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Braking system is a crucial component of tractors as it ensures safe operation and control of the vehicle. The limited space availability in the workspace of a small tractor exposes the operator to undesirable posture and a maximum level of vibration during operation. The primary cause of road accidents, particularly collisions, is attributed to the tractor operator's insufficient capacity to provide the necessary pedal power for engaging the brake pedal. During the process of engaging the brake pedal, the operator adjusts the backrest support to facilitate access to the brake pedal while operating under stressed conditions. In the present study, a linear actuator‐assisted automatic braking system was developed for the small tractors. An integrated artificial neural network proportional–integral–derivative (ANN‐PID) controller‐based algorithm was developed to control the position of the brake pedal based on the input parameters like terrain condition, obstacle distance, and forward speed of the tractor. The tractor was operated at four different speeds (i.e., 10, 15, 20, and 25 km/h) in different terrain conditions (i.e., dry compacted soil, tilled soil, and asphalt road). The performance parameters like sensor digital output (SDO), force applied on the brake pedal (Fb ), and deceleration were considered as dependent parameters. The SDO was found to good approximation for sensing the position of the brake pedal during braking. The optimized network topology of the developed multilayer perceptron neural network (MLPNN) was 3‐6‐2 for predicting SDO and deceleration of the tractor with a coefficient of determination (R2 ) for the training and testing datasets of the SDO and deceleration were obtained as 0.9953 and 0.9854, and 0.9254 and 0.9096, respectively. The Ziegler–Nichols (Z‐N method) method was adopted to determine the initial optimal gains of the PID controller and later these coefficients were optimized using response surface methodology. The optimized proportional (Kp ), integral (Ki ), and derivative (Kd ) coefficient values were 4.8, 6.782, and 3.15, respectively. The developed integrated ANN, that is, MLPNN and PID‐based algorithm could successfully control the position of the brake pedal during braking. The stopping distance and slip of the tractor during automatic braking increased with an increase in the forward speed for the tractor from 10 to 25 km/h in all the selected terrain conditions. [ABSTRACT FROM AUTHOR]

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Direct current (DC) motors have superior features such as operating at different speeds, being affordable and easily controllable. Therefore, DC motors have many uses, such as machine tools and robotic systems in many factories up to the textile industry. The PID controller is one of the most common methods used to control DC motors. PID is a feedback controller with the terms Proportional, Integral, and Derivative. The proper selection of P, I, and D parameters is critical for achieving the desired control in the PID controller. In this study, the transfer function of a DC motor is first obtained, and the speed of the DC motor is controlled by the PID controller using this transfer function. Then, Particle Swarm Optimization (PSO), an optimization method based on swarm intelligence, is used to adjust the P, I, and D parameters. By using the obtained P, I, and D coefficients, the speed of the DC motor is tried to be controlled, and the effect of the filter coefficient on the system output is examined. The performance of the proposed PSO-PID controller with successful results is given in tables and graphics. Control and optimization studies are carried out with MATLAB Simulink. [ABSTRACT FROM AUTHOR]

This paper presents the operation of a modified speed controller with a standard PI/PID structure that includes the preprocessing of the controller's input signal, focusing on the past behavior of control errors. The modification involves adding a delay line, with the outputs of the individual line segments summed with a weighting method, as detailed in the paper. One of the significant advantages of this method is its use of a standard industrial controller structure, which makes it highly practical and easily implementable in existing systems. By relying on well-established control frameworks, this approach reduces the need for specialized hardware or complex modifications, allowing for smoother integration and lower implementation costs. The delay-based signal shaping shows excellent properties for the electric drive system powered by a hard-switching PWM converter. The set of weighted delays acts as a filter whose parameters are chosen using the quality function to test different configurations for optimal performance. When tested in a speed control system for a Permanent Magnet Synchronous Motor, the modifications improved the control quality index, indicating better performance and efficiency. Importantly, the system allows for reducing or eliminating the gain in the differentiating part of the controller, which decreases motor current chattering and noise. This paper includes an experimental verification of the proposed solution in a laboratory setting under semi-industrial conditions. [ABSTRACT FROM AUTHOR]

Friction stir welding (FSW) offers significant advantages over fusion welding, particularly for high-strength alloys like Inconel 718. However, achieving optimal surface quality in Inconel 718 FSW remains challenging due to its sensitivity to temperature fluctuations during welding. This study integrates finite element simulations, statistical analysis, and advanced control methodologies to enhance weld surface quality through adequate thermal management. High-fidelity simulations of the FSW process were conducted using a validated 3D transient COMSOL Multiphysics model, producing a comprehensive dataset correlating process parameters (rotational speed, axial force, and welding speed) with workpiece temperature. This dataset facilitated statistical analysis and parameter optimization through Analysis of variance (ANOVA) method, leading to a deeper understanding of process variables. A nonlinear state-space system model was subsequently developed using experimental data and the system identification toolbox in Matlab, incorporating domain-specific insights. This model was rigorously validated with an independent dataset to ensure predictive accuracy. Utilizing the validated model, tailored control strategies, including proportional-integral-derivative (PID) and model predictive control (MPC) in both single and multivariable configurations, were designed and evaluated. These control strategies excelled in maintaining welding temperatures within optimal ranges, demonstrating robustness in response times and disturbance handling. This precision in thermal management is poised to significantly refine the FSW process, enhancing both surface integrity and microstructural uniformity. The strategic implementation of these controls is anticipated to substantially improve the quality and consistency of welding outcomes. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The control of heliostat is crucial for the development of solar tower power plant. Currently, most power plants use open‐loop control, which has low cost but low efficiency, closed‐loop control has high concentrating efficiency, but each heliostat requires sensors and has high cost, and Proportional‐Integral‐Derivative (PID) controller has good control effect, but the parameter adjustment is difficult and overshooting problem occurs. In this paper, we propose a DDPG‐based heliostat cluster control aimed at improving the heliostat control effect and reducing the control cost. A leader‐follower strategy is used to control the heliostat, where the whole heliostat field is divided into several groups, each group is assigned a leader heliostat, and the rest of the heliostats follow the leader heliostat to rotate. The leader acquires the control error by means of a photoelectric sensor or a camera device. The following heliographs rotate with the leader to obtain the control signal, so there is no need for sensors, which reduces the number of sensors and lowers the cost. To address the shortcomings of traditional PID, we propose a DDPG‐based PID control algorithm. The algorithm is trained to find out the optimal value at each moment, which ensures that the controller parameters are optimal at each moment. The results show that the tracking error is below 0.0001 rad for both cluster control and individual control. This ensures effective tracking performance while reducing the sensor cost. The controller based on the DDPG algorithm eliminates overshoots, reduces errors, and shortens the stabilization time by 0.5 seconds. [ABSTRACT FROM AUTHOR]

The proportional‐integral‐derivative (PID) was developed and recognized for its reliability. A PID controller is not only simple but also relatively cheap. However, the controller causes system performance degeneration over time due to the presence of windup in a motor speed control system. The windup phenomenon is caused by the saturated control state. Various anti‐windup methods were introduced to decrease a system's long settling time and extreme overshooting. Most anti‐windup techniques require integral switching between saturated and unsaturated states, whereby both versions of steady‐state integral proportional‐integral controller do not need integral switching mechanism. They possess a certain degree of decoupling between kp$$ {k}_p $$ and ki$$ {k}_i $$ tuning parameters and tested to allow a more comprehensive range of tuning in the absence of derivative control. This research investigated the impact of derivative control component on the tuning gain decoupling through hardware simulation. By integrating the derivative component, kd$$ {k}_d $$, the control system demonstrated an improved system stability and reduced overshoot. Result shows that the decoupling feature allows SIPIC01+D and SIPIC02+D controllers to produce performance with zero overshoot and short settling time. However, SIPIC01+D has better dynamical performance with fastest rise and settling time with no overshoot as compared to other anti‐windup controllers. [ABSTRACT FROM AUTHOR]

A thorough literature review was conducted on the effects of free surface oscillation in open channels, highlighting the risks of the occurrence of positive and negative surge waves that can lead to overtopping. Experimental analyses were developed to focus on the instability of the flow due to constrictions, gate blockages, and the start-up and shutdown of hydropower plants. A forebay at the downstream end of a tunnel or canal provides the right conditions for the penstock inlet and regulates the temporary demand of the turbines. In tests with a flow of 60 to 100 m3/h, the effects of a gradually and rapidly varying flow in the free surface profile were analyzed. The specific energy and total momentum are used in the mathematical characterization of the boundaries along the free surface water profile. A sudden turbine stoppage or a sudden gate or valve closure can lead to hydraulic drilling and overtopping of the infrastructure wall. At the same time, a PID controller, if programmed appropriately, can reduce flooding by 20–40%. Flooding is limited to 0.8 m from an initial amplitude of 2 m, with a dissipation wave time of between 25 and 5 s, depending on the flow conditions and the parameters of the PID characteristics. [ABSTRACT FROM AUTHOR]

The article presents the design of an adaptive controller, simulation, code generation, and porting of the algorithm into target hardware, movement of the 7.5 m antenna, testing, verification, and validation of theoretical and experimental results of the antenna system. The conventional servo control methods based on PID controllers are being used for tracking X-band missions. As the need for higher data rates is increasing, missions are switching to the Ka band, as a result the satellite ground stations will have to track the satellite in the Ka band where the antenna band beam width is narrowed by a factor of 3 when compared to the X-band antenna beamwidth. These challenges had driven the designer to design and implement a Model Reference Adaptive Controller over PID to improve and achieve the tracking accuracy of less than 25m° from the present 30 m°. Thus, consistent and constant performance is achieved throughout the trajectory tracking of the mission using PID + MRAC (Model Reference Adaptive Control). MRAC uses a reference model that specifies the desired response of the antenna system and adjusts the controller parameters such that the plant follows the reference to achieve the desired performance. The entire drive chain modules and their parameters are considered in the design of the plant, namely MRAC controller, PID controller, Power Drive electronic (Rectifier and Inverter), Servo Motor, mechanical torque coupling, Gear reducer, Slew Ring Bearing (SRB), and absolute encoder modules of each axis for position feedback from the antenna. Model Order Reduction of higher-order actual Plant model to the standard second-order system is optimized. The modelled and analysed outcomes of the MRAC scheme using the Modified MIT are demonstrated. The developed model is tested against step, ramp, parabolic, satellite trajectory, velocity and acceleration inputs in MATLAB/Simulink and validated with practical/ experimental test results with antenna load at the satellite ground Station. The achieved results indicate that the PID + MRAC controller has significant and consistent performance improvements when compared to the PID controller alone. [ABSTRACT FROM AUTHOR]

In order to enhance the accuracy of temperature control of the patenting furnace, a new temperature control system (TCS) based on the fuzzy adaptive (FA)–proportion integral differential (PID) control algorithm is proposed. The system takes temperature error value and error rate as input and automatically adjusts the PID control parameter value according to the signal of the furnace temperatures to realize adaptive adjustment. Based on the parameter of the FA from simulations, the results of online application showed the temperature fluctuation reduced from 14 to 4°C in Zone I, and it decreased from 15 to 5°C in Zone II. Thus, the accuracy control of operational ability of the TCS was improved obviously. Following practical application, the microstructure analysis of the steel wire postpatenting demonstrated the creation of a more consistently distributed sorbite with enhanced properties by leveraging the new FA‐PID system. [ABSTRACT FROM AUTHOR]

This article shows that the PID control, applied in linear systems, is introduced to handle the adaptive fixed-time PID-based control issue of uncertain nonlinear systems. For handling smooth complex unknown nonlinearities existing in the considered system, neural networks will be introduced to estimate these functions. Furthermore, by integrating the self-tuning gain PID controller, an actual controller is proposed. The stability analysis and the simulations all demonstrate the utilisability of the given scheme. [ABSTRACT FROM AUTHOR]

For lithium batteries in fast charging and other scenarios, natural cooling is usually difficult to ensure that the battery temperature is working in the appropriate range, which is very likely to cause the battery temperature is too high or even thermal runaway. The thermal management system integration scheme for different working conditions is proposed, and the vehicle thermal management system model is constructed by AMESim, the compressor speed of the air conditioning system is taken as the regulating quantity, and the compressor speed is regulated by PID control and fuzzy control according to the feedback battery temperature, and the temperature change of the controlled target is analysed by using the high temperature in summer and the intense driving condition US06 as the testing condition. The results show that the designed thermal management integration method and control strategy can meet the cooling requirements of each subsystem, and the fuzzy control can obtain better control effect than PID control. [ABSTRACT FROM AUTHOR]

This work presents an unprecedented resolution for the lack of harmony between wired network TCP, which transacts with stationary routers, and the Ad-hoc, which appoints mobile hosts as routers, causing failure. This is done by modifying the TCP's queue via fuzzy logic as a controller (FLC). The novelty is that when the mobile router changes location, leading to failure, the controller keeps tracking error signals, which become a behavior prediction base for the next error before occurring. The transfer function is designed with respect to the I/O parameters to optimize the system, leading to the steady with free of fluctuation and overshoot. Other than previous techniques, which depend on fixed criteria or proactive strategies such as AQM, the proposed system is distinguished by dynamic adaptation with continuous changes like mobility and signal power. Using FLC, the system response is improved by reducing time delay, packet loss, and enhancing stability and spectral efficiency. A simulation is designed to continuously monitor these parameters' results, represented by the metrics rise time, undershoot, overshoot, and steady time. For comparison purposes, the PID controller is applied, and the results show surpassing this system where the metrics rise time, undershoot, overshoot, and steady time values are for FLC 0.55, 0.017, 0, and 2.5, respectively, with fluctuation-free, while for PID 0.6, 0.526, 9.341, and 3.6533, respectively, with fluctuation. Furthermore, by dealing the FLC with the challenges of high mobility and the signal power changes, it exceeds the traditional PID controller, as the simulation results show that in terms of reducing the delay time by 28.6%, packet loss by 50%. Besides, there is a remarkable increase in the throughput by 20% and spectral efficiency by 16.7%. [ABSTRACT FROM AUTHOR]

The hydraulic support face guard system is essential for supporting the exposed coal wall at the working face. However, the hydraulic support face guard system approaching the coal wall may cause impact disturbances, reducing the load-bearing capacity of coal walls. Particularly, the hydraulic support face guard system is characterized by a large turning radius when mining thick coal seams. A strong disturbance and impact on the coal wall may occur if the approaching speed is too fast, leading to issues such as rib spalling. In this paper, a feedforward fuzzy PID displacement velocity compound controller (FFD displacement speed compound controller) is designed. The PID controller, fuzzy PID controller, feedforward PID controller, and FFD displacement speed compound controller are compared in terms of the tracking characteristics of the support system and the impact response of the coal wall, validating the controller's rationality. The results indicate that the designed FFD displacement speed compound controller has significant advantages. This controller maintains a tracking error range of less than 1% for target displacement with random disturbances in the system, with a response adjustment time that is 34% faster than the PID controller. Furthermore, the tracking error range for target velocity is reduced by 8.4% compared to the feedforward PID controller, reaching 13.8%. Additionally, the impact disturbance of the support system on the coal wall is suppressed by the FFD displacement speed compound controller, reducing the instantaneous contact impact between the support plate and the coal wall by 350 kN. In summary, the FFD compound controller demonstrates excellence in tracking responsiveness and disturbance rejection, enhancing the efficacy of hydraulic supports, and achieving precise control over the impact on the coal wall. [ABSTRACT FROM AUTHOR]

Copyright of Duzce University Journal of Science & Technology is the property of Duzce University Journal of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Proportion Integration Differentiation (PID) is a common clock disciplining algorithm. In satellite clock source equipment and in Internet of Things (IoT) sensor nodes it is usually required that both time and frequency signals have high accuracy. Because the traditional PID clock disciplining method used in the equipment only performs PID calculation and feedback control on single variable, such as frequency, the time accuracy error of the clock source is large and even has inherent deviation. By using the integral relationship between frequency and time, a new two variables PID control algorithm for high-precision clock disciplining is proposed in this paper. Time is taken as the constraint variable to make the time deviation converge. It can guarantee a high accuracy of time and high long-term stability of frequency. At the same time, frequency is taken as the feedback variable to make frequency obtain fast convergence. It can ensure high short-term stability of the frequency and the continuity of time. So, it can make the time and frequency of the disciplined clock have high accuracy and stability at the same time. In order to verify the effectiveness of the proposed algorithm, it is simulated based on the GNSS disciplined clock model. The GNSS time after Kalman filtering is used as the time reference to discipline the local clock. The simulation results show that the time deviation range of a local clock after convergence is −0.38 ns∼0.31 ns, the frequency accuracy is better than 1 × 10 − 15 averaging over one day, and the long-term time stability (TDEV) for a day is about 7 ps when using the two variables PID algorithm. Compared with the single variable PID algorithm, the time accuracy of the two variables PID algorithm is improved by about one order of magnitude and the long-term time stability (TDEV) is improved by about two orders of magnitude. The research results indicate that the two variables PID control algorithm has great application potential for the development of clock source equipment and other bivariate disciplining scenarios. [ABSTRACT FROM AUTHOR]

DC–DC buck converters have been designed by incorporating different control stages to drive the switches. Among the most commonly used controllers, the sliding mode control (SMC) and proportional-integral-derivative (PID) controller have shown advantages in accomplishing fast slew rate, reducing settling time and mitigating overshoot. The proposed work introduces the implementation of both SMC and PID controllers by using the field-programmable gate array (FPGA) device. The FPGA is chosen to exploit its main advantage for fast verification and prototyping of the controllers. In this manner, a DC–DC buck converter is emulated on an FPGA by applying an explicit multi-step numerical method. The SMC controller is synthesized into the FPGA by using a signum function, and the PID is synthesized by applying the difference quotient method to approximate the derivative action, and the second-order Adams–Bashforth method to approximate the integral action. The FPGA synthesis of the converter and controllers is performed by designing digital blocks using computer arithmetic of 32 and 64 bits, in fixed-point format. The experimental results are shown on an oscilloscope by using a digital-to-analog converter to observe the voltage regulation generated by the SMC and PID controllers on the DC–DC buck converter. [ABSTRACT FROM AUTHOR]

Recent developments in aircraft electrical technology, such as the design and production of more electric aircraft (MEA) and major steps in the development of all-electric aircraft (AEA), have had a significant impact on aircraft's electrical power systems (EPSs). However, the EPSs of the latest aircraft produced by the main players in the market, Airbus with the Neo series and Boeing with the NG and MAX series are still completely traditional and based on the constant speed constant frequency (CSCF) configuration. For alternating current ones, the EPS is composed of the following: prime movers, namely the aircraft turbofan engine (TE); the electrical power source, i.e., the integrated drive generator (IDG); the command and control system, the generator control unit (GCU); the transmission and the system distribution system; the protection system, i.e., the CBs (circuit breakers); and the electrical loads. This paper presents the analysis of this system using the Simscape package from Simulink v 8.7, a MATLAB v 9.0 program, which is actually the development of some systems designed in two previous personal papers. For the first time in the literature, a complete MATLAB modelled EPS system was presented, i.e., the aircraft turbofan engine model, driving the constant speed drive system (CSD) (model presented in the first reference as a standalone type and with different parameters), linked to the synchronous generator (SG) (model presented in second reference for lower power and rotational speed) in the so-called integrated drive generator (IDG) and electrical loads. [ABSTRACT FROM AUTHOR]

This study reviews numerous control approaches utilized to address various issues encountered by unmanned aerial vehicles (UAVs). Specifically, focusing on the quadrotor system. Due to its versatility and compact size, quadrotors have gained popularity as UAVs in recent decades. Quadrotors face challenges such as ambient disturbances, impediments, non-parametric and parametric perturbations while performing tasks. Consequently, a robust and efficient control system is essential for such a system to ensure the stability and enhance their performance. It should be noted that, in this review, we have examined and analyzed the most recent highly cited papers selected from esteemed journals and magazines renowned for their exceptional quality and reputation. [ABSTRACT FROM AUTHOR]

Thanks to the emerging integration of algorithms and simulators, recent Driving Simulators (DS) find enormous potential in applications like advanced driver-assistance devices, analysis of driver's behaviours, research and development of new vehicles and even for entertainment purposes. Driving simulators have been developed to reduce the cost of field studies, allow more flexible control over circumstances and measurements, and safely present hazardous conditions. The major challenge in a driving simulator is to reproduce realistic motions within hardware constraints. Motion Cueing Algorithm (MCA) guarantees a realistic motion perception in the simulator. However, the complex nature of the human perception system makes MCA implementation challenging. The present research aims to improve the performance of driving simulators by proposing and implementing the MCA algorithm as a control problem. The approach is realized using an actual vehicle model integrated with a detailed model of the human vestibular system, which accurately reproduces the driver's perception. These perception motion signals are compared with simulated ones. A 2-DOF stabilized platform model is used to test the results from the two proposed control strategies, Proportional Integrator and Derivative (PID) and Model Predictive Control (MPC). [ABSTRACT FROM AUTHOR]

Abstract Innovations in control algorithms, integration of smart grid technologies, and advancements in materials and manufacturing techniques all push the boundaries of AVR performance. As the demand for power systems progresses with the complexity and variety of loads, conventional AVR designs may struggle to handle these ever-changing circumstances efficiently. Therefore, the need for new optimization methods is crucial to bolstering the efficiency, reliability, and adaptability of AVRs. Thus, this work aims to improve the performance of the AVR system controller by using a novel hybrid technique between the Harmony Search (HS) and Dwarf Mongoose Optimization (DMO) algorithms to tune the proportional-integral-derivative (PID) and proportional-integral-derivative acceleration (PIDA) parameters. The suggested hybrid approach ensures an accurate solution with balanced exploration and exploitation rates. The reliability of the proposed HS-DMOA is verified through comparison with different optimization techniques carried out on time and frequency performance indicators, disturbances in the form of changes to time constants, and dynamic input signals. The proposed hybrid HS-DMOA PID-based has better overshoot than PID-based HS, LUS, TLBO, SMA, RSA, and L-RSAM by 20.37%, 18.5%, 18.5%, 2.77%, 5.55%, and 2.77%, respectively. Regarding the phase margin, the proposed hybrid HS-DMOA PID-based is better than PID-based HS, LUS, and TLBO by 39%, 37%, and 38%, respectively. While the proposed hybrid HS-DMOA PIDA-based has a better overshoot than PIDA-based HS, LUS, and PID HS-DMOA-based by 14%, 17%, and 20%, respectively. Moreover, the robustness under dynamic disturbance proved the reliability of the proposed HS-DMOA PID and PIDA based through enhancement of overshoot around 0.3%~20% for different cases. Finally, the main contribution of the paper is to propose a relatively new hybrid optimization method to enhance the AVR PID and PIDA-based performance with detailed analysis in time and frequency domains under normal and dynamic disturbances.

This study reviews numerous control approaches utilized to address various issues encountered by unmanned aerial vehicles (UAVs). Specifically, focusing on the quadrotor system. Due to its versatility and compact size, quadrotors have gained popularity as UAVs in recent decades. Quadrotors face challenges such as ambient disturbances, impediments, non-parametric and parametric perturbations while performing tasks. Consequently, a robust and efficient control system is essential for such a system to ensure the stability and enhance their performance. It should be noted that, in this review, we have examined and analyzed the most recent highly cited papers selected from esteemed journals and magazines renowned for their exceptional quality and reputation.

The article aims to develop a fractional-order proportional integral derivative (PID) controller to monitor insulin and glucose levels in humans under the influences of stress, excitement, and trauma. A novel fractional-order diabetes mellitus model is proposed, incorporating a nonsingular, nonlocal kernel (Mittag-Leffler function) to account for the effect of epinephrine on suppressing insulin secretion and the dynamics of beta-cell mass. As beta-cell mass increases in the presence of adrenaline, the system remains highly responsive to rising blood glucose and falling insulin levels, driven by the hormone’s suppressive effects. The key advantage of this model is its ability to incorporate these physiological stressors and use fractional-order derivatives to describe the nonlocal dynamics within the system. The innovations of this work include a fractional-order diabetes mellitus model that captures the biological memory and hereditary effects of glucose regulation under stress, and a fractional-order PID controller that offers greater stability and robustness compared to conventional controllers, particularly in managing adrenaline-induced hyperglycemia. The model’s positivity, boundedness, and equilibrium solutions are rigorously analyzed to ensure feasibility. Additionally, a new theorem is proven using fixed-point theory, confirming the existence and uniqueness of the fractional-order model. Ulam–Hyers stability analysis further demonstrates the model’s robustness and well-posedness, while qualitative properties are explored. Numerical simulations to explore which is done by solutions with a two-step Lagrange polynomial for generalized Mittag Leffler kernel showed that prolonged and severe hyperglycemia was caused by regular release of adrenaline into the blood at different fractional order values and fractal dimensions by changing initial values for normal and diabetes patients. PID and controller results are analyzed to increase the stability of the system to monitor and assess of glucose–insulin system with beta cell mass to control the hyperglycemia. Lastly, the results are obtained and visually shown using graphical representations, which provide empirical evidence in support of our theoretical findings. At the end comparison of numerical simulations is constructed to show the efficiency, convergence, and accuracy of proposed techniques at different fractional values with power law and exponential kernels. Numerical simulations, mathematical modeling, and analysis work together to shed light on the dynamics of diabetes mellitus and make important advances in the knowledge and treatment of this common disease.

This paper presents the design and optimization of a climbing robot. The design of a ladder‐climbing robot is done with fundamental mathematical considerations. The designed robot is robust enough to manage all environmental calamities, and at the same time, it is optimized for lightweight to reduce the actuator's cost and ease of transportation. An analytical evaluation is carried out for both static and dynamic conditions to determine strength and motion characteristics. The multiobjective optimization of the design parameters of a ladder‐climbing robot is done to obtain optimized values of design parameters. The formulation of an optimization problem that considers the minimization of weight and natural frequency is performed. Using an evolutionary genetic algorithm (GA) for the multicriteria optimization problem is solved, and a Pareto front solution is obtained. The optimal values of the parameters are decided based on the knee selection technique. As both objective functions are contradictory, the optimum results significantly improve the robot's performance. Controlling the proportional–integral–derivative (PID) parameters is crucial as the robot climbs with a two‐point contact gait pattern. The controlling parameters impart stability to the robot. PID parameters like proportional, integral and derivative gain are tunned using the GA. Finally, the developed prototype is tested on the ladders of the tower, and satisfactory climbing motion is achieved. [ABSTRACT FROM AUTHOR]

The primary challenge faced by a neural controller in the dynamic model of a mobile robot lies in its ability to address the inherent complexity of the system dynamics. Given that mobile robots exhibit nonlinear movements and are subject to diverse environmental conditions, they contend with a challenging dynamic environment. The neural controllers must demonstrate the capability to continuously adapt and effectively learn to manage the variability present in the dynamic of the robot. This paper presents two intelligent controllers utilizing neural networks, showcasing their relevance in the field of robotics. The first controller, referred to as the neural PID (PIDN), integrates the traditional PID controller with a neural component. The second controller leverages the dynamic model of a differential robot to improve trajectory tracking, employing a parallel architecture that combines PID with neural networks (PID+NN). Our proposals adhere to a cascading structure, where the outer loop takes the lead in reducing position errors through a kinematic controller, while concurrently, the inner loop is employed to regulate linear and angular velocities through the proposed controllers. The controllers are validated in the CoppeliaSIM simulator, offering a realistic setting for evaluating the behavior of the chosen Pioneer 3-DX robot. To comprehensively assess controller performance, three strategies are examined: PIDN, PID+NN, and the conventional PID. Through a blend of qualitative and quantitative analyses, employing diverse performance metrics, the advantages of our proposed controllers become apparent. Doi: 10.28991/ESJ-2024-08-04-01 Full Text: PDF

This research aims to optimize the fractional order proportional integral derivative (FOPID) controller for nonlinear process tests. Instead of relying on the traditional trial-and error method for dynamic parameter selection, this proposes a modified harmony search optimization method to compute the optimal dynamic parameters. Based on the physical parameters, using mathematical modelling techniques system transfer function is determined. Additionally, the delay of the control scheme is determined using the Open Loop Transfer Function (OLTF) response. The dynamic parameters of the FOPID controller are evaluated using the Harmonic Search Algorithm – Fractional Order Proportional Integral Derivative (HAS_FOPID) optimization technique, which aims to minimize the system's Integral Square Error (ISE). Time responses, including rise time, peak time, and peak overshoot, are obtained and optimized through the HAS_FOPID algorithm. To verify systems stability, the output response is analysed using various techniques such as bode plots, pole placement, and Nyquist plots. Furthermore, the HAS_FOPID optimization technique is compared with other natural optimization techniques to assess its effectiveness. The research also evaluates the system's robustness by supply/ load disturbances. The objective is to demonstrate that, the optimized HAS_FOPID technique, has significantly enhanced control performance, stability, and robustness of nonlinear systems compared to natural and alternative optimization approaches.

Research.fi is a national service for collecting, integrating and disseminating information on research conducted in Finland. This includes information on publications and related research outputs (including in the arts), datasets, funding decisions and research infrastructures. It has a specific section for researcher profiles, focusing on researcher descriptions, their research activities, merits, education - moving towards an affiliation-independent research CV. The plan is that within the Research.fi service, projects will become entities that bind different research objects together, even when funded from multiple sources and involving various researchers and organizations that may change over time. This concept of a project is aligned with the definition of a project in RAiD – the Research Activity Identifier. The RAiD service, developed originally by the Australian Research Data Commons, is being rolled out and adapted to the European context. RAiD provides a persistent and globally unique identifier that resolves in a browser to standardized information about a particular research project. Minting persistent identifiers (PIDs) for research projects facilitates the management of information about related participants, organizations, resources, and outputs. RAiD does so primarily by collecting other PIDs (where possible) representing project components, plus descriptive information about the project, storing it all in a metadata record associated with the identifier. This paper documents and discusses the implementation of RAiD in Europe and piloting its use in the national Finnish service context. [ABSTRACT FROM AUTHOR]

Buck-boost converters are one of the important devices in power conversion applications that are capable of increasing or decreasing the voltage according to the load requirements. However, the main challenge in operating a buck-boost converter is maintaining the stability of the output voltage. The selection of PID control is one of the ways that can be used to keep the output voltage stable, in this study, buck boost with 12 volts input is used and PID control is set with several parameters that can reduce errors and stabilize the output voltage, the PID constant values are Kp = 2 and Ki = 0.2 with these parameters obtained less stable results with time rise = 72.45 ms and overshoot = 0.995 % for boost mode with voltage 24 volt output and time rise = 123.2 ms and overshoot = 0.995 % for buck mode with an output voltage of 5 volts. Thus, to maximize the system with a stable output voltage, a fuzzy logic control is used which has an adaptive time response and is able to adapt to load variations, the simulation results show that the output voltage of the fuzzy logic control has a time rise = 114 ms and overshoot = 0.761 % in boost mode and time rise = 104 ms with overshoot = 0.971 % in buck mode. The advantage of fuzzy logic control is that it is more optimal and able to improve system performance responsively compared to other conventional controls. [ABSTRACT FROM AUTHOR]

A control strategy must be developed to maintain the product composition of the two- or more-mixed-flow reactor series. In this research, a two-mixing tank series (TMTS) was utilized in the laboratory to approach the two-mixed flow reactors' problems. Both tanks are designed to overflow so that the volume remains constant. The salt solution was directly fed to Tank 1, while the water was charged to both tanks as the mixing progressed. The output of Tank-1 flowed to Tank-2 through a rather long pipe, resulting in dead time in Tank-2. The Smith predictor was applied to overcome dead time. The PRC (process reaction curve) method was used to tune the PID (proportional-integral-derivative) parameters of Tank-1 and Tank-2. The novel composition controls, adaptive-PID (APID) and modified metaheuristic adaptive-PID (MMAPID), have been proposed and compared with conventional PID. The mathematical models were rigorously examined through XCOS simulations. The processing system is considered sensitive to changes in input disturbances since the process time constants of both tanks were quite low. The dead time of 0.5 minutes was found in Tank 2. The PID parameters produced by PRC experiments resulted in stable responses to changes in the disturbance system. As shown in the dynamic simulation study, both novel APID and MMAPID give almost the same results; their responses are faster than the conventional PID. Based on the integral absolute error (IAE) results, APID with scheduling PID parameters produced the smallest IAE and outperformed the conventional PID. The results of the study highlight APID and MMAPID potential in PID control applications for composition control in a TMTS. The combined APID-Smith and MMAPID-Smith were able to overcome the dead time well. This study creates opportunities for engineers and professionals to use cutting-edge control technologies, improving automation and chemical process industries. [ABSTRACT FROM AUTHOR]

In addressing the steering stability issues of four-wheel-drive electric vehicles on surfaces such as wet, slippery, frozen, and soft terrains, a novel control method based on particle swarm optimization for neural networks is proposed in this study. The approach integrates the advantages of Proportional-Integral- Derivative control, particle swarm optimization, and neural networks. By constructing a neural network model with input, hidden, and output layers, the study introduces particle swarm optimization algorithm for weight and structure optimization. Fuzzy logic and slip control theory are integrated into the steering stability control. The results demonstrated that, under wet and slippery road conditions, the model exhibited a system response time of 15 ms with a steering prediction accuracy of up to 92%. On frozen road surfaces, the model showed a system response time of 18 ms, with a steering prediction accuracy reaching 90%. Compared to other models, it significantly demonstrated superior steering stability control. This suggests that the designed model performs well in handling complex driving environments, indicating high application potential in the field of electric vehicle steering stability control. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to wheel slip control in Antilock Braking System (ABS) using an Approximated First Order Wheel Slip (AFOWS) Model Reference Adaptive Control (MRAC) based PID (AFOWS-MRAC-PID) controller. An ABS was modeled in a MATLAB/Simulink environment using a quarter car model with the proposed controller. Simulations were conducted with a wide range of adaptation gains (50, 100, 150, 200, and 250) to study the effectiveness of the proposed control system. The results revealed that the proposed system could track and maintain 10% wheel slip and eliminate oscillation (instability) in terms of overshoot associated with conventional PID controllers, particularly on wet and snowy road surfaces, using adaptation gains of 150, 200, and 250. Overall, the proposed system provided the best performance in terms of stopping distance, vehicle braking velocity, and braking torque on all road surfaces with an adaptation gain of 250, although braking on dry road surfaces was the most effective. [ABSTRACT FROM AUTHOR]

This study presents a comparative analysis of control strategies designed to enhance the performance of Anti-Lock Braking Systems (ABS) and improve vehicle safety. The research explores three key approaches: First, it evaluates Fuzzy Logic-Controlled ABS, comparing five defuzzification algorithms using MATLAB's Fuzzy Logic Toolbox. Second, it investigates a Neural Network-Based Fault-Tolerant Control strategy, emphasizing improved fault tolerance during braking. Third, it assesses the performance of three ABS controllers--fuzzy logic, bangbang, and PID controllers. The findings reveal that Fuzzy Logic-Controlled ABS significantly enhances braking performance and directional stability, while Neural Networks demonstrate rapid response and accuracy in generating real-time substitute signals, thereby boosting system reliability. Among the controllers, the PID controller excels in reducing stopping distance and time, though the Fuzzy Logic Controller shows superior control over relative slip, enhancing steerability despite longer stopping distances and times. This comparative analysis provides valuable insights into ABS control strategies and their implications for vehicle safety. Future research should focus on refining ABS algorithms, developing robust fault detection mechanisms, and optimizing controller designs to further advance automotive safety and ABS efficiency. [ABSTRACT FROM AUTHOR]

In the continuously evolving landscape of novel smart control strategies, optimization techniques play a crucial role in achieving precise control of indoor air quality. This study aims to enhance indoor air quality by precisely regulating carbon dioxide (CO2) levels through an optimized control system. Prioritizing fast response, short settling time, and minimal overshoot is essential to ensure accurate control. To achieve this goal, chaos optimization is applied. By using the global search capability of the chaos particle swarm optimization (CPSO) algorithm, the initial weights connecting the input layer to the hidden layer and the hidden layer to the output layer of the backpropagation neural network (BPNN) are continuously optimized. The optimized weights are then applied to the BPNN, which employs its self-learning capability to calculate the output error of each neuronal layer, progressing from the output layer backward. Based on these errors, the weights are adjusted accordingly, ultimately tuning the proportional–integral–derivative (PID) controller to its optimal parameters. When comparing simulation results, it is evident that, compared to the baseline method, the enhanced Chaos Particle Swarm Optimization Backpropagation Neural Network PID (CPSO-BPNN-PID) controller proposed in this study exhibits the shortest settling time, approximately 0.125 s, with a peak value of 1, a peak time of 0.2 s, and zero overshoot, demonstrating exceptional control performance. The novelty of this control algorithm lies in the integration of four distinct technologies—chaos optimization, particle swarm optimization (PSO), BPNN, and PID controller—into a novel controller for precise regulation of indoor CO2 concentration. [ABSTRACT FROM AUTHOR]

This paper presents a comparative analysis of a fractional-order proportional–integral–derivative (FO-PID) controller against the standard proportional–integral–derivative (PID) controller, applied to a nonlinear robotic arm manipulator systems. The genetic algorithm (GA) optimization method was implemented to tune the gain parameters of the FO-PID and PID controllers. The performance of the FO-PID and PID controllers were evaluated though different cost functions, including integral of squared error (ISE), integral of absolute error (IAE), integral of time-weighted absolute error (ITAE), and integral of time-weighted squared error (ITSE). The performance of these controllers was examined via extensive simulations by using MATLAB/SIMULINK for different operating scenarios of the robotic arm manipulator system. Based on the obtained results, a comparative performance matrix is proposed, wherein cost functions ISE, IAE, ITAE, and ITSE are represented as columns while characteristic parameters (overshoot, rising time, and settling time) are represented as rows. The proposed performance matrix facilitates the selection between the PID and FO-PID controllers. [ABSTRACT FROM AUTHOR]

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In recent years, various solutions have been developed to control power electronic converters using devices available on the market that are powerful and easy to use. These solutions, in most cases, offer high performance. However, these have high implementation costs because the required devices are expensive. For this reason, this document presents the implementation of two discrete-time controllers widely used in the literature for a boost converter implemented on a low-cost platform. The objective is to obtain a constant voltage at the converter's output for photovoltaic system applications. The proportional-integral control is implemented as the first case, and the second case is a sliding mode control. In addition, a prior analysis is presented through simulation. Both control algorithms are implemented on the TMS320F28379D microcontroller from Texas Instruments through the same manufacturer's integrated development software based on an optimized C/C++ language compiler. The results of the non-linear algorithm reveal better performance in reducing the time response, the overshoot of the transient state, and the steady-state error. Finally, the significant economic savings associated with the implementation costs of the controllers tested on a low-cost platform differentiate this work from other similar ones. [ABSTRACT FROM AUTHOR]

Automotive air conditioning (ACC) system designers faced unique challenges in satisfying customer demands for efficient and comfortable operation across a broad temperature range. The AAC system's effective or fixed temperature setting makes this possible. Many climate control models, like the state flow switching controller in MATLAB control, must handle heating and cooling due to a single Simulink/environment software and frequently contain steady-state error (SSE). The present research proposes to design an automatic temperature control scheme and fuzzy logic control (FLC) for an automotive air-conditioned (AAC) system. In addition, it employs MATLAB Simulink/environment software to model the fuzzy control technique to analyze the AAC system's response. A simulation has been set up to evaluate the suggested system performance to match a selection of user-specified reference temperatures and compressor speeds. Compared with a PID-controlled AAC system, the proposed FLC-based system reduced the undershoot to only 2.30% from 33.30% respectively, and was robust, quicker, and better at controlling temperature. • Challenges in automotive air conditioning systems for designers were addressed. • An automatic temperature control scheme was proposed via fuzzy logic control. • Improved efficiency and comfort in the AAC system was achieved. • The proposed FLC-based system reduced the undershoot to 2.30% from 33.30%. • The FLC-based was more robust, quicker, and better at controlling temperature. [ABSTRACT FROM AUTHOR]

Background and Aim: Pelvic inflammatory disease (PID) is usually managed by conservative treatment, but in selected cases, especially in the presence of a tubo‐ovarian abscess (TOA), surgical management is a recognized treatment option. We compared the trends in managing PID and short‐term outcomes before and during the SARS‐CoV‐2 pandemic. Methods: This is a retrospective study performed in three Italian gynecological centers. We included patients admitted to hospital with a diagnosis of PID. Demographic characteristics, management, time to diagnosis, and time to treatment were compared before versus during the SARS‐CoV‐2 pandemic. Results: One hundred nineteen PID patients were screened, eighty‐one before the SARS‐CoV‐2 pandemic, and thirty‐eight after the onset. At admission, leukocytosis (median 19.73 vs. 13.99 WBC/mm3, p‐value = 0.02) was significantly higher in patients who underwent surgery after the onset of the pandemic. TOA incidence was higher in patients who underwent surgery during the SARS‐CoV‐2 pandemic, but the difference did not reach statistically significance (p = 0.06). The proportion of patients treated with surgery dropped to 26.3% after the onset from 46% of patients before the onset of pandemic (p = 0.03). Furthermore, a higher percentage of emergency surgical procedures on day 0 of hospital admission were performed after the onset of the pandemic (50% vs. 13.1%, p = 0.01). Conclusions: In this retrospective cohort study, we found that the SARS‐CoV‐2 pandemic influenced the clinical presentation and management of PID in favor of conservative treatment. Patients who underwent surgery during the SARS‐CoV‐2 pandemic had higher inflammatory markers. [ABSTRACT FROM AUTHOR]

Compared with the quad rotor UAV (Unmanned Aerial Vehicle), the large twin rotor UAV has the advantages of lower price, longer endurance and higher efficiency, but it has serious coupling and poor anti-in-terference ability. To solve these problems, this study proposes a dual rotor control algorithm using variable universe fuzzy PID (Proportional Integration Differentiation). In this study, the UAV system is divided into position control and attitude control. In attitude control, fuzzy PID is used to adjust the parameters of PID in real time to improve the stability of UAV. According to the motion mode of UAV, a new type of UAV structure is proposed, and the dynamic model of double rotor is established, and the simulation model is built on Simulink for verification. The results show that the proposed algorithm reduces the overshoot by 50% when compared with the ordinary PID control algorithm, which indicates that the proposed algorithm has better stability on large twin rotor UAV and can recover faster in case of interference. [ABSTRACT FROM AUTHOR]

In order to improve the intelligent perception and adaptability of the 6G network, drones joined this challenge. For large-scale long-range Unmanned Aerial Vehicle (UAV), most of the time during normal flight belongs to fixed altitude flight. It is required to sail along the planned optimal path. Whether it can fly along the optimal path is mainly attributed to the tracking problem of horizontal flight trajectory. In order to minimize the UAV horizontal plane tracking error, it is necessary to consider the influence of many factors (such as strong winds, heavy rain, obstacles, etc.). Due to the complexity of High-Altitude environment, these disturbances are uncertain. In addition, there are some dynamic errors in the model of UAV control system, and these errors also have uncertainties. And, due to the change of global planning path coordinates, the control system needs to adjust the set value in real time during AUV horizontal trajectory tracking, and the conventional control algorithm is difficult to meet the requirements. Therefore, firstly, the influence of prediction uncertainty of grey prediction on AUV horizontal track tracking control is used; Then the grey prediction is improved according to the practical application; Ultimately, the control law is designed by combining the grey prediction with the control method. Finally, the grey prediction fuzzy adaptive PID method of UAV flight control is applied to the planned path simulation, and good control results are obtained. [ABSTRACT FROM AUTHOR]

This research aims to optimize the fractional order proportional integral derivative (FOPID) controller for nonlinear process tests. Instead of relying on the traditional trial-and error method for dynamic parameter selection, this proposes a modified harmony search optimization method to compute the optimal dynamic parameters. Based on the physical parameters, using mathematical modelling techniques system transfer function is determined. Additionally, the delay of the control scheme is determined using the Open Loop Transfer Function (OLTF) response. The dynamic parameters of the FOPID controller are evaluated using the Harmonic Search Algorithm – Fractional Order Proportional Integral Derivative (HAS_FOPID) optimization technique, which aims to minimize the system’s Integral Square Error (ISE). Time responses, including rise time, peak time, and peak overshoot, are obtained and optimized through the HAS_FOPID algorithm. To verify systems stability, the output response is analysed using various techniques such as bode plots, pole placement, and Nyquist plots. Furthermore, the HAS_FOPID optimization technique is compared with other natural optimization techniques to assess its effectiveness. The research also evaluates the system’s robustness by supply/ load disturbances. The objective is to demonstrate that, the optimized HAS_FOPID technique, has significantly enhanced control performance, stability, and robustness of nonlinear systems compared to natural and alternative optimization approaches. [ABSTRACT FROM AUTHOR]

We propose a novel framework for achieving precision landing in drone services. The proposed framework consists of two distinct decoupled modules, each designed to address a specific aspect of landing accuracy. The first module is concerned with intrinsic errors, where new error models are introduced. This includes a spherical error model that takes into account the orientation of the drone. Additionally, we propose a live position correction algorithm that employs the error models to correct for intrinsic errors in real time. The second module focuses on external wind forces and presents an aerodynamics model with wind generation to simulate the drone's physical environment. We utilize reinforcement learning to train the drone in simulation with the goal of landing precisely under dynamic wind conditions. Experimental results, conducted through simulations and validated in the physical world, demonstrate that our proposed framework significantly increases landing accuracy while maintaining a low onboard computational cost. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering for Thermal Energy & Power / Reneng Dongli Gongcheng is the property of Journal of Engineering for Thermal Energy & Power and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)