21 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)…
Roba Tarek AbdelFatah, Raafat Shalaby, Irene Samy Fahim, Mohamed Mahran Kasem
This study presents the development, optimized PID control, and experimental validation of a novel Multi-Stage Parabolic Trough Collector (MPTC) for solar water heating systems, aiming to enhance thermal efficiency and adaptability under varying environmental conditions. The research is structured into three key…
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…
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…
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…
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-…
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…
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…
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…
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…
Benita Nortmann, Thulasi Mylvaganam
In this paper, we combine a data-driven system representation with a framework to systematically construct (approximate) solutions to nonlinear optimal control problems. By immersing the unknown dynamics into an extended state space, solutions are characterised via purely data-dependent algebraic conditions. This…
Mrinal Kanti Rajak, Rajen Pudur
This paper presents a novel Mitochondrial Energy Production Optimization (MEPO) algorithm for enhancing grid-connected inverter control under weak grid conditions. The proposed bio-inspired approach addresses critical challenges in maintaining power quality and system stability in low Short Circuit Ratio (SCR)…
Alaa Abdelhamid Mohamed, Mohammed Hamouda Ali, Ahmed I. Omar, Mohammed Mehanna
Hydrogen is one of the potential clean energy sources that might help to address two critical global issues: energy scarcity and environmental concerns. Using fossil fuels for hydrogen generation has drawbacks, such as increased greenhouse gas emissions throughout the process. As a result, finding clean, sustainable…
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…
Luz A. Alvarez, Diego F. de Bernardini, Christophe Gallesco, Zhengmao Li
Model Predictive Control (MPC) is a popular technology to operate industrial systems. It refers to a class of control algorithms that use an explicit model of the system to obtain the control action by minimizing a cost function. At each time step, MPC solves an optimization problem that minimizes the future deviation…
Edward Ma, James Morrissey, Shutong Duan, Ziqi Lu + 10 more
Process optimization for Chinese hamster ovary (CHO) cell culture remains a challenge in biopharmaceutical development because multiple interacting parameters jointly influence productivity and product quality attributes. Traditional design-of-experiments (DoE) methods, while systematic, become impractically expensive…
Emmanouil Alexis, Clarence W. Rowley, José L. Avalos
Achieving complex multi-species control objectives is essential for engineering advanced autoregulated biomolecular devices. This paper addresses the problem of robust steady-state tracking for outputs defined as multiplicative combinations of biomolecular species concentrations. We first introduce a control…
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…
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…
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…
Yinqi Huang, Abhilasha Vishwanath, Yu Karen Du, Matthew F Watson + 5 more
Navigation requires estimating heading and transforming these estimates into actions. Prior models explain how self-motion and landmark cues are combined into heading estimates, but less is known about how these estimates are iteratively transformed into motor commands to reach a goal. Here, we hypothesized that…