Output Control Research Articles

Restricted-Enhanced Electrochemiluminescence biosensor of low excitation potential carbon quantum dots driven by magnetic Metal-Organic frameworks for sensitive immunoassay of neuron specific enolase

Electrochemiluminescence (ECL) of highly efficient and non-toxic carbon quantum dots (CQDs) plays an important role in immunoassays. However, the high absolute value of the excitation potential and poor controllability make precise detection of low levels difficult. In this work, novel CQDs with low excitation potential were reported to be used for the ultra-sensitive immunoassay of neuron-specific enolase (NSE). It is noteworthy that the narrow bandgap CQDs exhibit a trigger potential of −0.5 V and show excellent applicability of ECL signal at an excitation potential of −0.8 V. This property eliminates side reactions and electrode passivation and significantly improves ECL efficiency. Based on this, the magnetic zeolite-imidazole framework (MZIF) nanoreactor with co-reaction acceleration was employed as a carrier for the in-situ encapsulation of CQDs. The constrained-enhanced ECL sensor platform demonstrated the ability to achieve stable and controllable signal output. A sandwich-type biosensor was constructed by introducing copper sulfide (Cu7S4) as a dual-mechanism quencher, with NSE serving as the target analyte. Moreover, the electrochemical detection method based on the differential pulse voltammetry (DPV) properties of Cu7S4, was successfully validated against ECL. The sensors constructed on magnetic electrodes exhibited a bimodal signal output of ECL and DPV, encompassing a wide detection range (0.001 ng/mL − 500 ng/mL) and low detection limit (0.18 pg/mL for ECL and 0.15 pg/mL for DPV). This design offers a novel approach to understanding the efficient emission of carbon dots and provides a solution for the sensitive detection of biomarkers in clinical research.

SP2.10 - Early outcomes with the Insides Chyme Reinfusor (ICR) device in the management of parenteral nutrition dependent high output stomas (PN-HOS)

Abstract Aims PN-HOS represent a complex challenge, requiring input from surgical, gastroenterology and nutritional teams. Length of stay (LOS) is prolonged with difficulties in achieving output control and adequate nutrition with other associated risks. ICR is a modified stoma bag/mechanical pump and catheter device, reinfusing chyme into the distal limb, maintaining gut continuity, preserving enteral nutrient absorption and preventing distal limb atrophy. ICR may accelerate establishment of nutritional autonomy and reduce costs associated with PN/LOS. We report the largest study of ICR outcomes Methods Prospective study of ICR outcomes in patients with PN-HOS. Analysis included LOS, duration of PN, establishment of nutritional autonomy, nutritional markers, adverse ICR effects. Results Sixteen patients were trained to use the ICR (6M:10F). Median age 60yr. 75% achieved independent use (median 5.6 days). Overall usage was 395 days (median 32 days). Nutritional autonomy achieved in nine patients (pre-reversal). ICR increased median albumin 10.5 g/l. Adverse events included changing difficulties, spillage, noise, and discomfort. Two patients discontinued ICR because of pain/reflux. One patient died from unrelated post-operative complications. One patient with multiple loop stomas was successfully established on dual ICR. Nursing experience with ICR demonstrated ease of use and straightforward patient education. Cost analysis PN £5000/mth v ICR 1800/mth. Anecdotally at the time of reversal, surgical opinion was of much favourable bowel for anastomosis and return to function. Conclusions The ICR device is safe and contributes to restoration of nutritional autonomy. It reduces PN dependence with cost-benefits and acceptable adverse effects. At time of initial surgery distal limb should be fashioned to allow potential use of ICR.

Multi-variable fuzzy adaptive time-varying sliding mode control (MVFATVSMC) of the reverse osmosis-photovoltaic desalination system

Among the different desalination systems, reverse osmosis is popular due to its specific characteristics. Two challenges in these systems are power supply and compensation for the imposed disturbances and uncertainties which affect the performance of the system. To study these two challenges all main subsystems of the photovoltaic-reverse osmosis (PV-RO) system are considered. A photovoltaic system is considered for power supply and robust controllers are proposed to deal with the uncertainties and disturbances. For these purposes, all parts of the PV-RO desalination process are considered. Maximum power is tracked in different sun radiations and temperatures using a Mamdani-type fuzzy controller optimized by the invasive weed algorithm (IWA). This new MPPT controller gets about 10 % more power, 60 % lesser fluctuations, and 20 % smaller rise time. The speed controller of the induction motor and the pressure controller are fuzzy-PID. An adaptive time-varying sliding mode control (ATVSMC) is proposed to control the RO system because this system is highly nonlinear with many uncertainties and disturbances. The controller has global robustness and regulates the system to the desired points at a specified time. In this controller, knowing the maximum limit of the system uncertainties is not essential, which is a practical advantage for the PV-RO system. The numerical simulations in different scenarios and a comparison to existing work show the superiority of the proposed controllers. This novel controller decreases the overshoot and settling time of the bypass stream velocity by about 3.5 % and 7.6 % in comparison to super-twisting sliding mode control (STSMC), respectively. The settling time of the retentate stream velocity has decreased by about 9 %. Moreover, there are no fluctuations in the control inputs and outputs, and the amplitude of the bypass stream control input has decreased by about 98 %.

Adaptive prescribed performance control of output constrained hybrid-powered barrel elevator with dual-model uncertainty compensation

Adaptive prescribed performance control of output constrained hybrid-powered barrel elevator with dual-model uncertainty compensation

Development of an individualized stable and force-reducing lower-limb exoskeleton

In this study, an individualized and stable passive-control lower-limb exoskeleton robot was developed. Users’ joint angles and the center of pressure (CoP) of one of their soles were input into a convolutional neural network (CNN)–long short-term memory (LSTM) model to evaluate and adjust the exoskeleton control scheme. The CNN–LSTM model predicted the fitness of the control scheme and output the results to the exoskeleton robot, which modified its control parameters accordingly to enhance walking stability. The sole’s CoP had similar trends during normal walking and passive walking with the developed exoskeleton; the y-coordinates of the CoPs with and without the exoskeleton had a correlation of 91%. Moreover, electromyography signals from the rectus femoris muscle revealed that it exerted 40% less force when walking with a stable stride length in the developed system than when walking with an unstable stride length. Therefore, the developed lower-limb exoskeleton can be used to assist users in achieving balanced and stable walking with reduced force application. In the future, this exoskeleton can be used by patients with stroke and lower-limb weakness to achieve stable walking.

Research on Reactive Power Optimization of Synchronous Condensers in HVDC Transmission Based on Reactive Power Conversion Factor

With the rapid development of high-voltage direct current (HVDC) transmission systems, the coupling between AC and DC grids is becoming increasingly close. Voltage disturbances in the grid can easily cause commutation failures in the DC system, threatening its safe and stable operation. The new generation of synchronous condensers (SCs) and modified synchronous condenser units are powerful reactive power support devices widely used in large-capacity DC transmission systems. To maximize the voltage support and commutation failure suppression of SCs, this paper proposes improvements in the initial operating state of SCs, using the Shanxi–Wuhan HVDC receiving end in the Hubei power grid as an example, to better support the HVDC commutation process. Additionally, a reactive power output optimization strategy for SCs is proposed, considering the reactive power equivalent factor of electrical connections between grid nodes. This strategy determines the optimal reactive power output limit of SCs near the converter station to suppress DC commutation failures. Simulation results show that this strategy effectively utilizes the dynamic support capabilities of SCs, prevents DC commutation failures, improves HVDC transmission capacity, and enhances the safety and stability of the receiving end power grid, providing theoretical guidance for reactive power output control.

Control approach to well-posedness and asymptotic behavior of a queueing system

In this paper, we investigate well-posedness and asymptotic behavior of an M/G/1 retrial queueing system characterized by reserved idle time and setup time. This system is intricately described by an infinite system of integro-partial differential equations. Our methodology is rooted in the feedback theory of well-posed and regular infinite-dimensional linear systems. Firstly, we convert the system into a control framework, incorporating both input and output variables. Subsequently, we establish its well-posedness by leveraging the feedback theory specific to regular infinite-dimensional linear systems. Secondly, by scrutinizing the spectral distribution of the system operator along the imaginary axis, we demonstrate that the time-evolving solution of the system converges robustly to its steady-state solution. Lastly, we also discuss the essential properties of the semigroup induced by the system operator, encompassing stability and hyperbolicity, thereby shedding light on its regularity.

Single 5-nm quantum dot detection via microtoroid optical resonator photothermal microscopy

Label-free detection techniques for single particles and molecules play an important role in basic science, disease diagnostics, and nanomaterial investigations. While fluorescence-based methods are tools for single molecule detection and imaging, they are limited by available molecular probes and photoblinking and photobleaching. Photothermal microscopy has emerged as a label-free imaging technique capable of detecting individual nanoabsorbers with high sensitivity. Whispering gallery mode (WGM) microresonators can confine light in a small volume for enhanced light-matter interaction and thus are a promising ultra-sensitive photothermal microscopy platform. Previously, microtoroid optical resonators were combined with photothermal microscopy to detect 250 nm long gold nanorods and 100 nm long polymers. Here, we combine microtoroids with photothermal microscopy to spatially detect single 5 nm diameter quantum dots (QDs) with a signal-to-noise ratio exceeding 104. Photothermal images were generated by point-by-point scanning of the pump laser. Single particle detection was confirmed for 18 nm QDs by high sensitivity fluorescence imaging and for 5 nm QDs via comparison with theory. Our system demonstrates the capability to detect a minimum heat dissipation of 0.75 pW. To achieve this, we integrated our microtoroid based photothermal microscopy setup with a low amplitude modulated pump laser and utilized the proportional-integral-derivative controller output as the photothermal signal source to reduce noise and enhance signal stability. The heat dissipation of these QDs is below that from single dye molecules. We anticipate that our work will have application in a wide variety of fields, including the biological sciences, nanotechnology, materials science, chemistry, and medicine.

A Novel Linear-Based Closed-Loop Control and Analysis of Solid-State Transformer

In this paper, a new linear-based closed-loop control method for a Solid-State Transformer (SST) has been proposed. In this new control method, individual current and voltage loops for each of the power conversion stages (AC-DC, DC-DC, DC-AC) are implemented. The feedback between the input and output control signals for each loop is achieved through the voltage on the DC link capacitors and the current transferred between the converters. This enables the SST to be controlled easily in a linear-based closed-loop manner without the need for complex computations. In order to evaluate the performance analysis of the proposed control system, a simulation of an SST with approximately 10 kVA apparent power was performed. Based on the obtained simulation results, the response time of the proposed control method for dynamic load variations was proved to be in the range of 40 milliseconds, and it has been observed that this method allows electrical power to be transferred from the load to the grid. The power factor value of SST under inductive load is measured to be approximately 99%, and the overall system efficiency is 96% and above, indicating that this proposed new control method has very high performance.

Distributed predefined-time economic dispatch based on event-triggered strategy for microgrids under directed graphs

The proliferation of new energy solutions and the enhanced accessibility of distributed power sources have significantly increased the complexity and variability of microgrid structures. Due to inherent limitations in its operational mode, the traditional centralized optimization method falls short in effectively addressing the economic dispatch problem in contemporary microgrids. In this paper, a distributed optimization method is devised to address the economic dispatch problem of microgrids under directed graphs by leveraging the consensus algorithm of multi-agent systems. We define the incremental cost of each microgrid unit as the consensus variable, achieving convergence through a pioneering combination of event-triggered strategy and predefined-time control theory. This approach not only optimizes resource use but also ensures timely and efficient solution convergence, addressing critical gaps in existing methodologies. This approach enables control over the upper bound of convergence time for optimal solutions by directly adjusting time parameters in the controller under the directed graph. Consequently, it achieves coordinated control of the power output in microgrids within a predefined time while reducing the communication resources between nodes with event-triggered mechanism. The effectiveness of the proposed algorithm is verified through simulation and analysis.

Exponential control-based fixed/preassigned-time synchronization of output-coupled spatiotemporal networks with directed topology

This paper mainly explores the fixed-time and preassigned-time output synchronization of spatiotemporal networks with directed topology. Firstly, a kind of exponential-type control scheme associated with output feedback is developed. Subsequently, several sufficient criteria are established to ensure fixed-time output synchronization by utilizing the Lyapunov method and Taylor expansion. Moreover, the upper bound of convergence time is evaluated based on fixed-time stability theory. Furthermore, the preassigned-time output synchronization criteria are proposed by improving the fixed-time output controller, which incorporates a specified time parameter. Unlike previous forms, there are no stringent requirements imposed on the output matrix, whether semi-positive, non-singular or full rank in terms of columns. Finally, corresponding simulations are given to confirm the theoretical analysis.

Proposing a novel nonlinear integrated control technique for an electric power steering system to improve automotive dynamic stability

This article proposes a new control solution for an electric power steering (EPS) system to ensure the stability of car's dynamic behaviours. This work provides two new contributions which differ from previously existing publications. Firstly, a novel control method for the steering system is designed in this article based on a combination of proportional-integral-derivative (PID) and backstepping control techniques. The input to the backstepping algorithm is the output of the PID controller, whose parameters are tuned by a complex fuzzy algorithm with two inputs. Secondly, values of road reaction torque and other dynamic effects are calculated using a complex automotive dynamics model based on a nonlinear motion model and a spatial oscillation model. The stability of the control system is evaluated through the Lyapunov control function and the error between the output signals, while the dynamic stability is evaluated through the changes in car's dynamic behaviours. According to the simulation results, output values always closely follow ideal values with negligible errors if and only when the steering system is controlled by the proposed algorithm. In some conditions, the steering motor angle error achieved by the proposed controller does not exceed 0.022 rad, much lower than the fault scenario. In addition, the vehicle's roll angle and motion trajectory always follow the desired value with minimal errors. In conclusion, if the EPS system is controlled by the new control technique shown in this article, car dynamic stability will be guaranteed under all investigated conditions.

A Long Short-Term Memory Neural Network Algorithm for Data-Driven Spatial Load Forecasting

Based on the LSTM neural network, the author proposes a new data-driven spatial load prediction method to analyze the time series within the neural network, avoid data depression, and determine the correlation of the training data space. Establish prediction models based on different neurons, reduce the dimensionality of collected data through data preprocessing, and ensure data integrity. At the same time, provide data management base, control model input and output, unify process model, ensure training sequence model, combine LSTM neural network model, select prediction method, and finish data-driven related transportation. Experimental results show that the data driven global load forecasting model based on LSTM neural network proposed in this paper takes 1.23 seconds to complete 8000 training data, when traditional data drive spatial load forecasting method based on CNN neural network takes 3.56 seconds to finish 8000 training data. It can be seen that the prediction method proposed in this article has a good prediction accuracy.

Input–Output Scheduling and Control for Efficient FPGA Realization of Digit-Serial Multiplication Over Generic Binary Extension Fields

Input–Output Scheduling and Control for Efficient FPGA Realization of Digit-Serial Multiplication Over Generic Binary Extension Fields

Coordination control strategy of motion stability and tire slip for electric vehicle driven by in-wheel-motors

The Direct Yaw Moment Control (DYC) is a widely utilized technique for electric vehicles equipped with In-Wheel Motors (IWMs) to ensure yaw stability. However, most existing DYC studies calculate the additional control yaw moment solely based on the torque differences among the four IWMs, disregarding the nonlinear tire characteristics and wheel rotational movements. When road conditions are poor or torque changes are intense, the tire may slip, preventing it from generating the required longitudinal tire force for the desired direct yaw moment. To address this challenge, this study introduces a coordination control strategy for motion stability and tire slip in electric vehicles (EV) driven by IWMs. To enhance control performance, the nonlinear tire characteristics and wheel rotational movements are integrated into the modeling process when calculating tire forces. Additionally, a state and parameter coupling estimation algorithm is utilized to obtain feedback on tire parameters and vehicle motion states. Using these estimation results as a foundation, a coordination strategy is developed to achieve reference motion state tracking and tire slip ratio control. The Model Predictive Control (MPC) algorithm is employed to solve the control problem with constraints on both control inputs and outputs, and the estimation results are utilized to update the control model as well as provide states feedback. Simulations and experiments are conducted to validate the feasibility and effectiveness of the proposed control strategy. The results demonstrate that our proposed control strategy effectively improves vehicle stability by coordinating vehicle motion and tire slip control.

Investigation of anti-windup Proportional-Integral controller with semi-decoupled tuning gains in motor speed control

Abstract Integral windup refers to a prevalent phenomenon that can occur in Proportional-Integral-Derivative (PID) control systems that employ integral control where the control output generated exceeds the operating limit of an actuator. Despite the system’s inability to respond further due to control output limitations, the integral controller perseveres in accumulating errors. The accumulation of error can lead to system instability and undesirable oscillations. Additionally, the dynamic response of the PID controller is also limited by the coupled tuning gain characteristics, complicating the process of gain tuning. The coupling and decoupling of the tuning gains are depending on the separation of the gains in the error dynamics of the system. In this study, a concept of semi-decoupled tuning gains in anti-windup controllers is proposed. The performance of the proposed controller was investigated through hardware simulation against conventional PI and existing anti-windup schemes and has demonstrated an overall improvement in its dynamic behaviour such as faster settling time and its ability to tune the system without significantly altering its damping ratio.

Comparative performance analysis of robust and adaptive controller for three-link robotic manipulator system

Abstract Three-link robotic manipulator systems (TLRMS) often used in automation industries offer many capabilities, but become very complex in terms of their control and operations. In order to enhance trajectory tracking in the X and Y axes, this study investigates the application of a fractional-order nonlinear proportional, integral, and derivative (FONPID) controller for a three-link robotic manipulator system (TLRMS). Using a cost function that combines the integral of square error (ISE) and the integral of absolute change in controller output (IACCO), the cuckoo search algorithm (CSA) maximises the performance of the controller. The fractional-order term enhances the robustness and the nonlinear term supports the adaptiveness of the FONPID controller. The fractional-order proportional, integral, and derivative (FOPID) and classic PID controllers are contrasted with the FONPID controller's efficacy. The findings show that the CSA-tuned FONPID performs better than the other controllers, providing more robust and accurate tracking. By demonstrating fractional-order control's promise for intricate robotic systems, this study advances the discipline.

Robust hybrid control strategy for active power management in Kabertene wind farm within Algeria’s PIAT grid

The paper introduces a hybrid control strategy for optimised active power management in Algeria's Kabertene wind farm, crucial for the pole insalah-adrar-timimoune (PIAT) grid's stability. This strategy merges simultaneous interconnection and damping assignment (SIDA) passivity theory, passivity-based control (PBC), and multivariable proportional-integral-derivative (PID) controllers. This combined approach ensures frequency and voltage stability within the PIAT grid, which encompasses various elements like wind farms, solar plants, gas turbines, and dynamic impedance (Z), current (I), and active power (P) (D-ZIP), load model. By tailoring controllers for doubly fed induction generators (DFIGs) using SIDA-PBC principles and optimising internal parameters, the strategy achieves precise control of active power output. Additionally, particle swarm optimisation (PSO) refines power scheduling, which is especially beneficial for intermittent renewable sources like DFIGs. This comprehensive strategy offers numerous advantages: improved network stability, minimized voltage deviations, reduced frequency fluctuations, and enhanced integration of renewable energy sources. The paper emphasises practical implementation considerations, providing valuable guidance for efficient Kabertene wind farm operation and integration. This research contributes significantly to fostering cleaner and more reliable energy systems, facilitating the PIAT grid's transition towards sustainable energy generation.

Interacting multiple model-based yaw stability control for distributed drive electric vehicle via adaptive model predictive control algorithm

The vehicle lateral stability control is a challenging problem due to the tire nonlinearity and the immeasurable sideslip angle. Thus, an adaptive model predictive control (AMPC) scheme based on interacting multiple model (IMM) vehicle sideslip angle observer is proposed in this paper. First, the observer is composed of the Extended Kalman filter (EKF) and the Unscented Kalman filter (UKF), which improves the real-time performance as well as the observation accuracy. Then, a multi-objective controller based on adaptive vehicle model prediction is designed using model predictive control algorithm. This controller aims to achieve a balance between the actuation and state constraints. The T-S fuzzy algorithm is used to observe the tire cornering stiffness and design an adaptive vehicle model. By utilizing the appropriate objective function and a quadratic programing solver, the controller output is obtained to achieve vehicle stability control. Finally, the effectiveness of the designed AMPC scheme under various working conditions is verified by Carsim-Simulink joint simulation platform and hardware-in-the-loop (HIL) test.

Enhancing Vapor Compression Refrigeration Systems Efficiency via Two-Phase Length and Superheat Evaporator MIMO Control

The present investigation focuses on enhancing the efficiency of a vapor compression refrigeration system (VCRS) by proposing a Multiple Input Multiple Output (MIMO) control strategy based on the evaporator’s two-phase length and superheat temperature. A moving boundary dynamic model for the VCRS is implemented using the Thermosys Matlab Toolbox. The study analyzes the influence of actuation parameters, specifically compressor speed and expansion valve opening, on control parameters, namely two-phase length and superheat temperature. A comprehensive analysis based on the first and second laws of thermodynamics is conducted across a wide range of operating conditions. Simulation results demonstrate that two-phase length can be effectively utilized as a control parameter by selecting operating points that maximize the system efficiency. Additionally, the study reveals that extending the evaporator’s two-phase length to 80–90% of its limit increases system efficiency, enabling a reduction in compressor speed while maintaining the cooling capacity.