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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]

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]

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]

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]

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.)

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]

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]

Decentralized control systems are frequently employed to tackle control challenges in multi-input multi-output (MIMO) systems. Hence, this paper proposes a decentralized PID controller that relies on frequency domain specifications. This controller is designed to minimize the loop interactions and fulfill the design criteria of the MIMO system. The PID parameters are calculated based on gain and phase margin specifications. Additionally, a first-order plus dead time model is derived for each of the decoupled subsystems. Furthermore, robust stability is analysed using disc margin analysis, which overcomes the constraints of traditional margins. Moreover, the paper determines a secure range of uncertainty for various levels of gain and phase uncertainties. The findings suggest that the proposed controller achieves robust stability over a broader range of margins when compared to alternative control methods. [ABSTRACT FROM AUTHOR]

This study investigates advanced control techniques to evaluate the trajectory tracking control of a two-degrees-of-freedom (2-DOF) helicopter system based on simulation and hardware implementation experiments. For this, a Quanser Aero 2 platform and its QUARC software, integrated within MATLAB/Simulink, are used to design and implement multiple controllers, including Proportional Integral Derivative (PID), Fuzzy PID, and Adaptive Neuro Fuzzy Inference System (ANFIS) controllers. A two-phase approach was followed to assess and compare these controllers' ability to handle parametric uncertainties, unmodeled dynamics, and matched disturbances. Firstly, simulation experiments were conducted using an uncertain system model, considering the controller's responses in scenarios with and without cross-coupling and matched disturbances. Subsequently, hardware experiments were conducted under the same conditions to validate the simulation results, providing real-time performance comparisons. Finally, a rigorous quantitative assessment based on multiple performance metrics including Root Mean Square Error (RMSE), peak value, Integral Square Error (ISE), Integral of Absolute Error (IAE), and Integral of Time-multiplied Absolute Error (ITAE) demonstrated overperformance achieved using ANFIS for pitch control and Fuzzy PID for yaw control. [ABSTRACT FROM AUTHOR]

The primary goal of enhancing automatic generation control (AGC) in interconnected multi-area power systems is to ensure high-quality power generation and reliable distribution during emergencies. These systems still struggle with consistent stability and effective response under dynamic load conditions despite technological advancements. This research introduces a secondary controller designed for load frequency control (LFC) to maintain stability during unexpected load changes by optimally tuning the parameters of a Proportional–Integral–Derivative (PID) controller using pelican optimization algorithm (POA). An interconnected power system for ith multi-area is modeled in this study; meanwhile, for determining the optimal PID gain settings, a four-area interconnected power system is developed consisting of thermal, reheat thermal, hydroelectric, and gas turbine units based on the ith area model. A sensitivity analysis was conducted to validate the proposed controller's robustness under different load conditions (1%, 2%, and 10% step load perturbation) and adjusting nominal parameters (R, Tp, and Tij) within a range of ±25% and ±50%. The performance response indicates that the POA-optimized PID controller achieves superior performance in frequency stabilization and oscillation reduction, with the lowest integral time absolute error (ITAE) value showing improvements of 7.01%, 7.31%, 45.97%, and 50.57% over gray wolf optimization (GWO), Moth Flame Optimization Algorithm (MFOA), Particle Swarm Optimization (PSO), and Harris Hawks Optimization (HHO), respectively. [ABSTRACT FROM AUTHOR]

The quadcopter controller plays a significant role in autonomous vehicle trajectory tracking and has been extensively examined in the literature because it is a highly nonlinear and under-actuated system. This article contributes by methodically comparing PID, BSC, IBSC, SMC, and ISMC control algorithms under standardized conditions. The study involves implementing these algorithms and optimizing their parameters using a hybrid Flower Pollination Algorithm - Genetic Algorithm (FPA-GA). Robust testing scenarios, including mass uncertainty and time-varying disturbances, are employed to assess algorithmic performance. The research centers on trajectory tracking-control approaches for highly maneuverable Unmanned Aerial Vehicles (UAVs) equipped with four heave thrusters, emphasizing controller stability through Lyapunov functions. Theoretical exploration precedes comprehensive numerical simulations, enabling a detailed comparison of the efficacy and resilience of the proposed approaches. Results from exhaustive testing reveal the consistent superiority of the BSC controller, demonstrating notably low Integral of Time-weighted Squared Error (ITSE) values of 0.1253 and 0.5262 from two simulation tests. These findings underscore the practical advantages of the BSC controller in highly dynamic UAV trajectory tracking scenarios. [ABSTRACT FROM AUTHOR]

The mathematical model for a full actuated T-shaped Tri-copter is derived in details and a suitable controller is proposed in this work. The system is governed by three tiltable motor– propeller sets to change the thrust direction to a desired angle. This will lead to the achievement of maneuverability and reduce power consumption. To control the model, a feedback linearization method is used to decouple and get a direct control on the copter degree of freedoms. Attitude and Altitude PID controller is designed to control the system and fulfil the desired trajectory. The PID parameters are tuned using Genetic algorithm (GA) optimization method. The system and the controller performance are proved using MATLAB Simulink platform through several flight scenarios. [ABSTRACT FROM AUTHOR]

Functional electrical stimulation (FES) has shown potential in rehabilitative exercises for patients recovering from spinal cord injuries. In recent developments, conventional open-loop FES control techniques have evolved into closed-loop systems that employ feedback controllers for automation. However, closed-loop FES systems often face challenges due to muscle nonlinear effects, such as fatigue, time delays, stiffness, and spasticity. Therefore, an accurate non-linear knee model is required during the design stage, and precise tuning of the feedback controller parameters is vital. A proportional– integral–derivative (PID) controller is commonly used as a feedback controller due to its simplicity and ease of implementation. However, most PID tuning methods are complex and time consuming. This paper investigates the viability of employing the pole placement technique for tuning a PID controller that regulates the non-linear knee extension model. The pole placement method aims to improve the control and adaptability of the PID controller in closed-loop FES systems, specifically by facilitating knee extension exercises. MATLAB Simulink was used to assess the effectiveness of this tuning approach. Results showed that the PID controller performed satisfactorily without non-linearities, but performance varied with the inclusion of specific non-linearities. The pole placement tuning method facilitated preliminary assessments of PID controller performance, preceding highly advanced optimization. [ABSTRACT FROM AUTHOR]

Featured Application: This study is mainly applied to the MRTD tester and improves the four-bar target to enhance the detection accuracy of the MRTD tester. Additionally, MRTD is an important parameter for infrared thermal imaging cameras, contributing to improving their accuracy. Furthermore, the dung beetle optimized fuzzy PID parameters can be applied to most control systems, enabling stable tracking of abrupt commands and compensating for the deficiencies of traditional PID control. With the widespread application of infrared thermal imagers in various fields, the demand for thermal imagers and their performance parameter testing equipment has increased significantly. There are particularly high demands on the detection accuracy of minimum resolvable temperature difference (MRTD) testers. Traditional MRTD testers have an issue with the four-bar target temperatures being easily affected by the external environment, resulting in non-uniform temperatures and imprecise detection results. This paper proposes an improvement to the four-bar targets by making them temperature-controllable. Temperature is controlled by installing thermoelectric coolers (TECs) and thin-film platinum resistors at the center and periphery of the four-bar targets with different spatial frequencies. The dung beetle algorithm is used to optimize fuzzy PID parameters to regulate the TEC's heating and cooling, improving the overall temperature uniformity of the four-bar targets. Temperature simulations of the four-bar targets were conducted on the COMSOL platform, with the control part simulated on the Simulink platform. The simulation results show that, compared to traditional PID, the fuzzy PID controller reduces overshoot by approximately 3.6%, although the system still exhibits mild oscillations. The fuzzy PID controller optimized by the dung beetle optimization (DBO) algorithm, in comparison to standard fuzzy PID, reduces the settling time by about 40 s and lowers overshoot by around 7%, with oscillations in the system nearly disappearing. Comparing the fuzzy PID optimized by the particle swarm optimization (PSO) algorithm with the fuzzy PID optimized by the DBO algorithm, the DBO-based controller shows shorter rise and settling times, further illustrating the superiority of the fuzzy PID control optimized by the dung beetle algorithm. This provides a theoretical foundation for improving the accuracy of MRTD detector measurements. Finally, experimental verification was carried out. The experimental results indicate that DBO (drosophila-based optimization) has significant advantages, and its optimized results are closer to the actual values. [ABSTRACT FROM AUTHOR]

Community enterprises engaged in rice field crab farming face significant challenges due to the absence of an electricity grid, resulting in energy shortages that hinder the operation of a 24-hour recirculating water system, essential for maintaining optimal conditions during the crab laviculture phase. Consequently, crab larva survival rates have experienced a significant 60% decline. To address this issue, this paper introduces the design and simulation of an off-grid photovoltaic solarpowered motor-driven water pumping system (PVWPS) using maximum power point tracking (MPPT) and charger controllers, to meet the power demands. The proposed MPPT-based neurofuzzy controller (NFC), in conjunction with a proportional-integral-derivative (PID) controller, maximizes the utilization of PV power for the motor pump. Additionally, a bidirectional converter facilitates battery charging and discharging. The motor speed, which directly influences the required pump flow rate, is regulated by controlling the voltage across the DC-linked capacitor, using an additional proportional-integral (PI) controller. The Kp, Ki, and Kd parameters of both the PID and PI controllers are meticulously adjusted through a genetic algorithm (GA) to optimize control performance. Simulation results demonstrate the effectiveness of the implemented MPPT-NFC and charge controller. The energy utilization efficiency can be increased by up to 97% and the overall efficiency of the PVWPS is improved by up to 10.5%, effectively overcoming the power shortage challenge. Consequently, the proposed PVWPS, utilizing MPPT-NFC with PID-controller and a bidirectional converter PI-controller, enables 24-hour operation of the recirculating water system, with the anticipation of an increased crab larva survival rate. [ABSTRACT FROM AUTHOR]

The coupled systems with numerous interdependent variables pose considerable control and stability issues in commercial chemical processes. These systems exhibit complex interactions, time delays, parameter uncertainties and disturbances, which can lead to poor performance and reduced productivity. Conventional single-loop controllers struggle to effectively manage the interactions between variables, leading to poor disturbance rejection and tracking accuracy. A more robust and efficient control strategy is essential to tackle these limitations. Hence, a robust decentralized PID controller design is proposed to address the shortcomings of coupled industrial chemical processes. The aim is to enhance control system performance by achieving better disturbance rejection and tracking accuracy, thereby improving process efficiency and product quality. The complementary sensitivity function is exploited to attain this, and a novel graphical tuning method is proposed for obtaining optimal PID controller parameters. The use of the H ∞ robust criterion ensures robust stability and minimizes the influence of external disturbances. The proposed approach offers an efficient means to design robust decentralized controllers for complex chemical processes. The simulation results demonstrate the effectiveness of the method compared to existing controllers. Numerically, the proposed controller is able to attain almost a 20 % reduction in integral absolute error and a 30 % decrease in the settling time for all cases compared to existing controllers. The graphical tuning method enables efficient parameter optimization, resulting in faster response times and reduced settling time. This work represents a significant step toward achieving enhanced control system performance and stability in coupled industrial chemical processes. [ABSTRACT FROM AUTHOR]

Micro-Hydro Power (MHP) Plant System is the renewable energy resource that utilizes water potential energy. In MHP, the energy flows depend on the rotation speed of the generator which cause instability and nonlinearity in the frequency of electrical power. It is also supported by the fluctuation on the electricity load. Therefore, this study used Fuzzy Logic Controller combined with FPA-tuned PID to control the power frequency of the load. This test consisted of 4 stages, namely testing the system without a controller, testing the system using PID, testing the MHP system with a PID controller tuned to the Flower Pollination Algorithm, and testing the system using a Fuzzy PID tuned by the Flower Pollination Algorithm. Based on these tests, the Micro-Hydro Power Plant system response using a Fuzzy PID-tuned FPA controller performed best, especially in accelerating the time to a steady state, reducing overshoot and undershoot with the fastest rise time. As for the output signal from the controller used in the MHP, optimizing the Flower Pollination Algorithm for the Kp, Ki, and Kd parameters is effective and smooth in improving all elements in the Micro-Hydro Power Plant frequency stabilization. Meanwhile, the role of the fuzzy logic controller (FLC) is not very significant, and there is relatively a lot of noise in the output signal of the Fuzzy PID controller itself in terms of stabilizing the load frequency on the Micro-Hydro Power Plant. [ABSTRACT FROM AUTHOR]

The primary objective of this paper is to design and assess the performance of conventional Proportional Integral Derivative (PID), Internal Model Controller (IMC), and IMCbased PID controllers tailored for Hydro Turbine Power Systems (HTPS) exhibiting Non-Minimum Phase (NMP) dynamics. The focus is on overcoming the limitations of existing approaches in handling such complex system dynamics. Existing literature underscores the difficulty of crafting controllers for such systems. The current study represents a sincere endeavour to design and evaluate the performance of conventional Proportional Integral and Derivative (PID), Internal Model Controller (IMC), and IMCbased PID controllers tailored for HTPS characterized by NMP behaviour. The design case study and simulations were conducted using MATLAB and Simulink. The closed-loop responses of HTPS with PID, IMC, and IMC-PID are presented, and the controller performances are scrutinized in both time and frequency domains. To validate the effectiveness of the controllers, performance indices such as Integrated Squared Error (ISE), Integrated Absolute Error (IAE), Integrated Time-weighted Absolute Error (ITAE), Integrated Time Squared Error (ITSE) are calculated, as well as control efforts are calculated using 2-norm and infinity-norms. These performance indices and control effort norms offer a comprehensive evaluation of the controllers' performance in terms of minimizing error, handling system dynamics, and optimizing control effort across different time scales. Analysing these metrics aids in selecting and refining controllers for optimal performance in HTPS with NMP behaviour. Our findings illustrate that IMCbased PID controllers exhibit superior performance compared to conventional PID controllers in effectively handling the Non- Minimum Phase (NMP) dynamics of Hydro Turbine Power Systems (HTPS). This superiority is substantiated by enhanced performance indices, including reductions in ISE, IAE, ITSE, and ITAE. [ABSTRACT FROM AUTHOR]

Copyright of SDU Journal of Engineering Sciences & Design / Mühendislik Bilimleri ve Tasarım Dergisi is the property of Journal of Engineering Sciences & Design 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.)

Aiming at the problems of poor size adjustability and low joint tracking accuracy of lower limb exoskeleton rehabilitation robot (LLERR), a variable size lower limb exoskeleton rehabilitation robot (VSLLERR) was designed by UG NX software based on human body size data. Then, the kinematics model of VSLLERR was established by DH method, and the motion space of VSLLERR was analyzed. In addition, the dynamics model of VSLLERR was established by Lagrangian energy method, and the general nonlinear friction model was designed to modify and improve the accuracy of dynamics model. Then, the PID and reaching law (RL) controllers of VSLLERR were designed by SIMULINK. Furthermore, the joint tracking accuracy of the two controllers and the influence of RL controller parameters on tracking accuracy were studied by simulation experiment. The results indicate that the joint angle and joint angular velocity tracking accuracy of RL controller are higher than that of PID controller. In addition, appropriate parameters (c1, c2, k, ε, ϕ) can improve the tracking accuracy of VSLLERR. [ABSTRACT FROM AUTHOR]

This paper proposes an improved actuator system model for UAV elevators using multibody dynamics simulation. The multibody dynamics simulation employs the Simscape Multibody, module in MATLAB coupled with Simulink to model the servo and hinge moment calculation. The actuator system comprises an electrical servo and mechanical components, including arms, push rods, horns, and the elevator. The electrical servo is modeled using a PID controller and a simplified motor model. The multibody dynamics simulation is employed to capture the dynamics of the mechanical components, coupled with the electrical servo through torque delivery to the mechanical components. The simulation is applied to the elevator of a medium altitude long endurance (MALE) UAV with a Maximum Take Off Weight of 1300 Kg. Generating these quantities provide a benefit in capturing the operational envelope of the servo to be compared to its limitations. Given the features of this simulation, it is proposed to extend the research by integrating this method with flight dynamics simulation. [ABSTRACT FROM AUTHOR]

Given the challenges associated with accurately modeling complex nonlinear systems with time delays in industrial processes, this paper introduces an advanced model-free control algorithm that combines data-driven and model-driven approaches. Initially, an enhanced algorithm for multi-innovation model-free control, incorporating error feedback, is presented based on the error feedback principle. Subsequently, a novel control strategy is introduced by delving into PID neural network (NN) recognition and control theory, merging PID NN control with multi-innovation feedback control. Through meticulous mathematical derivation, the proposed strategy is proven to ensure system stability. Compared with traditional NN PID controllers, the convergence rate of the proposed scheme is 50 s faster and the steady-state errors are limited to ±1. [ABSTRACT FROM AUTHOR]

In this study, a novel planning and tracking approach is proposed for a mobile robotic arm to grab objects in an obstacle environment. First, we developed an improved APF-RRT* algorithm for the motion planning of a mobile robotic arm. This algorithm optimizes the selection of random tree nodes and smoothing the path. The invalid branch and the planning time are decreased by the artificial potential field, which is determined by the specific characteristics of obstacles. Second, a Fuzzy-DDPG-PID controller is established for the mobile robotic arm to track the planned path. The parameters of the PID controller are set using the new DDPG algorithm, which integrated FNN. The response speed and control accuracy of the controller are enhanced. The error and time of tracking of the mobile robotic arm are decreased. The experiment results verify that the proposed approach has good planning and tracking results, high speed and accuracy, and strong robustness. To avoid the occasionality of the experiments and fully illustrate the effectiveness and generality of the proposed approach, the experiments are repeated multiple times. The experiment results demonstrate the effectiveness of the proposed approach. It outperforms existing planning and tracking approaches. [ABSTRACT FROM AUTHOR]

The three-phase induction machine that is used has a constant speed in accordance with the factory capacity without being able to adjust the speed. The inverter is a motor speed control device by changing the frequency value that enters the motor. Setting the frequency value is intended to obtain the desired rotation speed or as needed. But the inverter itself needs to be added with a controller so that the control value of the motor can be adjusted according to the desired needs. PID controller is a controller that is most widely used in the field of control systems in the industrial world. Combining the three PID constants in parallel can cover every disadvantage and strength of each constant so that a good controller is obtained. Based on the results of the experiments that have been carried out, it can be concluded that the speed of the three-phase induction motor can work well. When tested by giving values of Kp = 02, Ki = 0 and Kd = 3.7, it was found that the motor speed response had a Time Constant = 1.5 seconds, Rise time = 2.2 seconds and Settling time = 2.7 seconds. [ABSTRACT FROM AUTHOR]

Capitalizing on the strides in artificial intelligence and the escalating demand for safer and more efficient traffic systems, the investigation unveils a trio of evolutionary algorithms - namely Grey Wolf Optimizer (GWO), Multi-Verse Optimizer (MVO) and Salp Swarm Algorithm (SSA) - in the context of hyperparameter calibration for the Proportional-Integral-Derivative (PID) controller. Revered for their classical simplicity and widespread industrial use, PID controllers are pivotal in feedback control systems, ensuring desired system performance through meticulous parameter adjustments. This research introduces a novel application of GWO, MVO, and SSA in the realm of PID control, aiming to optimize the controllers' parameters. To exemplify the utility of the proposed algorithms, two distinct trajectory scenarios are employed as target trajectories. Rigorous numerical evaluations, accompanied by graphical analyses, showcase the prowess of these algorithms in steering the trajectory tracking process. By pioneering the application of these optimizers in the PID controller domain, this investigation not only demonstrates their superior performance over traditional methods but also contributes to the broader field of control engineering by suggesting a more efficient approach to traffic system optimization. This exploration also paves the way for further research into leveraging advanced optimization techniques to elevate the safety and efficiency of traffic systems. [ABSTRACT FROM AUTHOR]

In this study, we propose a new approach to selecting PID controller parameters for a UAV tricopter during programmed flights. The approach uses optimization techniques to determine the optimal values of the PID controller parameters based on the desired performance of the UAV. The proposed method is particularly effective for repeated programmed flights in which the UAV follows a defined flight path. The use of a PID control provides a reliable and robust control system that can cope with various disturbances and uncertainties in UAV dynamics. The proposed method provides an alternative to adaptive control, which requires a significant amount of system identification and parameter tuning. The effectiveness of the proposed method has been verified in simulation studies, and the results show its ability to achieve satisfactory performance with low tracking error and fast response time. The result of the work is an improvement in control for a specific object at a specific mission. We present how the recipients can perform this procedure for their object and their mission to be able to improve the control gain in the physical controller. [ABSTRACT FROM AUTHOR]

In order to address the issue of drill string stick–slip vibration, which leads to drill bit wear and reduces the drilling velocity, we conducted a study on the characteristics of stick–slip vibration using a proportional-integral-derivative (PID) controller. By applying the principles of rigid body mechanics, we established a two-degree-of-freedom torsional dynamics equation and derived the first-order differential dynamics equation for the drill string. Subsequently, we designed a PID controller and obtained an equation for the control of stick–slip vibration. The research findings indicate that variations in the difference between the static and dynamic friction coefficients directly impact the nature of the limit cycles in the phase plane. As this difference decreases, the limit cycle narrows and the stick–slip vibrations weaken progressively. When the static and dynamic friction coefficients are equalized, no stick–slip vibrations occur within the drill string. The implementation of PID control effectively manages stick–slip vibrations in the drill string, with greater efficiency observed in controlling the turntable velocity compared to the drill bit velocity. This research provides valuable insights for the development of control strategies aimed at mitigating stick–slip vibrations in drilling engineering applications, thereby facilitating the efficient and safe extraction of oil and gas resources. [ABSTRACT FROM AUTHOR]

Tuning a nonlinear inverted pendulum is a complex and uncertain optimization problem. In this paper, we develop two new GWO variants by introducing a DLH (Dimension Learning-based Hunting) module and new formulas to enhance the exploitation/exploration ratio aiming to avoid local minima. A statistical analysis is carried out to compare the two proposed approaches with five GWO variants. After that, they are used to tune a PID and FSMC controller. The obtained results are promising even when compared to other approaches. [ABSTRACT FROM AUTHOR]

Over the past few years, many electrical vehicle manufacturers have focused on developing enhanced controller efficiency for Four-Wheel Drive (FWD) systems. The control of the speed and direction of the FWD is crucial for safe and efficient operation, particularly in challenging maneuvers. The FWD system movements in straight routes and during maneuvers, turning all four wheels right and left, have not been well covered. Therefore, a robust control design that is capable of controlling the FWD system at an optimal time of operation is highly required. In this research, a Finite Time Control (FTC) is designed, implemented, and simulated to improve the robustness and performance of the FWD system during challenging maneuvers. The proposed FTC controls both the speed and direction of all wheels of FWD according to the route situations. The proposed FTC is compared with an FWD system that is controlled by a traditional Proportional, Integral, and Derivative (PID) controller during straight moving and maneuvers. The comparison is based on controlling parameters such as settling time, maximum overshoot, and speed error values. The results showed that the proposed FTC has a much faster settling time, significantly less maximum overshoot, and much lower error values than the PID controller. These factors are considered the main features of the contribution of any controller system that aims for optimal and robustness and FTC proved to have them adequately. [ABSTRACT FROM AUTHOR]

The main issue in electrical system is providing a stable voltage values in order to obtain best devices performance for this reason controlling the automatic voltage regulation (AVR) system becoma more helpful to achieve this requirments, in this paper three controllers are suggested for maintaining the terminal voltage level value of the generator part in AVR system that supplied to custumers, these controllers are (Conventional PID, ArcTan PID and Nonlinear PID), all gains of these controllers is tuned by using an intelligent sun flower optimization (SFO) algorithm. The objective of the design is finding suitable values of these gains that give a stable response based on minimizing error value and testing it using the Integral Time Absolute Error (ITAE) fitness function, the numerical results exhibited that the ArcTan PID controller give the best results values with a lower settling time (0.698) and its faster than conventional PID 5.034 % and faster than nonlinear PID by 5.163% for 5 second simulation time also its reach its peak value in 0.513 sec. and an expectable overshoot value equal to 0.513 with a small error value (0.000645) at its steady state case and then small error value when working at normal state without any disturbance or any uncertainty cases applied but when a disturbances signal is applied with a value equal to ± 0.3 to the system, the NLPID and the conventional PID presents a best response by returning the system to its desired value in just 2 second in the two cases applied and when change the gain values of two parts of AVR system(amplifier & sensor) the NLPID behaves as a the more suitable one and give a superior robust performance in facing this unwanted signals and then a desired response is achieved after small period time with satisfied and acceptable values for woring in this enviroment. [ABSTRACT FROM AUTHOR]

The traditional air-assisted side-deep fertilization device has some problems, such as inaccurate control system parameters and poor precision in variable fertilization. It seriously affects the application and popularization of the device. Aiming at the above problems, this paper wanted to realize the precise fertilizer discharge control of an air-assisted side-deep fertilization device. This paper designs an electronically controlled fertilization system based on a PID controller from the past. The system model was constructed in MATLAB, and the mathematical model and transfer function model of a stepper motor, the mathematical model of fertilizer discharge, and the stepper motor rotational speed were established too. In order to improve the accuracy of precise fertilizer discharge control system parameters, the system parameters were optimized based on the particle swarm optimization algorithm and the control system tuner toolbox. We had established a validation test platform to test the performance of a precise fertilizer discharge control system. In the actual experiment, the maximum stability coefficient of variation was 0.91% at the target fertilizer discharge mass level of 350 g/min, and the maximum error of fertilizer discharge was 4.14% at 550 g/min of the target fertilizer discharge mass level. By analyzing the test results of the precise fertilizer discharge control system, the new precise fertilizer discharge control system had good fertilizer discharge stability and could also meet the technical specification for quality evaluation of fertilization machinery (NY/T 1003-2006). This research can improve the fertilizer discharge accuracy of the air-assisted side-deep fertilization control system. [ABSTRACT FROM AUTHOR]

This article represents and compares two control systems for a vertical takeoff and landing (VTOL) unmanned aerial vehicle (UAV): a sliding proportional–integral–derivative (PID) controller and an adaptive L1 controller. The goal is to design a high-performing and stable control system for a specific VTOL drone. The mathematical model of the unique VTOL drone is presented as a control object. The sliding PID and adaptive L1 controllers are then developed and simulated, and their performance is compared. Simulation results demonstrate that both control systems achieve stable and accurate flight of the VTOL drone, but the adaptive L1 controller outperforms the sliding PID controller in terms of robustness and adaptation to changing conditions. This research contributes to ongoing work on adaptive control systems for VTOL UAVs and highlights the potential benefits of using L1 adaptive control for this application. [ABSTRACT FROM AUTHOR]

Suspension systems are critical parts of modern cars. In this study, a radial basis function neural networks-based adaptive PID optimal method is presented for vehicle suspension systems. To avoid the shortcoming that the parameters of PID control are determined by experience in the traditional method, to avoid the local optimality problem and the slow rate of convergence in the modern intelligence method, radial basis function neural networks are applied in this paper. First, a quarter-car suspension is presented. Then, the radial basis function neural networks are employed to obtain the parameters of proportional, integral, and derivate components that are used in PID control. The simulation is conducted later. Next, a comparison of the progress between uncontrolled suspension, the radial basis function-based PID control, the H ∞ control method, and the FPM control method is presented. According to the simulation results, the proposed control method performs better than the others. This contrast reveals the superior characteristics of the suggested control strategy. [ABSTRACT FROM AUTHOR]

The temperature system of the Continuous Stirred Tank Reactor (CSTR) has the characteristics of strong nonlinearity and uncertain parameters. The linear PID controller makes it difficult to meet CSTR's control requirements. Nonlinear PID (NPID) can improve the control effect of nonlinear controlled objects, but due to the influence of nonlinear function selection and manual parameter setting, when parameters are uncertain or subject to external interference, the control performance of the system will decrease. To improve the adaptive capability of the NPID controller, the RBF-NPID control algorithm is proposed. The learning ability of RBF neural network is used to adjust NPID parameters online to improve the control performance of the system. In order to verify the effectiveness of the proposed algorithm, a CSTR model was established in MATLAB and algorithm comparison research was carried out. Simulation results show the effectiveness and superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper exploits the design of a decentralized proportional integral derivative (PID) controller based on nonlinear optimization for a continuously stirred tank reactor system. The basic objective is to attain the design specifications by maintaining both the temperature and concentration. The continuously stirred tank reactor is modeled to a first-order plus dead time system by designing a decoupler. The proposed PID controller is designed on the basis of fundamentals of nonlinear optimization. Further, the overshoot is bounded with constraints on the maximum closed-loop amplitude ratio. The control algorithm is designed for decoupled systems to reduce the loop interactions and attain the servo response. The robust stability is analyzed by considering multiplicative input as well as output uncertainties, while stability is verified with the Kharitonov–Hurwitz theorem. A concise comparison is made between the proposed technique with existing methods. It is envisaged that the proposed control algorithm exhibits better servo and regulatory responses compared to the existing techniques. Furthermore, the efficacious nature of the proposed control scheme is validated by considering a wide range of closed-loop amplitude ratios. [ABSTRACT FROM AUTHOR]

This study addresses the critical need for efficient room monitoring and air conditioning systems, particularly in educational settings like the STMIK STIKOM Indonesia campus. The paper introduces a novel approach that combines ESP-12E based sensors with Proportional-Integral-Derivative (PID) control automation to optimize air conditioning efficiency. Utilizing an ESP-12E microcontroller, the study designed and implemented a room monitoring tool equipped with DHT22 and BH1750 sensors for accurate measurement of temperature, humidity, and light intensity. We also explores the integration of a PID control system into an existing air conditioning (AC) unit. The PID controller was fine-tuned to maintain a stable indoor temperature of 25oCelsius, even when subjected to external heat loads, such as ten LED lamps. The effectiveness of this system was quantified through real-time monitoring of temperature, humidity, and energy consumption, both pre- and postimplementation. Results indicated a rapid and stable response from the PID controller, achieving an amplitude of 1 within 0.08 seconds, thereby confirming its successful tuning and adaptability. We found that this study has broader implications for enhancing energy efficiency and creating conducive learning environments. However, it is worth noting that the research was conducted under specific conditions, and further studies could explore its applicability in different settings. [ABSTRACT FROM AUTHOR]

The ant colony algorithm (ACA) is a heuristic algorithm that resolves the optimality problem by simulating an ant's foraging process, which finds the shortest path. The connotation of the ACA is to find the optimal solution. The Proportional Integral Derivative (PID) parameter tuning is an essential tool in the control field and includes three parameters, Kp, Ki, and Kd, to achieve the best control effect. Besides, tuning the PID parameters is closely related to finding the "optimal" solution that can be attained based on the feasible combination of the two. This article transforms the PID parameter tuning problem into an ACA that finds the optimal solution called ACA-based PID parameters tuning. Furthermore, PID control is simulated by setting the parameters of ACA, such as ant colony size, iteration times, nodes, paths, path evaluation criteria, pheromone concentration, heuristic function, weight factor, and decision function. Eventually, the two PID controller parameter tuning strategies are compared and analyzed, and the advantages and disadvantages of each are obtained. Compared with the 4:1 attenuation curve method, the proposed method can significantly reduce the MP score of the overshoot of the system, increase the time, and improve the dynamic and steady-state performance of the system, but reduce the steady-state error of the system. Therefore, the feasibility and effectiveness of the proposed method is verified. [ABSTRACT FROM AUTHOR]

Introduction: This paper proposes an optimal active vibration control system to control an active tuned mass damper (ATMD), which can help reduce the seismic structural damages caused by the earthquake effects. Methods: The proposed controller exploits the features of the fractional-order calculus to improve the performances of the derivative and the integral actions of the conventional PID controller, the parameters gains ( K p , K i , K d ), λ and μ of the fractional-order PID controller (FO-PID) are selected optimally using the recent artificial hummingbird algorithm (AHA), A comparative study conducted against the classical PID controller in different earthquake excitations, to highlight the advantages of proposed FO-PID. Finally, A new graphical user interface (GUI) based on MATLAB called "SVCS Structural Vibration Control Simulator" is developed to facilitate the simulation of different vibration control systems using different earthquake excitations. Conclusion: The results obtained reflect that using FO-PID controller improves the performance of active control and can reduce further the structural response. [ABSTRACT FROM AUTHOR]

Featured Application: An implementation of PID controller optimized with PSO to improve the road holding and ride comfort simultaneously on a real suspension test setup was proposed. The manuscript contains valuable experimental results for suspension system controller researchers. This study presents an implementation of a proportional–integral–derivative (PID) controller utilizing particle swarm optimization (PSO) to enhance the compromise on road holding and ride comfort of a quarter car semi-active suspension system (SASS) through simulation and experimental study. The proposed controller is verified with a processor-in-the-loop (PIL) approach before real-time suspension tests. Using experimental data, the magnetorheological damper (MR) is modeled by an artificial neural network (ANN). A series of experiments are applied to the system for three distinct bump disturbances. The algorithm performance is evaluated by various key metrics, such as suspension deflection, sprung mass displacement, and sprung mass acceleration for simulation. The phase plane method is used to prove the stability of the system. The experimental results reveal that the proposed controller for the SASS significantly improves road holding and ride comfort simultaneously. [ABSTRACT FROM AUTHOR]

The Fluid Catalytic Cracking Unit (FCCU) serves as the lifeblood of a modern refinery, playing a crucial role in ensuring superior product quality. This paper delves into the design of a nonlinear Proportional-Integral-Derivative (PID) control mechanism aimed at regulating the temperature of both the reactor and the regenerator within the FCCU. A total of four nonlinear PID controllers have been meticulously designed, two each for controlling the temperature of the reactor and the regenerator. Utilizing the cross-coupling method, these controllers are proficient in compensating for the effects of cross-coupling and the nonlinearities inherent in the model. The Firefly algorithm, renowned for its capability in finding optimal solutions to intricate optimization problems, has been employed for tuning the parameters of the controllers. The outcomes discussed in this paper underscore the transient responses of the reactor temperature, featuring an overshoot of 0.239% and a rise time of 24.87631 sec. For the regenerator temperature, the overshoot stands at 0.703% with a rise time of 25.636 sec. These results highlight significant improvements in product quality, achieved by enhancing and closely monitoring the temperatures of both the reactor and the regenerator within the FCCU--an aspect of paramount importance in the oil industry. [ABSTRACT FROM AUTHOR]

This paper proposes a controller to track the maximum power point (MPP) of a photovoltaic (PV) system using a fractional-order proportional integral derivative (FOPID) controller. The employed MPPT is operated based on a dp/dv feedback approach. The designed FOPID-MPPT method includes a differentiator of order (μ) and integrator of order (λ), meaning it is an extension of the conventional PID controller. FOPID has more flexibility and achieves dynamical tuning, which leads to an efficient control system. The contribution of our paper lies is optimizing FOPID-MPPT parameters using Aquila optimizer (AO). The obtained results with the proposed AO-based FOPID-MPPT are contrasted with those acquired with moth flame optimizer (MFO). The performance of our FOPID-MPPT controller with the conventional technique perturb and observe (P&O) and the classical PID controller is analyzed. In addition, a robustness test is used to assess the performance of the FOPID-MPPT controller under load variations, providing valuable insights into its practical applicability and robustness. The simulation results clearly prove the superiority and high performance of the proposed control system to track the MPP of PV systems. [ABSTRACT FROM AUTHOR]

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Managing industrial processes in real-time is challenging due to the nonlinearity and sensitivity of these processes. This unpredictability can cause delays in the regulation of these processes. The PID controller family is commonly used in these situations, but their performance is inadequate in systems and surroundings with varying set-points, longer dead times, external noises, and disturbances. Therefore, this research has developed a novel controller structure for PIDD α that incorporates the second derivative term from PIDD 2 while exclusively using fractional order parameters for the second derivative term. The controllers' robust performance has been evaluated on four simulation plants: first order, second order with time delay, third-order magnetic levitation systems, and fourth-order automatic voltage regulation systems. The controllers' performance has also been evaluated on experimental models of pressure and flow processes. The proposed controller exhibits the least overshoot among all the systems tested. The overshoot for the first-order systems is 9.63%, for the third-order magnetic levitation system, it is 12.82%, and for the fourth-order automatic voltage regulation system, it is only 0.19%. In the pressure process plant, the overshoot is only 4.83%. All controllers for the second-order systems have a time delay, while the flow process plant has no overshoot. The proposed controller demonstrates superior settling times in various systems. For first-order systems, the settling time is 14.26 s, while in the pressure process plant, the settling time is 8.9543 s. Similarly, the proposed controllers for the second-order system with a time delay and the flow process plant have the same settling time of 46.0495 s. In addition, the proposed controller results in the lowest rise time for three different systems. The rise time is only 0.0075 s for the third-order magnetic levitation system, while the fourth-order automatic voltage regulation system has a rise time of 0.0232 s. Finally, for the flow process plant, the proposed controller has the least rise time of 25.7819 s. Thus, in all the cases, the proposed controller results in a more robust controller structure that provides the desired performance of a regular PIDD 2 controller, offering better dynamic responses, shorter settling times, faster rise times, and reduced overshoot. Based on the analysis, it is evident that PIDD α outperforms both PID and FOPID control techniques due to its ability to produce a more robust control signal. [ABSTRACT FROM AUTHOR]

The paper explains a control method for turntable by feedback a PID controller with the traditional model reference adaptive controller (MRAC) based on the MIT rule. The traditional MRAC is designed for a first-order system with the adjustment of a two-variable parameter. However, the majority of plants, including turntable, are second-order systems. Traditional MRAC tracking performance for second-order systems is unsatisfactory. The control law for the second order system along with extension from the first to the second order of MRAC is derived. The modified MRAC i.e., MRAC-PID controller is designed for the application of turntable. It enhanced the system's dynamic performance. To assess the performance of the proposed controller, MATLAB/Simulink software was used. The article incorporates a detailed analysis and comparison of PID, MRAC as well as MRAC-PID controllers based on the MIT rule for the turntable system. The robustness of the proposed controller is validated by introducing uncertainties in two aspects i.e., mistuning of the controller gains and turntable system dynamics change. The probabilistic design assessment for the mistuning is carried out through levels of uncertainty in controller gain. It is observed that PID and MRAC would track the reference model but modified MRAC has better performance in terms of tracking accuracy, adaptability, and rapidity. Several performance indexes such as integral absolute error (IAE), integral time absolute error (ITAE), and integral square error (ISE) were employed to justify the proposed controller superiority. [ABSTRACT FROM AUTHOR]