Traditional Control Method Research Articles

Insecticidal potential of botanical extracts for management of, the oriental fruit fly, Bactrocera dorsalis (Diptera: Tephritidae)

BackgroundBactrocera dorsalis (Hendel) poses a significant threat to global fruit production and quality, including in Pakistan, where it causes substantial losses. Traditional control methods have heavily relied on the indiscriminate and imprudent use of insecticides, leading to challenges such as insecticide resistance, environmental pollution, fruit contamination, and health risks. This study explores the insecticidal potential of seven selected botanicals: Artemisia vulgaris L., Capparis spinosa L., Mentha longifolia, Mentha piperita, Peganum harmala, Eucalyptus globulus, and Callistemon viminalis, each at three different concentrations (10mL, 20mL, and 30mL), against Bactrocera dorsalis (B. dorsalis). Two in vitro experiments involving the application of plant extracts mixed with protein bait and direct spray were conducted.ResultsBoth experimental results demonstrated a direct correlation between the concentration of plant extract and the mortality of B. dorsalis. In the plant extract mixed with protein bait, results recorded after 96 h at 10 mL ranged from 8.667 ± 1.52–47.000 ± 3.00, at 20 mL from 18.667 ± 1.52–53.667 ± 6.02, and 30 mL from 26.000 ± 5.29- 67.667 ± 2.08. Meanwhile, for direct spray, mortality ranged from 2.333 ± 1.15–32.333 ± 6.80 at 10 mL, 1.000 ± 1.00–42.667 ± 7.02 at 20 mL, and 9.667 ± 2.30–61.333 ± 8.50 at 30 mL, respectively. The plant species exhibiting the highest toxic potential against the respective pests was C. viminalis, followed by A. vulgaris L. In contrast, the least toxic effect was observed in Eucalyptus globulus at all concentrations. The highest mean mortality and repellence % were recorded in C. viminalis and E. globulus in both experimental trials. The results revealed the highest efficacy of C. viminalis with an LC50 value of 9685.15 and 19,909.11 ppm, for the extract mixed with protein bait and sprayed, was obtained, respectively, the most toxic to the pest followed by the extracts E. globulus and C. spinosa L.ConclusionBased on our experimental results, it is concluded that C. viminalis and A. vulgaris L. are an effective alternative for the management of B. dorsalis.

Using microbes to suppress overwintering inoculum of Monilinia laxa on mummified fruits and to protect cherry blossoms and fruit from infection

Cherry brown rot, primarily caused by Monilinia laxa, poses a significant threat to cherry production in the UK. Traditional control methods are labour-intensive or environmentally damaging, favouring alternative solutions such as using biological control agents (BCAs). This study investigated the survival and efficacy of two BCAs, Bacillus subtilis (B91) and Aureobasidium pullulans (Y126), in reducing M. laxa populations on mummified fruits and cherry blossoms. Field experiments were conducted to assess the survival of B91 and Y126 on mummified fruits over winter and their effect on M. laxa sporulation in spring. Additionally, the efficacy of the two BCAs when applied to cherry blossoms was evaluated, at different times of application relative to M. laxa inoculation. Y126 could persist on blossoms and throughout the growing season. B91 population level declined rapidly on blossoms but recovered once fruits appeared. Neither BCA usefully reduced the concentration of M. laxa. Y126 was marginally more effective at long-term reduction. On mummified fruits, Y126 population size declined rapidly within the first two weeks after inoculation before stabilising. B91 survived slightly longer. Longer interaction times between M. laxa and the BCAs in the late winter resulted in lower spore counts of M. laxa from mummified fruits. While both BCAs reduced M. laxa populations when applied close to M. laxa inoculation, BCA populations were not maintained at effective levels throughout the growing season. These results suggest that multiple applications targeting critical periods (blossom and pre-harvest) are necessary for effective control of M. laxa by either of these BCAs.

Application of Fuzzy Adaptive Impedance Control Based on Backstepping Method for PAM Elbow Exoskeleton in Rehabilitation

In this study, a fuzzy adaptive impedance control method integrating the backstepping control for the PAM elbow exoskeleton was developed to facilitate robot-assisted rehabilitation tasks. The proposed method uses fuzzy logic to adjust impedance parameters, thereby optimizing user adaptability and reducing interactive torque, which are major limitations of traditional impedance control methods. Furthermore, a repetitive learning algorithm and an adaptive control strategy were incorporated to improve the performance of position accuracy, addressing the time-varying uncertainties and nonlinear disturbances inherent in the exoskeleton. The stability of the proposed controller was tested, and then corresponding simulations and an elbow flexion and extension rehabilitation experiment were performed. The results showed that, with the proposed method, the root mean square of the tracking error was 0.032 rad (i.e., 21.95% less than that of the PID method), and the steady-state interactive torque was 1.917 N·m (i.e., 46.49% less than that of the traditional impedance control). These values exceeded those of the existing methods and supported the potential application of the proposed method for other soft actuators and robots.

Adaptive Neural Network-Based PID Controller Design for Velocity Control of an Internal Combustion Engine Using Back Propagation Technique

Precise velocity control in internal combustion engines (ICEs) is essential for optimizing performance, improving fuel efficiency, and minimizing emissions. However, the nonlinear dynamics and inherent uncertainties within ICE systems present substantial challenges for traditional control methods. In this paper, we propose an adaptive control strategy that integrates a Proportional-Integral-Derivative (PID) controller with Back Propagation Neural Networks (BPNNs) to effectively address these complexities. The proposed controller architecture consists of two key components: a BPNN to estimate modelling uncertainties, such as unknown friction and external disturbances affecting the ICE, and a primary PID controller responsible for velocity regulation. The BPNN functions as a dynamic estimator, continuously learning and adapting to changes in system dynamics, thereby enhancing the robustness and adaptability of the control system. By accurately capturing the nonlinearities and uncertainties inherent in ICEs, the BPNN contributes to improved control performance and system stability. To validate the effectiveness of the proposed approach, extensive numerical simulations are performed using MATLAB Simulink. These simulations encompass a range of operating conditions and scenarios to thoroughly evaluate the controller's performance. Additionally, the proposed method is compared to conventional PID control techniques found in the literature, with a focus on robustness, tracking accuracy, and disturbance rejection. The results indicate that the adaptive PID controller, incorporating BPNNs, outperforms traditional PID methods, delivering superior velocity regulation and disturbance rejection. Furthermore, the proposed approach demonstrates significant potential for real-world applications in ICE systems, providing enhanced control performance and efficiency. This study advances the field of control engineering by introducing an innovative adaptive control strategy tailored specifically for velocity control in internal combustion engines. Leveraging the capabilities of BPNNs to address uncertainties, this approach contributes to improved system performance and offers a promising direction for future advancements in engine control technologies.

Vehicle active suspension control considering parameter uncertainty and velocity variation

Due to the uncertainty in vehicle model parameters, the control performance of active suspensions often deteriorates, especially under extreme conditions such as high velocities and full loads. To address this challenge, this paper proposes a novel robust finite frequency H ∞ output feedback control strategy based on linear parameter varying (LPV) systems. First, we establish a new LPV model for the half-car active suspension control system, taking into account the uncertainties associated with vehicle velocity and sprung mass. Next, a robust finite frequency H ∞ output feedback controller is designed using a gain scheduling approach, formulated through a set of linear matrix inequalities (LMIs). Finally, simulation results demonstrate that the proposed velocity-dependent robust control strategy significantly reduces the root mean square (RMS) values of vertical acceleration by approximately 21% to 31% under various conditions of sprung mass and vehicle speed compared to traditional control methods.

Performance Enhancement of Microgrid Systems Using Backstepping Control for Grid Side Converter and MPPT Optimization

Microgrid networks represent a crucial model in the field of power transmission and distribution, integrating renewable energy sources with modern control technologies. This research focuses on enhancing the performance of a microgrid system connected to the main grid through a three-phase converter controlled using Voltage Oriented Control (VOC). The microgrid comprises a storage element and a photovoltaic (PV) generation system. To improve system efficiency and power quality, backstepping (BS) controllers are implemented and compared with traditional control methods. Three control systems are evaluated: a conventional system using Perturb and Observe (P&O) algorithm for Maximum Power Point Tracking (MPPT) and Proportional-Integral (PI) controllers for grid-side converter (GSC) control, a hybrid system with BS-modified P&O (BS-P&O) for MPPT and PI controllers for GSC, and an advanced system employing BS-P&O for MPPT and BS controllers for GSC. The research demonstrates that BS controllers exhibit superior dynamic performance, contributing to improved regulation of DC-link voltage, active and reactive powers. Additionally, they achieve lower Total Harmonic Distortion (THD) values and increase the overall efficiency of the PV system. The proposed advanced control strategy shows particular effectiveness in tracking speed, disturbance rejection, and power quality enhancement. This study underscores the potential of nonlinear control techniques in optimizing microgrid operations and facilitating the integration of renewable energy sources into the main grid.

Study on Self-tuning of Robot Parameters for EMC Vehicle Steering Test

With the continuous advancement of electric vehicles and smart internet technologies, ensuring vehicle safety through electromagnetic compatibility (EMC) testing in complex electromagnetic environments has become increasingly critical. However, due to the significant variability in vehicle response characteristics under electromagnetic interference, traditional PID control methods for steering robots struggle to meet the high-precision requirements of such tests. In this study, a novel fuzzy PID parameter self-tuning method is proposed, leveraging a Sparrow Search Algorithm-Back Propagation (SSA-BP) neural network. This method optimizes the fuzzy controller's quantization factor by constructing a neural network system where the expected motor angle serves as the input and the quantization factor as the output. The quantization factor is then calibrated online through iterative training.The proposed approach enables the steering robot to achieve real-time, adaptive tuning of the PID parameters for the drive motor by adjusting the steering torque according to different vehicle characteristics, thereby enhancing the robot's anti-interference capability and robustness in EMC testing. The effectiveness of this method is validated through Matlab/Simulink simulations, experiments conducted on the Sensodrive platform, and tests performed in an EMC anechoic chamber. The results indicate that the method offers substantial improvements in control accuracy and anti-interference capabilities, highlighting its strong potential for practical application.

Blockchain-based Enterprise Community Data Access Control Methods and Applications

Data, as a fundamental resource in the digital economy of enterprise communities, can significantly enhance digital development and enterprise growth if shared securely and orderly. Despite providing detailed control, traditional attribute-based data access control methods face challenges related to privacy protection and data ownership, potentially causing data loss and trust issues. To tackle these challenges, this paper presents a blockchain-based data sharing management system, which merges the decentralized and traceable aspects of blockchain technology with attribute-based access control.

Energy-efficient and quality-focused control of conveyor belt dryers in petrochemical production

The petrochemical industry’s dryers, which operate at high temperatures, consume substantial energy and require precise temperature control to maintain product quality. To address these challenges, conveyor belt dryers are used for continuous drying, but optimizing their operation is complex. Traditional control methods often rely on operator experience or fixed settings, which may not be optimal. This paper introduces a novel approach using a Graph Neural Network and Multiagent Deep Deterministic Policy Gradient algorithm. This method models dryers’ components—chambers, fans, and belts—as cooperating agents, optimizing temperature, fan speed, and belt speed based on quality predictions. The algorithm, trained with industrial data, improves decision-making by integrating quality predictions into its reward function. Extensive experiments show a 5% improvement in product quality and a 3% reduction in energy consumption per time step, translating to significant energy savings. This approach enhances both efficiency and product quality control in conveyor belt dryers.

Optimal control of grid-connected overvoltage of distributed photovoltaic power generation based on cluster division

To address the overvoltage problem caused by the reverse flow of current when a high proportion of distributed photovoltaic (PV) is connected to the distribution network, this paper proposes a grid-connected voltage regulation control strategy based on the cluster division of the Distributed Model Predictive Control (DMPC) algorithm. Firstly, the overvoltage responsibility of each node is calculated using the Shapley value method. This is combined with k-means clustering to achieve effective cluster division, enabling dynamic adjustment of the active and reactive power of photovoltaic power generation units to stabilize regional voltage. Secondly, a group grid-connected voltage control strategy is introduced. This strategy controls the active and reactive power outputs by integrating real-time power output and voltage information from PV generating units in the region with the DMPC algorithm, ensuring overall voltage stability of the grid-connected system. Finally, actual overvoltage data from a 10 kV distribution line in the Dingxi power grid, Gansu Province, is used to verify that under the proposed control strategy, PV grid-connected overvoltage nodes are maintained within 1.06 p.u. The control effect is improved by a margin of 0.05 compared to traditional control methods. This demonstrates the effectiveness of the grouped grid-connected voltage regulation control strategy, achieving smoother voltage regulation performance in distributed PV grid-connected systems.

A Robust Control Strategy for Effective Field-Oriented Control of PMSMs

Field-Oriented Control (FOC) is widely recognized as a standard framework for Permanent Magnet Synchronous Motor (PMSM) drives. Linear control techniques are commonly employed in designing controllers for this strategy. However, traditional control methods often exhibit performance limitations and reduced robustness, particularly under harsh operating conditions, which makes the FOC structure less appealing and less effective. To address and overcome these challenges, this study proposes a Second-order Non-singular Terminal Sliding (SNTS) mode approach to achieve fast, accurate, and robust tracking for the FOC control structure applied to PMSM drives. The SNTS method combines the benefits of non-singular terminal sliding mode and second-order control laws. This approach ensures rapid and precise tracking while minimizing steady-state errors by using a nonlinear terminal sliding mode surface instead of a linear one. Furthermore, the system state transitions smoothly along the sliding mode surface with continuous functions, which reduces chattering around the sliding surface. The second-order control law incorporated into this method helps mitigate chattering and achieve fast convergence. The Lyapunov stability theory is employed to verify the stability of the SNTS technique designed for the PMSM system. Simulation and experimental validation on a hardware platform confirm the effectiveness and superiority of the proposed SNTS method, demonstrating its capability to enhance the performance of speed controllers for PMSM drives.

Construction error control method of large-span spatial structures based on digital twin

In the process of construction of large-span spatial structures, there are many control elements, which need to carry on the dual control of internal force and shape. The traditional control method cannot measure the construction deviation completely and accurately, and there is a lag in information feedback. In this paper, spatio-temporal information is integrated into digital twin and a construction error control method is proposed for large-span spatial structures. By analyzing the fusion mechanism of spatio-temporal information and digital twin, a closed-loop control system for structural errors before, during and after construction is formed. Driven by the control architecture, the method of structure simulation analysis and real-time monitoring during construction is proposed, and the functional relationship between cable force and cable length is established. Based on point cloud data, construction error optimization detection method is proposed for structure formation state. The nonlinear relation between elevation change rate and cable force deviation is formed and the control measures of formation state error are obtained. The experimental model of cable truss structure is taken as an example to apply and verify the theoretical method. The construction error control twin platform is formed in the experiment. Driven by the platform, the twinning analysis and real-time monitoring of the construction process are carried out to control the error of the structure formation. The results show that this method integrates spatio-temporal information into digital twin to realize data integration processing. The optimized error detection method can save 30% time cost and effectively control the construction error within 3%. The integration of spatio-temporal information and digital twin provides theoretical and algorithm support for the intelligent management and control of large-span spatial structure construction.

Intelligent Monitoring System for Roller-Compacted Earth-Rock Dam Construction Process

Abstract The conventional quality control method of earth-rock dam is insufficient to meet the requirements of intelligent and fine management of dam filling construction in the new stage. With the expansion of construction scale and the overall improvement in information technology level, the traditional compaction quality control methods no longer meet the demands of modern mechanized rapid construction or provide real-time compaction information for engineering contractors. In response to the current conventional design and construction schemes for earth-rock dams, an intelligent real-time monitoring system established in this paper for compaction construction of earth-rock dams. This system utilizes advanced Beidou high-precision positioning and navigation technology, vibration-sensing IoT technology, real-time processing of massive construction information, and BIM technology. It achieves real-time intelligent monitoring of key compaction processes in dam construction and refined management of unit engineering. The intelligent system established provides crucial technical support for the safe and reliable operation of water conservancy and hydropower projects. Simultaneously, it enables real-time, remote, and intelligent monitoring of dam construction processes, significantly enhancing the technological content and informatization level of water conservancy engineering construction.

Pushing the boundaries of aphid detection: An investigation into mmWaveRadar and machine learning synergy

Agriculture, essential for global sustenance and economic vitality, faces significant threats from pest-induced damages, resulting in substantial crop losses and affecting food supply if not detected on time. Traditional pest control methods, primarily reliant on pesticides. However, blindly applying pesticide may cause environmental issue. Therefore detecting the infested crops at early stage is crucial for application of sustainable pest management solutions. This study innovatively employs the IWR1443BOOST FMCW Millimeter Wave Radar (mmWaveRadar) in conjunction with machine learning algorithms such as SVM, Random Forest, Adaboost, Lightgbm, Catboost, and edRVFL for enhanced pest detection in crops. Our novel framework encompasses the collection and pre-processing of mmWaveRadar data from both healthy and infested crops, followed by comprehensive feature extraction. Decision tree-based methods exhibited a remarkable detection accuracy of 98%. EdRVFL demonstrated a 95% detection accuracy. SVM, post-feature selection, achieved a 90% accuracy. The research reveals the efficacy of the mmWaveRadar as a robust tool, overcoming the environmental and concealment limitations of conventional image-based pest detection methods. The integration of curated features with machine learning algorithms has shown promising empirical results, establishing a connection between the discerned features and the real-world attributes of healthy and infested crops. This study underscores the potential of mmWaveRadar, coupled with specific machine learning algorithms, as a significant stride towards sustainable and effective pest management strategies in agricultural technology.

MIMO ultra-local model-based adaptive enhanced model-free control using extremum-seeking for coupled mechatronic systems

Multiple degree-of-freedom (DOF) mechatronic systems, such as robots and robotic arms, play a crucial role in modern life and production. However, due to strong coupling, uncertain dynamics, and external disturbance, accurately modeling these systems is challenging, making traditional model-based control methods impractical. To address this, this paper proposes an extremum-seeking-based adaptive enhanced model-free control for multi-input multi-output (MIMO) mechatronic systems to realize robust trajectory tracking. Unlike previous model-free control methods that decouple and reorganize the MIMO system into several single-input single-output ultra-local models, this paper develops a MIMO ultra-local model with a non-diagonal gain matrix α to approximate the system dynamics within an ultra-short time window. Time-delay estimation (TDE), Proportional-Derivative (PD) control law and accuracy compensation compose an TDE-based enhanced intelligent PD control that ensures the closed-loop stability. Furthermore, an extremum-seeking (ES) technique is designed to optimize the gain matrix α to enhance control performance. The main contributions of this paper are the development of a model-free control framework based on the MIMO ultra-local model and the successful application of ES to optimize the non-diagonal gain matrix α. Stability analysis of the closed-loop system is conducted using Lyapunov theorem. Finally, numerical simulations on a 2-DOF robotic manipulator and co-simulation results on a 3-DOF PUMA 560 robotic manipulator validate the effectiveness and superiority of the proposed methods.

Nonlinear Parameter Identification of the Franka Emika PANDA Robot

This research addresses the problem of dynamic parameter identification for robot manipulators. As the complex manipulation of tasks increases, traditional control methods become insufficient, necessitating accurate model-based control. Previous studies have explored various parameter identification methods for robot manipulators. Still, the impact of different friction models on dynamic parameter identification, particularly for the Franka Emika PANDA robot, is yet to be comprehensively investigated. The linear and nonlinear parameter identification methods and nonlinear friction models (Lugre, Stribeck, and Sigmoidal) were studied on the PANDA robot. A linear inverse dynamic model was developed using the Newton-Euler method. Identification and validation trajectories were designed. Nonlinear constraints and bounds were applied for easier convergence, ensuring a positive definite inertia tensor in the center of mass (COM) frame. Using constrained least squares with Lasso regularisation, model-estimated torques comparison to measurements was done to estimate the physical parameters, considering the PANDA robot’s hand as a payload. We obtained the PANDA robot’s physical parameters, including the hand’s mass, without a friction model where discrepancies in joint torques were observed. Incorporating friction models reduced these discrepancies, validated by another trajectory comparison between the measured and the model’s predicted joint torques. Stribeck friction model exhibited the best performance in estimating parameters from its Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), indicating its effectiveness in capturing nonlinearities in the PANDA robot.

Enhanced operation of PVWPS based on advanced soft computing optimization techniques

This study introduces three soft computing (SC) optimization algorithms aimed at enhancing the efficiency of photovoltaic water pumping systems (PVWPS). These algorithms include the Gorilla Troop Algorithm (GTO), Honey Badger Algorithm (HBA), and Snake Algorithm (SAO). The goal of the SC optimizers is to maximize the output power of the PV array (PPV) and enhance the efficiency of the DC motor (η), thereby optimizing the water flow rate (Q) of the pumping system. The analytical modeling approach proposed in this study involves forecasting the optimal duty cycle (Dop) for a buck-boost converter, taking into account variables such as solar radiation (G) and ambient temperature (T). A comparative analysis is conducted between the suggested SC optimizers and analytical modeling. MATLAB simulation is employed to explore an adaptive neuro-fuzzy inference system (ANFIS) trained for the proposed system. The objective is to assess system performance and accuracy. Findings indicate a strong convergence between the analytical model and the simulation model utilizing SC optimizers. Moreover, the neuro-fuzzy system trained offline, coupled with the proposed SC optimizers, demonstrates superior performance compared to traditional control methods like perturb and observe (P&O) and incremental conductance (IC). This superiority is evident across various metrics including motor efficiency (η), photovoltaic (PV) output power (PPV), water flow rate (Q), and time response.

Real-Time Milk Quality Control Using Multi-Spectral Sensing and Edge Computing: Advancing On-Site Detection of Milk Components with XGBoost

This study explores the use of edge computing technologies to enhance the quality control processes in the dairy industry. Traditional milk quality control methods can be time-consuming and sometimes inadequate, whereas this new approach offers real-time data processing and rapid decision-making capabilities. The objective of the study is to assess the effectiveness of evaluating various spectral characteristics of milk in predicting critical parameters such as protein and fat content. In this research, a multi-channel sensor capable of collecting spectral data at various wavelengths was utilized. The collected data were processed using advanced machine learning models, where XGBoost and other regression models were assessed for their accuracy in predicting protein and fat content. The findings demonstrate the suitability of this technology for quality control in the dairy industry. The results reveal that edge computing-based systems can determine milk quality more quickly and accurately. This technology holds significant potential for overcoming the challenges faced in milk quality control, particularly in developing countries. This study provides valuable insights into how the use of edge computing can enhance operational efficiency and ensure product quality in the dairy industry. This research represents an important step towards developing more effective quality control mechanisms in the dairy industry and aims to establish a robust foundation for future studies. Recommendations focus on the adaptation of this technology to other food safety applications and its diversification for widespread industrial use.

A deep reinforcement learning control method for multi-zone precooling in commercial buildings

In commercial buildings, implementing precooling measures before office hours in summer can effectively meet the thermal comfort needs of employees. However, in multi-zone environments, differences in the cooling rates between regions often exacerbate the heat transfer interference between zones, increasing the complexity of the precooling system and leading to energy waste with limited cooling capacity. To overcome these challenges, we have developed a novel multi-zone precooling control method, which integrates deep reinforcement learning (DRL) to optimize the heat transfer process by adjusting the Air Handling Units (AHUs) valve openings, thus achieving uniform precooling across the building. Comparisons with traditional precooling control methods demonstrate the effectiveness of the proposed method. The results show that, under conventional conditions, compared with the rule-based control (RBC) and proportional integral derivative (PID) methods, the precooling time is reduced by 11.4% and 5.8%, respectively, the complexity of heat transfer is reduced by 77.6% and 64.1%, and energy consumption is reduced by 14.5% and 9.3%. In addition, the study analyzes the influence of environmental parameters on precooling optimization. The findings indicate that weather conditions have the most substantial impact on short-term precooling performance, followed by building thermal performance and cooling conditions.

Output feedback control of LTI systems using quantized periodic event-triggering and 1-bit data transmission

This paper presents a novel framework for periodic event-triggered control (PETC) coupled with dynamic quantization for linear systems. Unlike traditional time-driven control methods, our approach leverages event-based mechanisms to judiciously update control actions, thus minimizing computational load and network traffic. We introduce a two-level dynamic quantizer for encoding feedback information with a single bit, thereby enhancing resource efficiency. The proposed PETC mechanism decides the transmission instants based on the quantized output samples. The resulting system is modeled as a hybrid dynamical system to capture both continuous and discrete dynamics. Sufficient conditions for ensuring the stability of the closed-loop system are presented in the form of a linear matrix inequality. Through numerical simulations, we demonstrate that our approach captures the initial output within a finite time and significantly reduces data transmissions compared to traditional methods. This paper makes key contributions in the integration of dynamic quantization with PETC, leading to resource-efficient and stable control systems.