Search · four archives
Search · four archives
24 papers · ranked by Valyu relevance
Zheng-Meng Zhai, Mohammadamin Moradi, Ling-Wei Kong, Bryan Glaz + 2 more
'Mulugeta Haile' 'Ying-Cheng Lai'] Nonlinear tracking control enabling a dynamical system to track a desired trajectory is fundamental to robotics, serving a wide range of civil and defense applications. In control engineering, designing tracking control requires complete knowledge of the system model and equations. We…
Ashly Mary Tom, J. L. Febin Daya
This study presents machine learning (ML)-based controllers for a surface permanent magnet synchronous motor (PMSM) drive system. The ML-based regression techniques like linear regression (LR), support vector machine regression (SVM), feedforward neural network (NN) and advanced NN like Long Short-Term Memory network…
Guy Y. Cornejo Maceda, François Lusseyran, Bernd R. Noack
xMLC is the second book of this 'Machine Learning Tools in Fluid Mechanics' Series and focuses on Machine Learning Control (MLC). The objectives of this book are two-fold: First, provide an introduction to MLC for students, researchers, and newcomers on the field; and second, share an open-source code, xMLC, to…
Robert M. Kent, Wendson A. S. Barbosa, Daniel J. Gauthier
Machine learning provides a data-driven approach for creating a digital twin of a system - a digital model used to predict the system behavior. Having an accurate digital twin can drive many applications, such as controlling autonomous systems. Often, the size, weight, and power consumption of the digital twin or…
Thomas Duriez, Vladimir Parezanović, Laurent Cordier, Bernd R. Noack + 4 more
'Joël Delville' 'Jean-Paul Bonnet' 'Marc Segond' 'Markus Abel'] We propose a general model-free strategy for feedback control design of turbulent flows. This strategy called 'machine learning control' (MLC) is capable of exploiting nonlinear mechanisms in a systematic unsupervised manner. It relies on an evolutionary…
Ying–Cheng Lai, Ling-Wei Kong, Zheng-Meng Zhai, Mohammadamin Moradi + 2 more
'Bryan Glaz' 'Mulugeta Haile'] Nonlinear tracking control enabling a dynamical system to track a desired trajectory is fundamental to robotics, serving a wide range of civil and defense applications. In control engineering, designing tracking control requires complete knowledge of the system model and equations. We…
Robert M. Kent, Wendson A. S. Barbosa, Daniel J. Gauthier
Machine learning provides a data-driven approach for creating a digital twin of a system – a digital model used to predict the system behavior. Having an accurate digital twin can drive many applications, such as controlling autonomous systems. Often, the size, weight, and power consumption of the digital twin or…
Michel Fliesś, Cédric Join
This paper states that Model-Free Control (MFC), which must not be confused with Model-Free Reinforcement Learning, is a new tool for Machine Learning (ML). MFC is easy to implement and should be substituted in control engineering to ML via Artificial Neural Networks and/or Reinforcement Learning. A laboratory…
Suk Ki Lee, Hyunwoong Ko
Dynamic manufacturing processes exhibit complex characteristics defined by time-varying parameters, nonlinear behaviors, and uncertainties. These characteristics require sophisticated in-situ monitoring techniques utilizing multimodal sensor data and adaptive control systems that can respond to real-time feedback while…
Johannes Günther, Elias Reichensdörfer, Patrick M. Pilarski, Klaus Diepold + 1 more
'Klaus Diepold' 'Yanzheng Zhu'] Modern automation systems largely rely on closed loop control, wherein a controller interacts with a controlled process via actions, based on observations. These systems are increasingly complex, yet most deployed controllers are linear Proportional-Integral-Derivative (PID) controllers.…
Ulbio Alejandro-Sanjines, Anthony Maisincho-Jivaja, Victor Asanza, Leandro L. Lorente-Leyva + 4 more
'Leandro L. Lorente-Leyva' 'Diego H. Peluffo-Ordóñez' 'Huiling Chen' 'Yudong Zhang' 'Shuihua Wang'] Automated industrial processes require a controller to obtain an output signal similar to the reference indicated by the user. There are controllers such as PIDs, which are efficient if the system does not change its…
Zhiwen Huang, Jianmin Zhu, Jiajie Shao, Zhouxiang Wei + 1 more
For improving the dynamic quality and steady-state performance, the hybrid controller based on recurrent neural network (RNN) is designed to implement the position control of the magnetic levitation ball system in this study. This hybrid controller consists of a baseline controller, an RNN identifier, and an RNN…
Xiaoliang Luo, Robert M. Mok, Brett D. Roads, Bradley C. Love
Complex behavior is supported by the coordination of multiple brain regions. We propose coordination is achieved by a controller-peripheral architecture in which peripherals (e.g., the ventral visual stream) aim to supply needed inputs to their controllers (e.g., the hippocampus and prefrontal cortex) while expending…
Authors not listed
This paper presents an empirical comparison of process control algorithms, with particular emphasis on classical Proportional–Integral–Derivative (PID) control, Model Predictive Control (MPC), and neural network-based methods in the context of complex industrial plants. Since industrial sectors frequently demand…
Olivier Codol, Jonathan A. Michaels, Mehrdad Kashefi, J. Andrew Pruszynski + 1 more
Artificial neural networks (ANNs) are a powerful class of computational models for unravelling neural mechanisms of brain function. However, for neural control of movement, they currently must be integrated with software simulating biomechanical effectors, leading to limiting impracticalities: (1) researchers must rely…
Reza Yousefian, Sukumar Kamalasadan
—This paper reviews the current status and challenges of Neural Networks (NNs) based machine learning approaches for modern power grid stability control including their design and implementation methodologies. NNs are widely accepted as Artificial Intelligence (AI) approaches offering an alternative way to control…
Bodo Rueckauer, Marcel van Gerven
Brain-machine interfaces have reached an unprecedented capacity to measure and drive activity in the brain, allowing restoration of impaired sensory, cognitive or motor function. Classical control theory is pushed to its limit when aiming to design control laws that are suitable for large-scale, complex neural systems.…
Harrison Ritz, Matthew R. Nassar, Michael J. Frank, Amitai Shenhav
In order to behave adaptively in environments that are noisy and non-stationary, humans and other animals must monitor feedback from their environment and adjust their predictions and actions accordingly. An under-studied approach for modeling these adaptive processes comes from the engineering field of control theory…
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…
Swarnendu Mandal, Swati Chauhan, Umesh Kumar Verma, Manish Dev Shrimali + 1 more
'Manish Dev Shrimali' 'Kazuyuki Aihara'] We demonstrate a data-driven technique for adaptive control in dynamical systems that exploits the reservoir computing method. We show that a reservoir computer can be trained to predict a system parameter from the time series data. Subsequently, a control signal based on the…
Yannick Ureel, Maarten R. Dobbelaere, Yi Ouyang, Kevin De Ras + 3 more
By combining machine learning with design of experiments, so-called active machine learning, more efficient and cheaper research can be conducted. Machine learning algorithms are more flexible, and are better at investigating the processes spanning all length scales of chemical engineering. While the active machine…
Giovanny Marquez, Harika Dechiraju, Prabhat Baniya, Houpu Li + 6 more
Precision medicine tailors treatment in a way that accounts for variations in patient response. Treatment strategies can be determined based on factors such as genetic mutations, age, and diet. Another way of implementing precision medicine in a dynamic fashion is through bioelectronics equipped with real-time sensing…
Muhammad Hanzla, Abdul Rehman Shinwari
Machine Learning (ML) can be defined as a class of Artificial Intelligence for automated data analysis, which is capable of detecting patterns in data. The extracted patterns can be used to predict un-known data or to assist in decision-making processes under uncertainty. Recent advances in experimental and…
Authors not listed
Integrating machine learning (ML) into drug discovery has ushered in a new era of innovation, dramatically enhancing the efficiency and precision of identifying and developing new therapeutics. This review provides a comprehensive analysis of the current applications of machine learning in drug discovery, focusing on…