Stable Control Research Articles

Attitude tracking control of a foldable wave energy powered AUV based on linear time-varying MPC under position constraints

In this paper, we investigate the tracking control of attitude and specific spatial position for a foldable wave energy powered autonomous underwater vehicle (FWEPAUV) under space constraints. In order to reduce energy consumption and maximizing the efficiency of the charging, a cascaded dual closed-loop control strategy is proposed. Firstly, we utilize model predictive control (MPC) with multi-variable constraint capabilities to design the kinematics controller. The output constraints are set as space constraints for MPC, and an objective function is used to strike a balance between control accuracy and energy consumption. The improved chaotic firefly algorithm (ICFA) is chosen due to its rapid convergence and strong robustness. It is employed in the rolling optimization process of MPC to secure a viable solution. Secondly, the kinematics controller is combined with the dynamics controller based on adaptive sliding mode control (ASMC) to achieve robust tracking control. In order to eliminate the interference from time-varying ocean currents and complex external forces meanwhile utilizing these interferes to reduce energy consumption, An ocean current observer based on ICFA-PI control and a prescribed time disturbance observer (PTDO) are devised to estimate them. Finally, an event-triggered (ET) mechanism is applied to integrate with the trajectory tracking controller to decrease the computational load. The stability of the ICFA-MPC controller and the ASMC controller is proven using the Lyapunov stability theory. Simulation results demonstrate that in complex external interference environments, the controller can achieve stable tracking control of the attitude and a specific spatial position, thereby meeting predefined goals such as space constraints, energy savings, and maximizing the efficiency of the charging.

RBF-based event-triggered fixed-time stable and chattering-free controller for load following of the pressurized water reactor

Preventing chattering phenomenon in load following control of the Pressurized Water Reactor (PWR) is still a main challenge. This article focuses on the chattering-free and rapid load following control strategy for PWR. Firstly, by proposing a newly adaptive updated law, the Radial Basis Function Neural Networks (RBFNNs) is employed to compensate the unmeasured states of relative delayed neutron precursor density, average fuel temperature and uncertain disturbances simultaneously online. On this basis, a fixed-time stable controller including saturation function is constructed to guarantee that the actual output power (OP) of nuclear reactor can follow the desired power (DP) within a fixed-time and the chattering phenomenon is alleviated. Additionally, to save the limited network resource and reduce the cost of computation, an event-triggered mechanism is constructed. Based on it, a chattering-free fixed-time stable controller is structured to ensure that the actual reactivity of control rod (ARCR) converges to the desired control rod reactivity (DCRR) within a fixed-time, meanwhile, it can eliminate Zeno phenomenon during the control process. Finally, the effectiveness and feasibility of the theoretical results are demonstrated through simulation examples.

Online detection method for magnetic suspension concentration based on machine vision

Abstract With the intelligent development of magnetic particle inspection, the quality of magnetic indications formed at cracks is closely related to the accuracy of magnetic particle inspection image analysis results. The concentration of magnetic suspension is a key process parameter affecting the quality of magnetic indication formation. Hence, this study presents an online detection method based on machine vision for measuring magnetic suspension concentration. The method initially enhances the contrast of images of the pear-shaped measuring tube containing magnetic suspension and then extracts scale lines through feature analysis and morphological processing. A method for extracting the magnetic particle sedimentation area of magnetic suspension based on a dual-threshold segmentation algorithm is proposed. The contour filtering algorithm and pixel calibration method are used to obtain the magnetic particle concentration of the non-estimation and estimation areas based on scale line extraction, ultimately forming an online accurate detection method for magnetic suspension concentration values. Experiments were conducted to validate the method against different concentrations, turbidity levels, tilting angles of the pear-shaped measuring tube, and ambient brightness. The results show that the error in magnetic suspension concentration detection based on this method is within ±3%. This has certain reference value for the stable control of magnetic suspension concentration and for enhancing the reliability of intelligent decision-making results in magnetic particle inspection.

Quantitative CT Imaging Features Associated with Stable PRISm using Machine Learning

Rationale and ObjectivesThe structural lung features that characterize individuals with preserved ratio impaired spirometry (PRISm) that remain stable overtime are unknown. The objective of this study was to use machine learning models with computed tomography (CT) imaging to classify stable PRISm from stable controls and stable COPD and identify discriminative features. Materials and MethodsA total of 596 participants that did not transition between control, PRISm and COPD groups at baseline and 3-year follow-up were evaluated: n=274 with normal lung function (stable control), n=22 stable PRISm, and n=300 stable COPD. Investigated features included: quantitative CT (QCT) features (n=34), such as total lung volume (%TLCCT) and percentage of ground glass and reticulation (%GG+Reticulationtexture), as well as Radiomic (n=102) features, including varied intensity zone distribution grainy texture (GLDZMZDV). Logistic regression machine learning models were trained using various feature combinations (Base, Base+QCT, Base+Radiomic, Base+QCT+Radiomic). Model performances were evaluated using area under receiver operator curve (AUC) and comparisons between models were made using DeLong test; feature importance was ranked using Shapley Additive Explanations values. ResultsMachine learning models for all feature combinations achieved AUCs between 0.63-0.84 for stable PRISm vs. stable control, and 0.65-0.92 for stable PRISm vs. stable COPD classification. Models incorporating imaging features outperformed those trained solely on base features (p>0.05). Compared to stable control and COPD, those with stable PRISm exhibited decreased %TLCCT and increased %GG+Reticulationtexture and GLDZMZDV. ConclusionThese findings suggest that reduced lung volumes, and elevated high-density and ground glass/reticulation patterns on CT imaging are associated with stable PRISm.

Provably Stable Learning Control of Linear Dynamics With Multiplicative Noise

Provably Stable Learning Control of Linear Dynamics With Multiplicative Noise

Patients with Moderate-to-Severe Atopic Dermatitis Maintain Stable Response with No or Minimal Fluctuations with 1 Year of Lebrikizumab Treatment.

Lebrikizumab is a novel monoclonal antibody with established efficacy in patients with moderate-to-severe atopic dermatitis (AD) in multiple Phase 3 trials. One of the ultimate treatment goals for patients with moderate-to-severe AD is to achieve stable disease control without concern for planning future life events. In ADvocate1 and ADvocate2, lebrikizumab-treated patients meeting the protocol-defined response criteria at Week 16 were re-randomized 2:2:1 to receive lebrikizumab every 2weeks (Q2W), lebrikizumab every 4weeks (Q4W), or placebo Q2W (lebrikizumab withdrawal) for 36 additional weeks. In this post hoc analysis, we evaluated the proportions of patients with no or minimal fluctuations of efficacy during the 36-week maintenance period and plotted individual patient trajectories. We defined no or minimal fluctuations as achieving and maintaining the defined endpoint (≥ 75% improvement in the Eczema Area and Severity Index [EASI 75], ≥ 90% improvement in EASI, Pruritus Numeric Rating Scale [NRS] ≥ 4-point improvement, or Pruritus NRS ≥ 3-point improvement) for ≥ 80% of the study visits. If patients used rescue medication, discontinued treatment, or transferred to the escape arm, data collected at or after the event were imputed as non-response. The proportions of lebrikizumab responders who maintained EASI 75 with no or minimal fluctuations were 70.8% (lebrikizumab Q2W), 71.2% (lebrikizumab Q4W), and 60.0% (lebrikizumab withdrawal). Of the patients with baseline Pruritus NRS ≥ 4 and who achieved ≥ 4-point improvement at Week 16, 66.1% (lebrikizumab Q2W), 62.7% (lebrikizumab Q4W), and 55.2% (lebrikizumab withdrawal) maintained ≥ 4-point Pruritus NRS improvement with no or minimal fluctuations. Patients who met the response criteria at Week 16 and continued treatment with lebrikizumab Q2W or Q4W demonstrated a stable response with no or minimal fluctuations of efficacy in measures of skin and itch up to Week 52. NCT04146363 (ADvocate1) and NCT04178967 (ADvocate2).

Apparatus for the room temperature measurement of low field Nernst and magneto-Seebeck coefficients.

Nernst coefficient measurements are a classic approach to investigate charge carrier scattering in both metals and semiconductors. However, such measurements are not commonly performed, despite the potential to inform material design strategies in applications such as thermoelectricity. As dedicated instruments are extremely scarce, we present here a room temperature apparatus to measure the low field Nernst coefficient (and magneto-Seebeck coefficient) in bulk polycrystalline samples. This apparatus is specifically designed to promote accurate and facile use, with the expectation that such an instrument will make Nernst measurements de rigueur. In this apparatus, sample loading and electrical contacts are all pressure-based and alignment is automatic. Extremely stable thermal control (10 mK of fluctuation when ΔT = 1K) is achieved from actively cooled thermoelectric modules that operate as heaters or Peltier coolers. Magneto-Seebeck measurements are integrated into the system to correct for residual probe offsets. Data from the apparatus are provided on bulk polycrystalline samples of bismuth, InSb, and SnTe, including raw data to illustrate the process of calculating the Nernst coefficient. Finally, we review how Nernst measurements, in concert with Seebeck, Hall, and electrical resistivity, can be analyzed via the Boltzmann equation in the relaxation time approximation to self-consistently predict the Fermi level, effective mass, and energy-dependent relaxation time.

An Output Feedback Approach for 3-D Prescribed Time Stabilization Control of Unmanned Underwater Vehicles

An Output Feedback Approach for 3-D Prescribed Time Stabilization Control of Unmanned Underwater Vehicles

Research and practice of virtualization PLC (vPLC) technology in 5G industrial network

Abstract Under the influence of information technology innovation, the empowerment of 5G and industrial internet in the steel industry has gradually expanded from production assistance to industrial control. In this paper, a deep integration network of 5G and industrial control is constructed, and a virtualization PLC(vPLC) system solution deployed on private cloud servers is proposed. Secondly, IoT protocols and security policies for cloud control are designed. Finally, the proposed scheme was successfully applied to the remote control of equipment in the steelmaking production process of a certain steel enterprise. At the same time, the real-time performance, reliability and stability of the 5G-enabled vPLC system were tested. The result indicated that the 5G industrial network can work stably with a delay of less than 10 ms and 99.99% reliability. The vPLC system ran continuously for 2000 hours and maintained stable control functions. This result can provide a research foundation and technical direction for achieving centralized control and one-stop production management in the future.

Dynamic control analysis of energy-efficient side-stream extractive distillation process for separating close-boiling mixture

To separate the close-boiling mixture comprising heptane and toluene, this study introduces an environmentally-friendly method of side-stream extractive distillation by utilizing aniline as the separating agent. This approach modifies traditional extractive distillation by adding a side rectifier, converting two-way vapor and liquid communication into one-way liquid transfer using an additional distillation section along with corresponding reboiler. Our present study investigates the dynamic control behavior for a strongly non-ideal system without and with heat integration. It focuses on achieving stable and reliable regulatory control by closely keeping the dual-product composition to their original design standards. Key control loops are pinpointed, with a specific emphasis on those centered around adjusting reboiler heat duty and side-stream flow rate within extractive column C1 to regulate the temperatures of Stages 48 and 36. Furthermore, control effectiveness is enhanced by introducing a feedforward action of reboiler heat duty-to-feed flowrate. By developing a new and robust control architecture, high feed flowrate and composition disturbances can be managed efficiently, solving the issues of oscillation and large transient response.

Soft sensing of NOx emission from waste incineration process based on data de-noising and bidirectional long short-term memory neural networks.

Continuous emission monitoring system is commonly employed to monitor NOx emissions in municipal solid waste incineration (MSWI) processes. However, it still encounters the challenges of regular maintenance and measurement lag. These issues significantly impact the accurate and stable control of NOx emissions. Therefore, developing a soft NOx emission sensor to complement hardware monitoring becomes imperative. Considering data noise, dynamic nonlinearity, time series characteristics and volatility in the MSWI process, this article introduces a soft sensor model for NOx emission prediction utilizing the complete ensemble empirical mode decomposition adaptive noise (CEEMDAN)-wavelet threshold (WT) method and bidirectional long short-term memory (Bi-LSTM). Firstly, the original data signal is decomposed into a group of intrinsic mode functions (IMFs) using the CEEMDAN. Subsequently, the WT processes the high-frequency IMFs that are noise-dominant. Then, all IMFs are reconstructed to obtain the denoized signal. Finally, the Bi-LSTM model is employed to predict NOx emissions. Compared to conventional modelling approaches, the model proposed in this article demonstrates the best predictive performance. The mean absolute percentage error, root-mean-squared error and average absolute error on the test set of the proposed model are 3.75%, 5.34 mg m-3 and 4.34 mg m-3, respectively. The proposed model provides a new method to soft sensing NOx emissions. It holds significant practical value for precise and stable monitoring of NOx emissions in MSWI processes and provides a reference for research on modelling key process parameters.

Addressing Launch and Deployment Uncertainties in UAVs with ESO-Based Attitude Control

This paper describes the design and implementation of a novel three-axis attitude control autopilot scheme for tube-launched, air-deployed UAVs. In early flight tests, various factors, such as model uncertainties during launch, aerodynamic uncertainties, geometric parameter changes during deployment, and significant uncertainties in booster rocket installation, exceeded the control capabilities of the attitude autopilot, causing flight instability. In order to address these issues, a numerical simulation model of the full launch process considering deviations was established based on early flight tests. A cascade attitude controller was then designed using an extended state observer (ESO), and the boundedness of control errors under unknown bounded disturbances was theoretically proven, providing requirements for the parameter tuning of the cascade controller. Comparative experiments and a second flight test both demonstrate that the ESO-based cascade attitude controller exhibits strong feedforward disturbance compensation under high-uncertainty conditions, effectively achieving stable control within the flight envelope.

IVC collapsibility indices in assessment of volume overload in neonatal cardiac patients

BackgroundFluid overload is associated with increased morbidity and mortality in critically ill patients. An accurate assessment of fluid status in neonatal cardiac pediatric patients is required for assessment of fluid overload. Estimation of fluid status using different Doppler parameters may be helpful in determination of fluid status.PurposeTo evaluate difference in fluid status in neonates with and without congenital heart diseases. To detect sensitivity of Doppler Ultrasound in early determination of fluid status. To compare between different parameters (IVC diameter, IVCCI, and IVC/AO ratio) between cardiac and healthy neonates.MethodsTransabdominal Doppler parameters of 25 full-term newborns with congenital heart diseases and congestive heart failure were compared to 25 post-natal age and sex-matched normal healthy neonates serving as controls. Aortic as well as inspiratory and expiratory inferior vena cava diameters were measured. Subsequently, inferior vena cava collapsibility index (IVCCI) and inferior vena cava-to-aortic ratio (IVC/AO) were calculated.ResultsFluid overload detected by increased IVC diameter (decreased IVCCI and increased IVC/AO ratio) was observed in the cardiac compared to the control group. There was positive correlation between the fluid volume and the IVC diameter and IVC/AO ratio and negative correlation with the IVC collapsibility indices.ConclusionsFluid overload with increased IVC diameter and lack of IVC collapsibility were observed in neonates with congenital heart diseases and congestive heart failure compared with those hemodynamically stable controls. We recommend the use of IVC diameter, IVCCI, and IVC/AO ratio as rapid, easy and sensitive parameters in assessing volume status.

Data-Driven Batch Process Monitoring for Continuous Annealing of Cold-Rolled Strip Steel

The continuous annealing process (CAP) is a crucial process of steel production, which has a significant impact on the uniformity and stability of mechanical properties. A novel batch monitoring process based on kernel dissimilarity (KDISSIM) and Kmeans++ is proposed in this paper, focusing on problems such as unequal sample lengths between batches and nonlinearity between variables. First, KDISSIM is used to describe the dissimilarity between batches. Secondly, Kmeans++ is employed to improve the accuracy of clustering tasks based on historical batches. The largest cluster is considered to be at a relatively stable control level, and these batches are further used as training data. Then, the center batch and boundary batch of the training set are used as the reference batch and monitoring threshold for the monitoring model, respectively. Finally, the effectiveness of the proposed method is verified via the actual CAP data, providing a feasible solution for CAP batch monitoring.

Compact lab-on-printed circuit board (PCB) for free-surfactant silver nanomaterial synthesis

This study introduces a compact Lab-on-Printed Circuit Board (PCB) device featuring thermal integrated microfluidic channels designed for nanoparticle synthesis. Commonly, complex heating chambers are employed to expedite reactions and enhance nanomaterial productivity, yet they pose challenges in temperature control and mixing rates within the reaction chamber. To address these issues, we propose a compact device composed of PDMS microfluidic channels and a PCB platform. The PCB heater, constructed to precisely regulate temperature within the microfluidic channels, utilizes copper lines as heating resistors, while an Arduino kit is employed for temperature measurement and control. Leveraging a Proportional–integral–derivative (PID) controller through programming, the device achieves rapid temperature increase and stable control within the microfluidic channels. Experimental validation demonstrates the operational principles and capabilities of this device, showcasing observable changes in nanoparticle size and morphology. Discussions on these observed results are included in this work.

Adaptive control for circulating cooling water system using deep reinforcement learning.

Due to the complex internal working process of circulating cooling water systems, most traditional control methods struggle to achieve stable and precise control. Therefore, this paper presents a novel adaptive control structure for the Twin Delayed Deep Deterministic Policy Gradient algorithm, which is based on a reference trajectory model (TD3-RTM). The structure is based on the Markov decision process of the recirculating cooling water system. Initially, the TD3 algorithm is employed to construct a deep reinforcement learning agent. Subsequently, a state space is selected, and a dense reward function is designed, considering the multivariable characteristics of the recirculating cooling water system. The agent updates its network based on different reward values obtained through interactions with the system, thereby gradually aligning the action values with the optimal policy. The TD3-RTM method introduces a reference trajectory model to accelerate the convergence speed of the agent and reduce oscillations and instability in the control system. Subsequently, simulation experiments were conducted in MATLAB/Simulink. The results show that compared to PID, fuzzy PID, DDPG and TD3, the TD3-RTM method improved the transient time in the flow loop by 6.09s, 5.29s, 0.57s, and 0.77s, respectively, and the Integral of Absolute Error(IAE) indexes decreased by 710.54, 335.1, 135.97, and 89.96, respectively, and the transient time in the temperature loop improved by 25.84s, 13.65s, 15.05s, and 0.81s, and the IAE metrics were reduced by 143.9, 59.13, 31.79, and 1.77, respectively. In addition, the overshooting of the TD3-RTM method in the flow loop was reduced by 17.64, 7.79, and 1.29 per cent, respectively, in comparison with the PID, the fuzzy PID, and the TD3.

Research on coordinated control strategy for braking energy recovery of pure electric vehicles based on ESC

AbstractTaking a rear‐wheel drive pure electric vehicle as the research object, considering the safety during braking and improving energy recovery rate, a study was conducted on the distribution strategy of front and rear axle braking force. During the braking process, the feedback braking force of the motor and the hydraulic braking force with an electronic stability controller (ESC) were coordinated and controlled, to ensure that the total required braking force was met. A fuzzy logic controller has been designed, with three variables of battery state of charge, vehicle speed, and braking intensity as inputs, and a modified motor braking ratio as output variable to prevent wheel lockup. Using Cruise software, a co‐simulation model was established with Amesim and Simulink, and simulation validation was conducted on the braking process and cycling conditions. The simulation results showed that the brake recovery strategy based on fuzzy control can effectively improve the vehicle's control performance and energy recovery rate compared to the Economic Commission of Europe regulation. The NEDC (New European Driving Cycle) working condition improved by 10.41% and the CLTC‐P (China Light‐duty Vehicle Test Cycle‐passenger) working condition improved by 10.57%. Effectively improving power consumption per 100 km, NEDC decreased by 1.81% and CLTC‐P decreased by 2.62%.

Design Approach for an Advanced Multi-Channel Pyrometer for Bulk Oven Processes

Industrial processes such as smelting and sintering require stable and precise temperature control of furnaces. To achieve this, accurate temperature measurements are required. Pyrometry allows for contact- less measurement of bulk materials and is particularly suitable for high temperature applications. One of the main influences on the accuracy of pyrometric measurements is the knowledge of the emissivity in the spectral measurement range. To reduce this dependence, two-color pyrometers or multi-color pyrometers can be used. With this in mind, the IRS is further developing their existing pyrometer technology by designing an advanced multi-channel pyrometer for bulk oven processes in a joint venture with Stange Elektronik GmbH and New Generation Kilns Grün GmbH. The design approach is explained here and the considered methods of achieving emissivity independent temperature measurements are examined.

The Method of Synthesis of a Stable Closed-Loop Object Control System with Limiters

The Method of Synthesis of a Stable Closed-Loop Object Control System with Limiters

A fuzzy coefficient deep flux weakening algorithm for IPMSM without out-of-control

A fuzzy coefficient q-axis current increment flux weakening (FCCIFW) control method is proposed in interior permanent magnet synchronous motor (IPMSM) drives. The proposed FCCIFW not only simplifies the calculation of flux weakening coefficient according to the derived formula, but also avoids the saturation of current regulator. In FCCIFW, the theoretical calculation formula of conversion coefficient is derived firstly, and then the fuzzy rule base is established according to the formula. FCCIFW not only refrains the use of complex flux weakening calculation formula or fixed control parameters, but also refrains the problem of out-of-control in the process of deep flux weakening control, so as to improve the overall performance in the process of flux weakening. The results show that the proposed method has an effective and correct calculation process and results, Compared with different control methods in the operation process, it shows that the proposed method has more stable control effect, which has great application value in engineering.