47011 research outputs found
Sort by
Advanced feedforward control techniques: Comprehensive review and a real-time industrial application
No full textFeedforward control (FFC), often paired with feedback, is widely applied in industry to improve accuracy under transient conditions and reject disturbances. Despite this, academic attention has been limited, with few comprehensive reviews connecting algorithms to practical applications. This work bridges that gap by surveying classical and advanced FFC strategies, their advantages and drawbacks, and their implementation across diverse sectors. Classical FFC improves tracking and disturbance rejection through methods such as inverse modelling and input shaping, but faces challenges from instability, intensive tuning, and model inaccuracies. Hybrid techniques, including model predictive and active disturbance rejection control, broaden capability yet remain constrained by complexity and sensitivity to nonlinearities. Recent advances introduce look-ahead, adaptive, optimization-based, and data-driven methods. Preview and predictive designs enhance responsiveness but depend on accurate future estimation, while iterative and intelligent approaches reduce modelling requirements at the expense of stability and training demands. Adaptive and optimization-based controllers strengthen robustness but add computational burden and parameter sensitivity. FFC has been deployed across process control, electrical drives, fuel cells, engines, robotics, motion systems, power electronics, and energy systems. These applications consistently show improved tracking and robustness, though adoption is limited by reliance on models, calibration effort, and a lack of hardware validation. Signal acquisition and processing shape the stability and robustness of feedforward-augmented control architectures. A discussion is had reviewing recent advances in noise-aware estimation, delay-robust control, and filtered/learned inverse models to identify practical design strategies for maintaining reliable performance, focusing on medium and high frequency. To demonstrate practical benefits, a feedforward-augmented PID (i.e., feedforward control added to a PID closed loop system) with preview and anti-windup enabled transient testing on absorbing dynamometers, traditionally restricted to steady-state use. Combined with reinforcement learning controllers, this approach reduced error, expanded applicability, and offered a cost-effective alternative to motored dynamometers
A durability evaluation method for CFRP-reinforced circular hollow section joints in a neutral salt spray environment
Full text linkThe durability of carbon fibre-reinforced polymer (CFRP) strengthened steel structures is a critical issue affecting their long-term reliability in practical engineering applications. However, the degradation behavior and durability of CFRP-strengthened circular hollow section (CHS) joints have not been quantified. This study proposed a numerical analysis method for assessing the durability of CFRP strengthened CHS joints based on experiments. First, durability tests in a neutral salt spray environment and tensile failure tests were conducted on the adhesive and CFRP sheets to obtain moisture diffusion parameters and establish mechanical performance formulae. Static tests were conducted on unaged and aged CFRP-reinforced CHS T-joints. Then, FE models of bare and CFRP-reinforced CHS joints were developed. In the models of CFRP-reinforced joints, mechanical property degradation of the CFRP composite layers and the CFRP-to-steel bonding interfaces were considered. FE models of CFRP composite layers and the bonding interface were developed for mass diffusion analysis. A coupled diffusion-mechanical analysis program was proposed to predict the joint’s durability year, which was verified by experimental results. Finally, the effects of interface moisture diffusion coefficient, CFRP length, and number of CFRP layers on the durability year were analyzed. Suggestions were proposed to improve the durability performance of CFRP-reinforced CHS joints. The proposed method provides an effective tool for predicting the long-term performance of CFRP-reinforced CHS joints and supports durability design
Asymptotic analysis of the total quasi-steady state approximation for the Michaelis–Menten enzyme kinetic reactions
Full text linkWe consider a stochastic model of the Michaelis–Menten (MM) enzyme kinetic reactions in terms of Stochastic Differential Equations (SDEs) driven by Poisson Random Measures (PRMs). It has been argued that among various Quasi-Steady State Approximations (QSSAs) for the deterministic model of such chemical reactions, the total QSSA (tQSSA) is the most accurate approximation, and it is valid for a wider range of parameter values than the standard QSSA (sQSSA). While the sQSSA for this model has been rigorously derived from a probabilistic perspective at least as early as 2006 in [4] , a rigorous study of the tQSSA for the stochastic model appears missing. We fill in this gap by deriving it as a Functional Law of Large Numbers (FLLN), and also studying the fluctuations around this approximation as a Functional Central Limit Theorem (FCLT)
A Novel Continuous Control Set Model Predictive Control of Three-Phase Single-Stage Differential Boost Inverter
Full text linkThe three-phase differential boost inverter (DBI) is a specialized category of power inverters that provides single-stage boosting functionality, characterized by high efficiency, cost-effectiveness, and compact size. However, it is challenging to control due to its non-linearity and non-minimum phase characteristics. Specifically, when model predictive control (MPC) is applied, the non-minimum phase characteristics can lead to unstable internal dynamics. To solve this problem, this paper proposes a novel continuous control set MPC (CCS-MPC) method for the three-phase DBI. Based on the established predictive models of circuit currents and voltages, the appropriate duty cycle for the next sampling period can be predicted. An additional inductor current-based stability constraint is designed to achieve system stability. This control strategy significantly optimizes the dynamic performance and the second-order harmonics in the output currents. Compared with finite control set MPC (FCS-MPC), CCS-MPC features fixed switching frequency with higher steady-state accuracy and lower current ripple. Additionally, complex parameter design is avoided compared to PI-based control. Simulation and experiment are conducted to verify the efficacy of the proposed CCS-MPC for the three-phase DBI
A Multiwavelength Evaluation of AGN in the Post-Starburst Phase
Full text linkThe quenching of star formation is a crucial phase in galaxy evolution. Although active galactic nuclei (AGN) feedback has been proposed as a key driver of this transition, the lack of strong AGN in nearby quenching galaxies raises questions about its effectiveness. In this study, we investigate AGN activity in post-starburst galaxies (PSBs), star-forming galaxies (SFGs), and quiescent galaxies (QGs) at z < 0.2, using multiwavelength data from eROSITA/eFEDS (X-ray), WISE (mid-infrared), and FIRST (radio). We assess AGN incidence and strength across different stages and apply stacking techniques to undetected galaxies to recover average AGN properties. Comparisons between observed luminosity and that expected from star formation (L obs /L SF) show that PSBs are consistent with star formation dominating their radio and X-ray emission. Although PSBs exhibit a MIR AGN incidence rate twice that of SFGs, their estimated AGN luminosities are small compared to those of MIR AGN in the literature. PSBs overall do not display significantly enhanced AGN emission relative to mass-and redshift-matched SFGs and QGs. While the presence of obscured, low-luminosity AGN in PSBs cannot be excluded, such AGN, if present, could be fueled by residual gas from the preceding starburst and may not play a dominant role in quenching. Our findings suggest that AGN's role in quenching at low redshift is more subtle than violently removing the gas-the feedback is likely more "preventive" than "ejective"
Using AI for the documentation of intangible cultural heritage
Full text linkThis paper explores the potential roles AI could play in interpreting, documenting, preserving, and making accessible intangible forms of cultural heritage for future generations. The paper starts by analysing the changing role of documentation in collections and archives (primarily museum archives and/or archives that include artworks). The paper then investigates what these changes in documentation may mean for archives, introducing also the notion of a dynamic archive. Finally, the paper utilises ChatGPT to explore how AI defines documentation, how it states documentation can be used, and what research might be necessary to establish the responsible and trustworthy use of AI in this context. The two case studies explored through ChatGPT are Lynn Hershman Leeson’s Agent Ruby (1998-2002) and Blast Theory’s Cat Royale (2022). Our argument is that the use of AI in the documentation of intangible cultural heritage will impact not only its presentation, exhibition, and conservation, but also change the role that archives will play in years to come
A Multimodal Adaptive Framework for Social Interaction with the MiRo-E Robot
Full text linkHighlights: This study explores how robots can interact with people in a more engaging way. By combining real-time user engagement estimation with advanced language models, the system allows robots to respond consistently through both speech and body language. Tests show that this approach makes interactions feel more natural while improving user engagement and task success. What are the main findings? Adapting the interaction based on user engagement significantly enhances user experience. The MiRo-E social HRI platform lends itself well to integrating verbal and nonverbal HRI. What are the implications of the main findings? Enhancing perceived naturalness is an important goal in social human–robot interaction. Generative AI and multimodality offer a credible pathway to achieving this goal. Adaptivity is a key component of social human–robot interaction (HRI) towards achieving more natural and human-like interactions. Current interactive systems tend to rely on preset and repetitive verbal communication and isolated nonverbal interactions, which results in unappealing engagement. This study proposes an integrated framework that combines a coordinated nonverbal interaction system based on real-time emotion expression with a fine-tuned large language model-based verbal communication system, resulting in more engaging and context-aware interaction. The design utilises the MiRo-E as the zoomorphic social interaction platform, with the aim of enhancing the consistency across verbal and nonverbal modalities and improving user engagement through adaptive and emotionally aligned responses. To evaluate the effectiveness of the approach, a user study was conducted with tasks designed to assess user engagement, task performance, and the perceived naturalness of interaction. Task performance metrics and subjective questionnaire responses indicate that the framework significantly enhances user experience, improving task completion rates, engagement, and perceived naturalness
A dynamic modelling framework to assess the impact of manual emergency responses on nuclear power plant resilience
Full text linkThe escalating frequency of extreme natural disasters due to climate change poses unprecedented risks to nuclear power plants (NPPs), underscoring the need to quantify the efficacy of manual emergency responses in enhancing resilience. However, quantifying the effectiveness of these measures remains a significant challenge. This paper develops a Petri Net-based resilience assessment framework to model multi-phase accident progression in an NPP subjected to extreme events, including loss of coolant accidents and station blackout scenarios. The PN models integrate stochastic system degradation processes, automated safety responses, manual recovery processes, and offsite resource mobilisations. The simulation results show that the developed model can successfully assess the impact of the efficiency of human responses on NPP resilience. This work provides actionable insights for optimising NPP emergency procedures and resource allocation strategies. The findings underscore the importance of timely manual interventions during emergency scenarios, offering a quantitative basis for enhancing nuclear safety management policies
Somaforming on an alien Earth
Full text linkThe realities of climate catastrophe increasingly threaten opportunities for multispecies liveability on this planet; Earth is becoming more alien every day through unequal intensities of weirding. We explore the conceptual provocations in the idea of ‘terraforming Terra’: that is, exploring the politics of transforming exoplanets – fabulated in the pages of science fiction – in contemporary empirical situations on an alien Earth. Gleaning insight from the speculative fiction of Becky Chambers in her 2019 novella To Be Taught, If Fortunate, we examine her concept of ‘somaforming’ in relation to terraforming. Chambers presents somaforming as a technology deployed to adapt bodies to alien planets, as an explicit alternative to terraforming, enabling human survival in hostile exoplanetary environments. We read somaforming with empirical reference to ongoing technoscientific efforts seeking to adapt bovine bodies to the imagined futures caused by global warming, in preparation for weird worlds to come. We analyse two scientific experiments that attempt to adapt cattle to the negative environmental impacts of climate change – respectively parasitism and heat stress – affecting animal welfare and agricultural productivity. While these somaforming practices each use a different technology transforming the cows’ body – through paint or gene editing – we argue that both illustrate the dangers of allowing ‘somaforming experiments’ to pre-empt an alien Earth, in its foreclosing of alternatives to ‘business as usual’
Local control of cellular proliferation underlies neuromast regeneration in zebrafish
Full text linkBiological systems are never in equilibrium, yet they maintain stability in the face of continuous external disturbances. A prime example of this is organ regeneration, during which organs are reliably rebuilt through controlled cellular proliferation. In this study, we employ a cell-based computational modelling approach to investigate the proliferative response of an organ after injury. We developed a minimal two-dimensional Cellular Potts Model (CPM) using empirical data from regenerating neuromasts in larval zebrafish. Remarkably, the CPM both qualitatively and quantitatively recapitulates the regenerative response of neuromasts following laser-mediated cell ablation. Assuming that cell proliferation is locally regulated by a delayed switch, we discovered that mitotic activity ceases once the type-dependent number of neighbouring cells exceeds a deterministic critical threshold. An intriguing corollary of our findings is that a local negative feedback loop among identical cells may represent a general mechanism underlying organ-level proportional homeostasis
Access Repository Dashboard
Do you manage Open Research Online? Become a CORE Member to access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard! 👇