24 papers · ranked by Valyu relevance
Sicheng He, Shugo Kaneko, Max Howell, NAN LI + 1 more
Multidisciplinary engineering system design typically employs a sequential process, progressing from system dynamics to design variables and control. However, this process is inefficient and may lead to a suboptimal design. We propose formulating the optimal control and multidisciplinary design optimization (MDO)…
Chenchen Zhou, Shaoqi Wang, Hongxin Su, Xinhui Tang + 2 more
Self-optimizing control is a strategy for selecting controlled variables, where the economic objective guides the selection and design of controlled variables, with the expectation that maintaining the controlled variables at constant values can achieve optimization effects, translating the process optimization problem…
Evan M. Dastin-van Rijn, Elizabeth M. Sachse, Michelle Buccini, Bradley Angstadt + 3 more
Identifying effective deep brain stimulation (DBS) parameters for psychiatric disorders has historically been a time-consuming and error prone process due to a lack of an objective and rapid readout of target circuit engagement. Cognitive control may have use as a biomarker of treatment efficacy but it has yet to be…
Serdar Ekinci, Davut Izci, Mostafa Jabari, Mohit Bajaj + 2 more
Achieving precise and stable engine speed regulation in spark-ignition (SI) systems remains a challenging task because of the inherent nonlinearities, time-varying characteristics, and external disturbances of internal combustion engines (ICEs). Conventional proportional-integral-derivative (PID) controllers often fail…
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…
Ossama Abdelkhalik, Aimar Negrete
Modern optimal control theory involves adjoining the already known equations of motion of a dynamic system to the objective function using dynamic costates; this is done in order to constrain the optimal control solutions to satisfy the equations of motion. The use of costates increases the number of variables and…
Xin Zhao
This paper presents a novel digital twin-driven intelligent robotic arm adaptive control system that integrates hybrid neural network architectures to address the limitations of traditional feedback control mechanisms in complex operational environments. The proposed approach uniquely combines Convolutional Neural…
M. Sai Neeharika, V. Shobhana, Nitish Katal
The classical controller design methods, often lead to sub-optimal performance, especially when implemented for plants exhibiting complex dynamics like integrals, non-minimum phase zeros, time-delays, etc.; and the controllers synthesised using classical methods can result in poor time domain characteristics, and…
Anas Abdelkarim, Daniel Görges, Holger Voos
Factor graph optimization serves as a fundamental framework for robotic perception, enabling applications such as pose estimation, simultaneous localization and mapping (SLAM), structure-from-motion (SfM), and situational modeling. Traditionally, these methods solve unconstrained least squares problems using algorithms…
Vincent Ton, Seungmoon Song
Neuromechanical simulations provide a powerful framework for investigating how neural control architectures generate and regulate human locomotion. Numerous biologically inspired locomotion controllers have been proposed, including reflex-based, central pattern generator (CPG)-based, and muscle synergy-based models.…
Aykut Fatih Güven, Erdinç Şahin, Onur Özdal Mengi, Mohit Bajaj + 1 more
The increasing integration of renewable energy sources introduces severe intermittency in multi-area power systems (MAPS), resulting in significant voltage and frequency fluctuations. This study addresses this problem by implementing an automatic generation control (AGC) framework for a two-area hybrid power system…
Chenchen Zhou, Hongxin Su, Xinhui Tang, Yi Cao + 2 more
Self-optimizing control (SOC) aims to maintain near-optimal process operation by judiciously selecting controlled variables (CVs). In this series of work, the generalized global SOC (g2SOC) approach is proposed, which extends the concept of SOC to the whole operation space and uses general nonlinear functions to design…
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…
Gökhan Yüksek, Kaan Can, Serdar Ekinci, Erdal Akin
This study addresses the optimal temperature regulation problem of a laboratory-scale heat flow system using a two-degree-of-freedom proportional-integral-derivative (2DOF-PID) controller tuned by a newly developed enhanced quadratic interpolation optimization (eQIO) algorithm. The main contribution lies in improving…
Adrian-Mihail Stoica, Isaac Yaesh, Lu Wei
This paper presents an optimal $L_{2}$-induced control problem for systems with multiple sector-bounded nonlinearities. Sufficient boundedness conditions for the $L_{2}$-induced norm are derived in terms of a specific system of linear matrix inequalities (LMIs). Based on these conditions, an optimal state feedback…
Sumedh S Nagrale, Alik S Widge
The use of Deep Brain Stimulation (DBS) on the ventral capsule/ventral striatum (VCVS) has therapeutic potential for patients with refractory psychiatric disorders, but clinical success is impeded by the need for a time-consuming and trial-and-error process when setting the parameters, this process relying on…
Authors not listed
This paper introduces Optical Fiber Chemistry (OFC) as a fourth-generation catalytic paradigm, distinguished not by incremental improvements in catalyst materials but by a fundamental reconfiguration of the catalytic reaction platform. By employing optical fibers as active photonic control elements, OFC achieves gen-…
Authors not listed
Azo dyes constitute one of the largest and most commercially important classes of synthetic colorants, widely applied in textiles, plastics, inks, and food. However, their manufacture through traditional batch processes is often constrained by safe-ty risks, poor heat and mass transfer, and inconsistent product…
Mohamed Barakat, Ahmed Donkol, Mohammed Sekhi, A. M. Mabrouk
Maintaining frequency stability in interconnected power systems (IPSs) is a critical challenge, particularly under sudden load changes and nonlinear constraints. Conventional PID and fractional-order controllers (FOPID, TID, FOID, and cascaded FO-PID structures) either lack adaptability or introduce excessive…
Authors not listed
This comprehensive review examines the evolution of autonomous materials synthesis laboratories that integrate artificial intelligence with advanced robotics to accelerate discovery. Traditional materials development pipelines typically require 10-20 years, but self-driving laboratories (SDLs) and Materials…
GilHwan Kim, Fabrizio Sergi
Human-in-the-loop optimization (HILO) is an established method for identifying subject-specific optimal controllers for performance augmentation. For HILO algorithms to be useful in rehabilitation, however, the optimization algorithm may need to account for how the human response changes over time in response to…
Sigurd Skogestad
Advanced regulatory control (ARC), also known as advanced PID architectures, is a simple and robust way of controlling processes with changing and possibly conflicting constraints, where it previously was believed - at least in academia - that model-based solutions, such as MPC, were the only effective solution. To…
Zesheng Yao, Zhen-Hua Wan, Canjun Yang, Qingchao Xia + 1 more
Model-free deep reinforcement learning (DRL) methods suffer from poor sample efficiency. To overcome this limitation, this work introduces an adaptive reduced-order-model (ROM)-based reinforcement learning framework for active flow control. In contrast to conventional actor--critic architectures, the proposed approach…
Robert M. Salati, Geng Li, Spencer Williams, Benjamin J. Fregly
Personalized computational neuromusculoskeletal models have great potential for optimizing the design of clinical treatments for movement impairments. While many software tools address specific parts of the model personalization and treatment optimization processes, they typically require significant programming…