Physics, Fluids & Plasmas
Yichen Fu, David Eldon, Keith Erickson, Kornee Kleijwegt, Leonard Lupin-Jimenez, Mark D. Boyer, Nick Eidietis, Nathaniel Barbour, Olivier Izacard, Egemen Kolemen
PHYSICS OF PLASMAS
(2020)
Article
Nuclear Science & Technology
Sang-hee Hahn, H. Han, M. H. Woo, J. G. Bak, J. Chung, Y. M. Jeon, J. H. Jeong, M. Joung, J. W. Juhn, H. S. Kim, Heungsu Kim, M. W. Lee, G. W. Shin, T. H. Tak, S. W. Yoon, J. Barr, N. W. Eidietis, D. A. Humphreys, A. Hyatt, B. G. Penaflor, D. A. Piglowski, M. L. Walker, A. S. Welander, M. D. Boyer, K. Erickson, D. Mueller
FUSION ENGINEERING AND DESIGN
(2020)
Article
Physics, Fluids & Plasmas
M. D. Boyer, X. Yuan, J. Ahn, S-H. Hahn, R. Nazikian, F. M. Poli, S. Sabbagh
Article
Nuclear Science & Technology
David Humphreys, A. Kupresanin, M. D. Boyer, J. Canik, C. S. Chang, E. C. Cyr, R. Granetz, J. Hittinger, E. Kolemen, E. Lawrence, V. Pascucci, A. Patra, D. Schissel
JOURNAL OF FUSION ENERGY
(2020)
Article
Nuclear Science & Technology
Shira M. Morosohk, Mark D. Boyer, Eugenio Schuster
Summary: A neural network model has been developed to study the effects of neutral beam injection on DIII-D, using experimental data from the 2018 campaign generated by the NUBEAM module of TRANSP. By reducing dimensionality of profile data using principal component analysis, the model accurately reproduces the results of Monte Carlo code NUBEAM with significantly faster execution time, making it suitable for offline scenario planning and online active control applications that require numerous simulation runs for optimization tasks.
FUSION ENGINEERING AND DESIGN
(2021)
Article
Physics, Fluids & Plasmas
T. Liu, Z. R. Wang, M. D. Boyer, S. Munaretto, Z. X. Wang, B. -H. Park, N. C. Logan, S. M. Yang, J. -K. Park
Summary: The successful application of 3D magnetohydrodynamic spectroscopy in tokamak experiments allows for the identification of multi-mode eigenvalues and real-time detection of stability. The new method efficiently uses internal coils to apply 3D fields for active plasma stability detection. Numerical validation has shown that this method is robust and more efficient for real-time monitoring of plasma stability based on stable mode eigenvalues.
Article
Physics, Fluids & Plasmas
M. D. Boyer, J. Chadwick
Summary: A new model developed using neural networks can predict electron density and pressure profile shapes on NSTX and NSTX-U efficiently and accurately, with potential applications in scenario development optimization and real-time plasma control. The model has been trained and tested on measured profiles from experimental discharges during the first operational campaign of NSTX-U.
Article
Physics, Fluids & Plasmas
M. D. Boyer, C. Rea, M. Clement
Summary: This paper presents a real-time capable algorithm for identifying the safe operating region of a tokamak. The algorithm calculates the distance of a point from a disruptive boundary, based on a convex set of linear constraints. It uses an empirical machine learning predictor to calculate the disruptivity of points. The algorithm enables active optimization of the operating point to maintain a safe margin from disruptive boundaries. The proposed algorithm is tested and shown to be effective and real-time capable.
Article
Physics, Fluids & Plasmas
J. L. Barr, B. Sammuli, D. A. Humphreys, E. Olofsson, X. D. Du, C. Rea, W. P. Wehner, M. D. Boyer, N. W. Eidietis, R. Granetz, A. Hyatt, T. Liu, N. C. Logan, S. Munaretto, E. Strait, Z. R. Wang, The DIII-D Team
Summary: Novel disruption prevention solutions, including real-time control algorithms, limited topology transitions during emergency shutdown, and emergency shutdown methods, have been developed and tested. These methods have successfully prevented vertical displacement events, improved the disruption risk during emergency shutdown after large tearing and locked modes, and developed a method to excite instabilities to form a warm, helical core post-thermal quench to avoid VDEs and runaway electron generation.
Article
Physics, Fluids & Plasmas
W. Guttenfelder, D. J. Battaglia, E. Belova, N. Bertelli, M. D. Boyer, C. S. Chang, A. Diallo, V. N. Duarte, F. Ebrahimi, E. D. Emdee, N. Ferraro, E. Fredrickson, N. N. Gorelenkov, W. Heidbrink, Z. Ilhan, S. M. Kaye, E-H Kim, A. Kleiner, F. Laggner, M. Lampert, J. B. Lestz, C. Liu, D. Liu, T. Looby, N. Mandell, R. Maingi, J. R. Myra, S. Munaretto, M. Podesta, T. Rafiq, R. Raman, M. Reinke, Y. Ren, J. Ruiz Ruiz, F. Scotti, S. Shiraiwa, V Soukhanovskii, P. Vail, Z. R. Wang, W. Wehner, A. E. White, R. B. White, B. J. Q. Woods, J. Yang, S. J. Zweben, S. Banerjee, R. Barchfeld, R. E. Bell, J. W. Berkery, A. Bhattacharjee, A. Bierwage, G. P. Canal, X. Chen, C. Clauser, N. Crocker, C. Domier, T. Evans, M. Francisquez, K. Gan, S. Gerhardt, R. J. Goldston, T. Gray, A. Hakim, G. Hammett, S. Jardin, R. Kaita, B. Koel, E. Kolemen, S-H Ku, S. Kubota, B. P. LeBlanc, F. Levinton, J. D. Lore, N. Luhmann, R. Lunsford, R. Maqueda, J. E. Menard, J. H. Nichols, M. Ono, J-K Park, F. Poli, T. Rhodes, J. Riquezes, D. Russell, S. A. Sabbagh, E. Schuster, D. R. Smith, D. Stotler, B. Stratton, K. Tritz, W. Wang, B. Wirth
Summary: The mission of NSTX-U is to advance the physics and technical solutions for next-step steady-state tokamak fusion devices. It aims to develop a deeper understanding of high-performance non-inductive plasmas and solve challenges related to plasma exhaust and thermal transport.