Search Results (111)

Fault-tolerant control systems deployed in manufacturing and process industries must maintain output regulation under actuator degradation and parametric drift while producing operator-readable intervention records. This paper proposes a three-layer cognitive supervisory control framework: a continuously synchronized nominal plant model serves as a lightweight digital twin, driving residual-based anomaly detection that responds to model-mismatch faults before tracking error accumulates; an event-triggered supervisory layer applies composite anomaly scoring, risk estimation, and dwell-time-constrained mode switching over a bank of pre-verified stable controllers; and an optional bounded LLM reasoning agent forming Layer 3 of the cognitive architecture produces real-time natural-language audit records at each supervisory event. The framework is functionally non-intrusive—the closed-loop response is numerically identical to standalone LQI in three of four benchmark scenarios and on an unstable plant variant, with zero supervisory interventions. Under abrupt 75% actuator loss, a single targeted intervention achieves lower overshoot and better tracking than all baselines, including MPC whose feasibility-degradation under reduced actuator authority is confirmed empirically. On near-undamped plants representative of compressor and pipeline dynamics, integral absolute error improves significantly relative to standalone LQI; on a distributed thermal process representative of laser welding and induction heating, IAE improves substantially under abrupt heater power loss. Parametric sensitivity analysis across fault severity, detection threshold, onset timing, measurement noise, score weights, and dwell-time confirms robustness of the detection architecture. The LLM layer’s contribution is regulatory rather than performative: its value is the causally connected audit record generated at the moment of each decision, not IAE improvement—a form of real-time explainability that after-the-fact attribution methods such as SHAP and LIME cannot provide by construction. Full article

Climate change is increasingly reshaping species habitat suitability worldwide. Primula L., the largest genus in Primulaceae, comprises 404 species in China (including 296 endemic species) and is characterized by high endemism and numerous rare and endangered taxa. However, global warming has intensified habitat fragmentation and loss, while its distribution patterns and key environmental drivers remain insufficiently understood. We compiled 7647 occurrence records of 404 wild Primula species in China and integrated 60 environmental variables (climatic, topographic, and soil factors). Using the MaxEnt model combined with ArcGIS spatial analysis, we assessed current and future habitat suitability, identified dominant environmental drivers, and quantified conservation gaps under multiple climate scenarios. Species richness is highly concentrated in Sichuan (186 species), Yunnan (177 species), and Xizang (165 species), with the Hengduan Mountains and eastern Himalayas representing the core distribution area and showing clear peripheral differentiation. The optimized MaxEnt model performed well (AUC = 0.858), identifying temperature seasonality (bio4, 39.8%) and elevation (27.1%) as the main limiting factors. The total suitable habitat area is 268.52 × 10⁴ km², with high-suitability areas mainly distributed in the Hengduan Mountains, southeastern Qinghai–Xizang Plateau, and the Central Mountain Range of Taiwan. Under three shared socioeconomic pathway (SSP) scenarios (SSP126, SSP245, and SSP585), suitable habitat shows a persistent decline, most pronounced under SSP585 in the 2090s (−20.73%), accompanied by a 25.86% reduction in low-suitability areas. Localized expansion of high-suitability habitats suggests that the Hengduan Mountains and southeastern Qinghai–Xizang Plateau may act as potential climatic refugia. Habitat loss consistently exceeds habitat gain, while the distribution centroid shifts westward and northwestward, with migration distances increasing under higher-emission scenarios. Conservation gap analysis indicates that 90.01% of high-suitability habitats lie outside the current protected area network, revealing a strong mismatch between biodiversity hotspots and conservation coverage. These findings highlight the urgent need to expand protected areas and establish micro-reserves in key gap regions (southwestern Sichuan, northwestern Yunnan, southeastern Xizang, and southern Gansu), and to integrate climate-driven migration corridors into conservation planning to support long-term alpine plant persistence under climate change. Full article

Consolidated bioprocessing (CBP) is difficult to monitor because enzyme production, lignocellulose degradation, sugar release, and fermentation occur simultaneously under sparse measurement, feedstock variability, and plant–model mismatch conditions. This study proposes a computational sensor-set design framework for digital-twin-assisted CBP monitoring. A five-state virtual plant, consisting of active biomass, cellulolytic enzyme activity, residual insoluble substrate, soluble sugar, and ethanol, was used to evaluate all 16 ethanol-mandatory measurement packages formed from ethanol, sugar, biomass, enzyme, and residual-substrate proxy channels. Candidate sensor sets were assessed using finite-difference output sensitivities, Fisher-information-based state-observability and parameter-identifiability analyses, eigenvalue and parameter-correlation diagnostics, and paired Monte Carlo unscented Kalman filter soft-sensing reconstruction. Within the tested five-state virtual-plant benchmark and with the specified excitation schedule, noise assumptions, burden indices, and scoring objective, ethanol-only sensing provided the weakest support for state-aware CBP digital-twin reconstruction. At a $6\mathrm{h}$ sampling interval, the state-observability log-pseudodeterminant increased from 4.18 with ethanol-only sensing to 8.56 after adding soluble sugar and to 16.42 with full-proxy monitoring. The ethanol–sugar–biomass–substrate package also gave strong reduced state-observability performance, with log-pseudodeterminants of 15.12, 13.76, and 12.51 at 6, 12, and $24\mathrm{h}$, respectively. Biomass and enzyme proxies contributed strongly to parameter learning, and the ethanol–sugar–biomass–enzyme package gave the strongest active parameter-identifiability performance, with log-pseudodeterminants of 10.82, 9.06, and 6.67 at 6, 12, and $24\mathrm{h}$, respectively. In the paired soft-sensing analysis, full-proxy monitoring reduced the mean latent-state RMSE from 1.1899 to 0.3756, followed by ethanol–biomass–enzyme–substrate with 0.3843 and ethanol–sugar–biomass–substrate with 0.4121. The primary aggregate ranking identified ethanol–sugar–biomass–substrate as the best overall package, with a sensor-value score of 0.8432 and a burden index of 7.0, followed by full-proxy monitoring with a score of 0.8173 and a burden index of 10.0. Robustness tests showed that ethanol–sugar–biomass–substrate remained top-ranked under uniform noise scaling, full UKF missingness, delay and bias stress test conditions, most scoring-weight scenarios, and all tested sensor-specific burden workflows. Full-proxy monitoring remained a close competitor under independent sensor-specific noise variation conditions and became top-ranked for some alternative operating trajectories. The proposed framework provides a simulation-based method for prioritizing informative measurement packages before implementing CBP digital twins in laboratory and pilot-plant settings. Full article

This paper presents a two-stage stochastic unit commitment model to mitigate the costs and reliability effects of high wind energy penetration into the power system. Wind energy variability/uncertainty is explored through this system. Mixed-integer linear programming (MILP) is used to solve the model and find optimal unit commitment and dispatch variables under uncertainty in wind conditions. The model champions reduced reliance on deterministic approaches, lowered costs, increased wind utilization, and provides reliable systems that can sustain 24 h. Wind energy is expected to constitute a significant share of future electricity generation portfolios; however, its inherent intermittency and variability often lead to mismatches between energy supply and demand. This uncertainty complicates generation scheduling decisions, particularly in determining which power plants to commit, their operating durations, and the optimal dispatch timing. Consequently, advanced optimization strategies are required to ensure efficient and reliable system operation. The proposed approach provides a structured, robust framework for optimal generation scheduling and resource allocation, even under limited wind availability. By enhancing the integration of wind energy into the power system, the method minimizes operational costs, improves resource utilization, and maintains system reliability and stability. Full article

A new fractional dynamic sliding mode control (FD-SMC) framework is introduced to reduce chattering in the control of fractional-order chaotic systems. In this method, chattering is eliminated by placing a fractional integrator before the system control input. As a result, the augmented system has a higher dimension than the original system, meaning that additional states are introduced. Effective control therefore requires identifying or estimating these new states or the corresponding plant model. To address this issue, a robust optimal fractional loop-transfer-recovery observer (ROF-LTRO) is developed. Furthermore, the key advantage of sliding mode control (SMC)—its invariance to matched uncertainties—is often lost in many plants such as chaotic systems, because many of them contain mismatched uncertainties. To restore and extend the invariance property, multiple sliding surfaces combined with a virtual control input are employed. In addition, the proposed FD-SMC and ROF-LTRO do not rely on prior knowledge of uncertainty bounds, which is beneficial for practical implementation. Then, a two-stage design procedure based on two-surface definition is presented, and simulation results are provided for the extended fractional Duffing–Holmes chaotic system (EF-DHCS) under both matched and mismatched uncertainties. Full article

With the increasing prevalence of uncertainties and variability in modern energy systems, model predictive control (MPC) often faces the challenge of predictive model mismatch. This paper proposes a desired-dynamics-based predictive control (DDPC) framework, in which an inner shaping layer is introduced to transform the raw plant into a desired dynamic model for the outer MPC. A unified design methodology is developed, including equivalent-model construction, desired-dynamics selection, and two inner-layer realizations based on desired dynamic equation (DDE)-PID and active disturbance rejection control (ADRC). In this way, the prediction model used by MPC is no longer the original uncertain plant but an explicitly shaped equivalent model determined by inner-layer controller parameters. The proposed method is validated on linear and nonlinear benchmark plants, together with frequency-domain and Monte Carlo robustness analyses. Results show that DDPC improves disturbance-rejection ability and enhances robustness against model mismatch and parameter perturbations. Further evaluation on the superheated steam temperature loop of a high-fidelity 660 MW coal-fired boiler hardware-in-the-loop simulator shows that DDPC reduces the peak-to-peak temperature fluctuation from 22.88 °C to 11.39 °C in the deep peak shaving scenario, corresponding to a 50.2% reduction relative to standard MPC. Full article

Against the backdrop of urbanization and global warming, reducing carbon emissions and achieving carbon neutrality have emerged as focal points in current urban ecological research. Urban green infrastructure (UGI) serves as the primary natural carbon sink within cities; therefore, investigating and optimizing its carbon sequestration services is a crucial step toward realizing carbon neutrality and fostering sustainable urban development. As the core components of urban ecosystems, urban parks provide essential ecosystem services that play a pivotal role in expanding carbon sinks, facilitating energy conservation and emission reduction, and enhancing urban climate resilience. This paper takes 20 parks in Linyi City’s central urban area as examples, systematically quantifies the carbon sequestration effect of urban parks in the central urban area of Linyi City from 2019 to 2024 using methods such as the Carnegie–Ames–Stanford Approach (CASA) and the gravity model, and quantitatively evaluates the equity of urban residents’ access to these services. The study shows that the overall annual average carbon sequestration rate of urban parks in Linyi City’s central area over nearly six years ranges from 202.02 gC·m⁻²·a⁻¹ to 279.31 gC·m⁻²·a⁻¹, while individual park annual averages range from 171.29 to 332.76 gC·m⁻²·a⁻¹, falling within the normal range for cities at the same latitude; in terms of vegetation carbon sequestration capacity, woody plant communities dominate in this region, with annual average carbon sequestration rates approximately 10% higher than those dominated by herbaceous vegetation. In terms of intrinsic activity performance of carbon sequestration, overall, woody-dominated plant communities exhibit greater stability and resilience under extreme weather conditions, experiencing smaller impacts on ecological functions but longer recovery cycles to peak levels. Regarding equity in the supply and demand of ecosystem services, the Gini coefficient in the study area is 0.59, indicating an extremely imbalanced state; within the same park service range, up to 60% of residents do not benefit from carbon sequestration ecosystem services. The urban supply–demand mismatch reveals that approximately 20% of the population resides in high-demand–low-supply areas, experiencing extreme ecological deprivation; only about 13% of the population falls into the high-demand–high-supply category, this group being the high-benefit recipients who enjoy both spatial convenience and high-quality ecological welfare. The theoretical implications for urban green space planning: according to the results, merely expanding park green space area to increase per capita access is myopic and inadvisable in central urban park planning. Instead, greater emphasis should be placed on enhancing ecological service levels beyond basic area requirements, comprehensively improving vegetation quality and ecosystem service capacity of parks. In old urban areas constrained by land use, the hierarchical structure of vegetation should be strengthened, and micro green spaces should have enhanced ecological service capabilities to improve residents’ access rights through higher service quality. In newly developed urban areas, planning should balance quantity and quality to serve more people and alleviate urban ecological pressures. Overall, by quantitatively assessing the carbon sequestration capacity and the socio-spatial equity of ecosystem services provided by urban parks in Linyi City, this study offers robust empirical evidence and methodological tools for sustainable urban planning, ultimately fostering the sustainable development of urban ecosystems. Full article

To address the problems of short-circuit flow and dead zones, complicated operation and control caused by intermittent influent, and the mismatch between aeration rate and oxygen demand in the Cyclic Activated Sludge System (CASS), a novel Multi-Compartment Fixed-Biofilm Cyclic Activated Sludge System (MCFCASS) was developed. This system operated in continuous-flow mode, and the aeration rate of each compartment was redistributed using a mathematical model. The results show that the plug flow ratio of the MCFCASS reactor increased from 18.75% to 31.25% compared with the CASS reactor. After aeration rate redistribution, the average total nitrogen (TN) removal efficiency of the MCFCASS reactor rose from 83.34% to 86.80%, and the effluent TN concentration consistently met the Grade I-A limit (15 mg/L) specified in the Discharge Standard of Pollutants for Municipal Wastewater Treatment Plant (GB 18918-2002). The average removal efficiencies of chemical oxygen demand (COD) and ammonium nitrogen (NH₄⁺-N) increased from 91.58% and 93.39% to 92.98% and 94.57%, respectively. Microbial community analysis revealed that after aeration rate redistribution, the relative abundances of Pseudomonadota, Bacteroidota, and Bacillota in the pre-reaction zone of MCFCASS were 39.17%, 17.78%, and 10.33%, respectively. In addition, the abundances of some functional bacterial groups in the first and fourth compartments of the main reaction zone shifted adaptively in response to the aeration rate redistribution, consistent with the trends in pollutant removal contributions in these compartments. Hierarchical clustering and principal coordinate analysis (PCoA) further indicated that aeration rate redistribution influenced the microbial community structure. The above laboratory-scale optimization results may provide a preliminary reference for aeration control and improvement of denitrification performance in similar processes. Full article

This paper examines an algorithm and evaluates the upper limits of technical parameters for step-by-step management of forecast coverage for aggregated generation from solar power plants (SPPs) in Ukraine, given the high share of renewable energy sources in the integrated power system of Ukraine. The relevance of the research is due to the growth in the installed capacity of SPPs, stricter requirements for forecasting accuracy, and the full financial responsibility of producers for imbalances in accordance with the current electricity market model. The problem is formulated as a special case of a hierarchically controlled quasi-dynamic power system, accounting for technological, energy, and economic constraints. The objective function is defined as the minimisation of the total hourly measure of discrepancy between the forecast and actual volumes of electricity supplied, whilst ensuring power balance through energy storage systems and flexible generation. The numerical implementation was carried out using the “SOPS” software and information complex. The input data used were hourly indicators of the forecasted and actual generation of Ukraine’s solar power plants for 2021–2025, published by the state-owned enterprise “Guaranteed Buyer”. Hourly, daily and monthly operating parameters for aggregated solar power generation in 2025 have been calculated. The calculations show that the maximum hourly mismatch between forecasted and actual solar generation in 2025 reached 3116 MW, while the maximum daily mismatch exceeded 19.8 GWh. Under the assumed operating conditions, an energy storage system with 30,000 MWh capacity and flexible generation of up to 7500 MW enabled full imbalance compensation, achieving IMB(t) = 0 for all hourly intervals in the analysed case. The required volumes of flexible generation and the operating parameters of the storage systems have been determined. The practical significance of the results lies in their potential use for operational planning of the operating modes of solar power plants, energy storage systems, and flexible generation on a daily and hourly basis, as well as for justifying technical and economic decisions aimed at reducing imbalances. The results obtained confirm the effectiveness of the proposed step-by-step control algorithm and demonstrate the potential to minimise imbalances through the rational coordination of solar power plants, energy storage systems, and flexible generation capacities. Full article

To address issues existing in current multi-media pollution assessment, such as data mismatch, parameter conflicts, and inadequate characterization of nonlinear effects, this study developed a multi-factor synergistic assessment methodological system encompassing “data preprocessing-parameter calibration-damage quantification-model coupling”. A three-stage parameter calibration system of “inheritance-linkage-sensitivity screening” was established to achieve cross-media parameter synergy; an Environmental Damage Entropy (EDE) model was constructed based on information entropy to quantify the nonlinear coupled damage of multiple factors; and the optimal governance threshold was determined by combining the coupling theory of marginal damage and governance cost. Taking a multi-media pollution incident (atmosphere-soil-surface water-groundwater) caused by a chemical plant explosion as a case study, pollution chain identification, damage quantification, ecological risk cascading effect analysis, and health risk assessment were conducted. The results show that this method can accurately identify key pollution pathways. Based on the calculation of Environmental Damage Entropy (EDE = 0.604) and the synergy coefficient (δ = 1.32), the comprehensive damage value was quantified as 8.21 million yuan. Additionally, the threshold exceedance characteristics of various media were identified, reflecting the cumulative and lagging nature of ecological risk cascading effects. The method proposed in this study can accurately identify key pollution pathways and quantify comprehensive damage as well as ecological risks, providing scientific support for the allocation of multi-media pollution governance responsibilities and precise prevention and control. Full article

The control of Twin-Rotor Multi-Input Multi-Output (TRMS) systems presents a significant challenge due to high nonlinearity, strong aerodynamic cross-coupling, and the inevitable discrepancies between theoretical models and physical plants. This paper first exposes the instability of conventional Backstepping control under real hardware conditions, where unmodeled dynamics and parametric uncertainties drive the yaw subsystem into divergent oscillation, then proposes and experimentally validates two advanced architectures to overcome this limitation. The first is an online adaptive Backstepping gain-tuning scheme based on a novel Rate-Constrained Sequential Quantum Black Hole Optimization (RS-QBHO) algorithm. The second is a Hybrid Backstepping–Sliding Mode Control (BSMC) architecture that integrates structural disturbance rejection directly into the recursive design. Both schemes are formally verified via Lyapunov stability analysis and validated on a physical TRMS rig under identical hardware-in-the-loop conditions. Experimental results confirm that while the standard Backstepping controller failed in the yaw axis with an RMSE of 2.5624 rad, both proposed methods achieved stabilization. The QBHO-tuned controller yielded RMSE values of 0.0799 rad for pitch and 0.2305 rad for yaw, while the BSMC strategy proved superior, achieving 0.0682 rad and 0.1858 rad, respectively. These findings demonstrate that while meta-heuristic optimization effectively compensates for parametric mismatches, the passive disturbance rejection of the sliding mode term offers a more effective solution for mitigating unmodeled aerodynamic dynamics in MIMO flight platforms. Full article

Despite the many advantages of renewable energy sources, the stochastic nature of their generation creates a mismatch between electricity production and demand timing. Without appropriate storage solutions, surplus energy remains unused. Although battery energy storage systems are increasingly applied to improve the flexibility and reliability of power systems, there is still a research gap in forecasting the optimal power and storage capacity of solar power plant–battery energy storage system energy complexes operating in parallel with the grid under short-term forecasting conditions, particularly when economic aspects such as partial leasing of storage capacity are considered. Therefore, the development of energy complexes based on solar power plants with the integration of battery energy storage systems, as well as the development of corresponding computational models, becomes critical for ensuring the stability, flexibility, reliability, and efficiency of power systems. Battery energy storage systems are widely used due to their availability, high response speed, significant energy density, and sufficient power capacity; however, their cost remains relatively high. This paper proposes a methodology and a calculation model for determining the optimal forecasted capacity and the rational storage requirements of an energy complex consisting of a solar power plant and a battery energy storage system operating in parallel with the grid at constant power under short-term forecasting conditions (day-ahead or longer). The proposed approach makes it possible to minimise the costs of energy companies associated with the short-term lease of part of a battery energy storage system when they do not own one, or, if such a system is available, to lease out its unused capacity and obtain corresponding profits. The validation of the computational model uses a dataset of hourly daily power outputs of solar power plants in the Integrated Power System of Ukraine for 2018. Statistical analysis of the obtained results shows that the probability of occurrence of maximum deviations for the optimal capacity of the energy complex (5.4%), as well as for the power and capacity of the battery energy storage system (13% and 18%, respectively), does not exceed 0.05 during the year. The results confirm that the proposed methodology provides a reliable basis for determining optimal parameters of solar power plant–battery energy storage system energy complexes and enables economically efficient use of storage capacity through short-term leasing mechanisms. Although the proposed methodology is applied using solar power plant generation data for the national power system as a whole, it can also be used for individual solar power plants located in different regions and countries with different climatic conditions. Certainly, the calculated coefficients differ, but the methodology itself and the sequence of its application remain the same. Full article

This study proposes a robust data-driven control framework, LSTM-MPC, designed to enhance the velocity stabilization of Autonomous Underwater Vehicles (AUVs) operating under stochastic marine disturbances. Traditional control methods often struggle with the highly nonlinear and time-varying hydrodynamics of irregular waves. To address this, we employ a Long Short-Term Memory (LSTM) recurrent neural network to capture complex temporal dependencies and provide accurate multi-step-ahead velocity predictions. These predictions are integrated into a Model Predictive Control (MPC) scheme, which optimizes control actions while respecting actuator constraints. A key contribution is the integration of an error-triggered online learning mechanism. Utilizing run-time weight synchronization via MATLAB Coder, the framework dynamically adapts to plant mismatches and high-frequency MEMS noise without an explicit analytical model. The architecture was validated using experimental data from a Pixhawk/ArduSub baseline. Results demonstrate that, under these stochastic conditions, the data-driven approach significantly outperforms the standard PID-based baseline. While adaptive PID variants offer improvements, the suggested framework drastically reduces tracking errors in rotational axes while maintaining high precision in translational velocities. This research confirms that adaptive, data-driven strategies can effectively bridge the gap between simulation and real-world deployment, offering a scalable solution for robust AUV autonomy in unpredictable environments. Full article

High-throughput plant phenotyping (HTPP) is increasingly limited by the mismatch between the need for field-relevant, fine-grained phenotypic information and the restricted capability of conventional observation platforms under complex agricultural conditions. Ground mobile robots are emerging as the key carrier for resolving this gap because they combine close-range sensing, autonomous mobility, and physical interaction within real field environments. In this paper, a structured scoping review is presented using a closed-loop perception–decision–action pipeline as the organizing principle. Within this framework, recent advances are synthesized from the perspectives of multimodal fusion, localization-aware sensing, motion planning, deep-learning-based phenotypic analysis, active observation, robotic intervention, and edge deployment. The review further clarifies the complementary roles of Unmanned Aerial Vehicles (UAVs), Unmanned Ground Vehicles (UGVs), and air–ground collaboration in multiscale phenotyping workflows. Beyond summarizing technologies, the article provides three concrete deliverables: a structured taxonomy of mobile phenotyping systems; comparative tables covering sensing modalities, localization/navigation methods, and AI models; and a research agenda linking technical progress to field deployability. The synthesis highlights four persistent bottlenecks, namely environmental generalization, annotation scarcity, limited standardization and reproducibility, and the gap between advanced models and agricultural edge hardware. Overall, ground robots are identified not merely as sensing platforms, but as the central system architecture for advancing mobile phenotyping toward autonomous, fine-grained, and field-deployable operation. Full article

Climate change is radically altering the Earth’s natural ecosystems, with temperature/precipitation alterations resulting in mismatches between specific ecosystems and their ‘new’ climatic profiles. Without political action to curb greenhouse gas emissions, most plant/animal species will need to move to higher latitudes to ensure survival. Many are incapable of migrating rapidly and will thus be reliant on human intervention to relocate to new regions (assisted migration). The first hypothetical steps of assisted migration are explored here, using the UK as a model. Urban parks/gardens have a history of hosting non-native plant species and could be used to test the validity of moving non-native plants and animals to regions of higher latitude. In this perspective paper, we added a small experimental component to examine public attitudes to species introductions into urban parks/gardens. Results showed support for using parks and gardens to protect both UK native and non-native wildlife. Indeed, >50% of respondents favoured utilising urban landscapes to conserve small non-native animals (e.g., tortoises and bee-eaters). These results imply there may be some public acceptance of assisted migration. Thus, the paper explores the potential to develop novel, but more sustainable ecosystems in new localities. Full article