Proportional-integral-derivative Parameters Research Articles

Revising the Motion Control Parameter Optimization Research of a Two-Wheel Differential Car

This paper proposes a solution based on the particle swarm optimization algorithm to address the issue of Proportional Integral Derivative parameter selection in the motion control of a two-wheel differential car. The mathematical motion model is established based on the driving principle of a two-wheel differential car. The transfer function of the DC motor is derived in detail, based on Kirchhoff’s law and the Laplace transform. The pose renewal equation and error renewal equation of the car are based on the mathematical motion model. Finally, a numerical simulation and experimental analysis were conducted using MATLAB R2022a, Simulink 9.1 (part of R2018a), VOFA 1.3.10 software, an STM32 microcontroller, an L298N driver chip, and other hardware components. The results indicate that the particle swarm optimization algorithm enables the rapid acquisition of optimal Proportional Integral Derivative parameters. The optimized parameter of the motor speed convergence time is set to 10 ms, with an overshoot of 1 r/min and an enhanced anti-interference ability. The optimized parameters effectively regulate the car’s motion, ensuring a maximum error control of approximately 0.003 m.

Oscillation Suppression Method of Digital Proportional Valve Based on Fuzzy Intelligent PID Control

A digital proportional valve is constituted by the main spool and a high-speed on/off valve bridge acting as the pilot stage. However, the main spool will generate oscillation during movement under the control of the pilot stage. This results in poor stability, slow response speed, low control accuracy, and even the potential loss of control of the valve. To tackle this issue, an oscillation suppression method based on fuzzy intelligent proportional-integral-derivative (PID) control is put forward. The movement state of the main spool is determined in accordance with its movement position and velocity. Thereafter, the fuzzy control parameters of the controller are calculated on the basis of the determined movement state of the main spool. Different PID parameters are adopted to eliminate the control effect difference caused by the structural asymmetry of the two pilot control chambers of the main valve. The performance and robustness of the proposed control method are verified by comparison with the PID controller based on full-bridge and half-bridge control. The results demonstrate that the proposed control method can effectively suppress the oscillation of the main spool of the digital proportional valve, improve the control accuracy, and reduce the response time. When the excitation signal takes the form of a step signal, the overshoot of the control method put forward in this paper is diminished by 26.2% in comparison with that of the PID control. Under stable operating conditions, the maximum tracking error is less than 3.1%. Moreover, compared with simply using the PID control method, this error is reduced by 41%.

Application of Proportional Integral Derivative (PID) Control in Overvoltage Protection Systems for Low Voltage Network Loads

Electricity is a very essential need for every element of society in this modern era, with electricity, various kinds of work can be easily done. However, in electricity distribution, there are often disturbances that can be detrimental to consumers, One of the power lines that usually experiences interference is the Low Voltage Network (JTR) line. JTR is a transmission network with a low voltage classification between 220 volts and 280 volts. In this transmission, various kinds of disturbances often occur, such as overvoltage and voltage drops. In this research, the author discusses the overvoltage protection system using Proportional Integral Derivative (PID) control. PID control is a simple control method that several researchers have developed to overcome various electrical problems in this modern era. For this reason, the researcher will develop PID as a protection system to overcome overvoltage disturbances in JTR transmissions. The appropriate PID parameter value to overcome overvoltage in this study is the value Kp = 5; Ki = 0,3 Kd = 0.01, where the system is able to protect the overvoltage according to the setpoint value 220 volts.

Optimal tuning of PID controller for V/f control of linear induction motor using artificial biological intelligence

In order to improve the performance of a Proportional-Integral-Derivative (PID) controller used in the control of Linear Induction Motor (LIM) V/f speed, this research presents a bio-inspired meta-heuristic soft computing approach. A PID controller specifically designed for the LIM system is described in detail, with a focus on how to optimize the controller using an evolutionary strategy that makes use of the Nutcracker Optimizer. Settling time, rise time, maximum overshoot, and ITAE (Integral-Time Absolute Error) are examples of transient response specifications that are achieved in MATLAB/Simulink by using a step input to the LIM and the optimal set of PID parameters chosen from the optimization. In order to determine the most effective technique for obtaining the optimum response in LIM, these outcomes are then contrasted with outcomes from other soft computing techniques.

Application of an Improved State Feedback Control in the Selective Catalytic Reduction Denitrification Systems of Coal Power Units Under Variable Load Conditions

Selective catalytic reduction (SCR) flue gas denitrification systems are inherently complex, typically embodying characteristics of non-linearity, significant time delays, and susceptibility to multiple disturbances. In the context of coal power units engaging in deep load cycling and rapid frequency adjustment, conventional proportional-integral-derivative (PID) control struggles to meet the demands of effective control. This study introduces a control strategy that incorporates a “state observer + Linear Quadratic Regulator (LQR) state feedback + Improved Quantum Genetic Algorithm (IQGA) optimized PID”. Initially, local linear mathematical models of an SCR denitrification system at 340 MW, 450 MW, and 540 MW loads were used to design state observer and LQR state feedback control parameters for each operational condition. At a single load point, the IQGA was employed to optimize the outer loop PID parameters, followed by simulation experiments of load increases and decreases between 340 MW and 540 MW. The results demonstrated that, compared to two other strategies, the proposed approach reduced the overshoot by a minimum of 1.5% and shortened the adjustment time by 31.7% under conditions of step disturbances and internal perturbations. Throughout variable operational conditions, the strategy consistently exhibited minimal output fluctuations, rapid adjustment capabilities, strong disturbance rejection, and robust stability. This algorithm proves to be an effective method for controlling NOx concentrations, offering insights for precise ammonia injection control in future applications.

PID control algorithm based on multistrategy enhanced dung beetle optimizer and back propagation neural network for DC motor control.

Traditional Proportional-Integral-Derivative (PID) control systems often encounter challenges related to nonlinearity and time-variability. Original dung beetle optimizer (DBO) offers fast convergence and strong local exploitation capabilities. However, they are limited by poor exploration capabilities, imbalance between exploration and exploitation phases, and insufficient precision in global search. This paper proposes a novel adaptive PID control algorithm based on enhanced dung beetle optimizer (EDBO) and back propagation neural network (BPNN). Firstly, the diversity of exploration is increased by incorporating a merit-oriented mechanism into the rolling behavior. Then, a sine learning factor is introduced to balance the global exploration and local exploitation capabilities. Additionally, a dynamic spiral search strategy and adaptive [Formula: see text]-distribution disturbance are presented to enhance search precision and global search capability. The BPNN is employed to fine-tune both PID and network parameters, leveraging its powerful generalization and learning ability to model nonlinear system dynamics. In the simplified motor experiments, the proposed controller achieved the lowest overshoot (0.5%) and the shortest response time (0.012s), with a settling time of 0.02s and a steady-state error of just 0.0010. In another set of experiments, the proposed controller recorded an overshoot and response time of 0.7% and 0.0010s, across five DC motor tests. These results demonstrate the proposed adaptive PID control algorithm has superior performance in optimizing control system parameters, as well as improving system robustness and stability.

Robust ELM-PID tracing control on autonomous mobile robot via transformer-based pavement crack segmentation

Pavement crack tracing is paramount to missions encompassing automated crack sealing for road maintenance. However, existing methods still face several challenges, including the incapability to precisely extract crack trajectories and the challenge of tuning control parameters within intricate backgrounds. To address these limitations, the ViT-S2T network and the ELM-PID control system are proposed for crack tracing. Specifically, the ViT-S2T consists of two branches. The transformer-based feature extraction module (TFEM) integrates multi-head attention mechanism and multi-layer perceptron to capture global contextual crack semantic features. The incoherent segmentation masks (ISM) employs a binary classifier to predict the coarsest irrelevant mask and further performs up-sampling fusion of higher-resolution features. Moreover, the Neural-PID control method is designed to track crack trajectories, combining Extreme Learning Machines (ELM) and Proportional Integral Derivative (PID). The ELM-PID controller utilizes a three-layer backpropagation neural network and proposes the ELM model for adaptive adjustment by predicting the tuning parameters of PID. This framework is applied to real-time visual tracing for edge AI. Extensive tests performed on three arduous datasets of DeepCrack, CFD, and S2TCrack, achieving a precision of 82.76% and mAP@0.5 of 75.63% and speed of 0.0513 m/s, demonstrating the superior and robust nature of our approach in pavement crack tracing.

Enhancing DC Motor Speed Control Performance Using Heuristic Optimization and Comparative Analysis of Control Methods

Direct Current (DC) motors are an important component that converts electrical energy into mechanical energy, used in a wide range of applications from industrial applications to home appliances. DC motor speed control has an important role in industrial processes to increase efficiency, realize precise movements and optimize energy consumption. In this study, various control methods and parameter optimization techniques for speed control of DC motors, which have a wide range of applications, have been systematically analyzed. The aim of the study is to develop an effective control strategy to ensure that DC motors reach the determined target speed by monitoring them in real time at different speeds and to minimize fluctuations caused by variable loads or external factors. In our study, Proportional-Integral-Derivative (PID), Proportional-Integral (PI), and Proportional-Derivative (PD) control methods were used. The parameters of these controllers were tuned using Matlab Tuned, The Cheetah Optimizer (CO) Algorithm, a new generation heuristic optimization method, and Particle Swarm Optimization (PSO), a widely accepted optimization method. The performances of the controllers were determined using criteria such as Integral of Absolute Error (IAE), Integral Squared Error (ISE), and Integral of Time multiplied by Absolute Error (ITAE). According to the results obtained, it was found that the PID, PI and PD control parameters determined using the CO Algorithm performed better than the controllers created using Matlab Tuned and PSO methods. New optimization methods, such as the CO Algorithm, have been found to have significant potential to improve the performance of control systems. Thanks to this study, it offers a practical approach for optimizing DC motor speed control in industrial processes. As a result, it has been found that the control parameters determined by the CO Algorithm have significant potential in improving the performance of DC motor speed control and control systems compared to other optimization methods.

Adaptive multimodal control of trans-media vehicle based on deep reinforcement learning

To solve the problem that the control system is prone to instability due to the sudden change of physical characteristics, strong interference, and nonlinear in the process of multimodal movement of trans-media vehicle, an adaptive control method combining the Deep Deterministic Policy Gradient (DDPG) and traditional Proportional-Integral-Derivative (PID) controller is proposed in this paper. In this approach, the upper-level DDPG controller continuously monitors the vehicle's state and environmental conditions, dynamically adjusting the PID parameters in real-time. The lower-level PID controller then utilizes these updated parameters to modulate the output thrust of the vehicle's motors, thereby achieving excellent control over the vehicle's entire movement. Firstly, according to the hydrodynamic analysis, the kinematics and dynamics mathematical model of the self-designed trans-media vehicle is constructed. This model includes the multi-stage motion modal process of aerial flight, underwater navigation, and cross-media motion, which is suitable for the simulation and verification of the control method. Then, an adaptive controller called RL-PID combining DDPG and PID is built, so that PID can adjust parameters in real-time according to the changes in the external environment. Finally, after theoretical stability proof, a comparison study is performed across three approaches, namely the novel RL-PID, Fuzzy PID, and PID. The experimental results illustrate the superiority of the proposed approach over the competing ones and the generalization of the proposed approach under different interference.

Research on a Bridge Hybrid Isolation Control System Based on PID Active Control and Genetic Algorithm Optimization

A bridge hybrid isolation system, integrating both active and passive control strategies, is proposed and investigated to enhance seismic performance. The system is modeled as a two-layer structure, with the upper layer subjected to active control via a Proportional–Integral–Derivative (PID) controller, and the lower layer employing a conventional passive base isolation system. The displacement response of the superstructure is minimized by the control forces generated by the PID controller, which also accounts for the reaction forces transmitted to the lower isolation layer. To optimize the controller’s performance, a genetic algorithm is implemented for real-time tuning of the PID parameters. Numerical simulations, conducted using the Newmark method, are employed to assess the influence of active control on the lower isolation system. The results reveal that, while active control increases the peak displacement of the superstructure to some extent, it significantly prolongs the structural period, thus enhancing the system’s overall seismic resilience and stability.

Comprehensive Optimization of PID Controller Parameters for DC Motor Speed Management Using a Modified Jellyfish Search Algorithm

ABSTRACTThe proportional integral derivative (PID) controller plays a crucial role in various industrial applications. However, conventional methods often fail to provide optimal parameter values. Therefore, this study proposes a novel version of the jellyfish search (JS) algorithm, the modified jellyfish search (mJS) algorithm, for optimally tuning PID controllers to regulate the speed of a direct current (DC) motor. The original JS algorithm is known for its nature‐inspired approach; however, it has limitations in terms of exploitation power. To address these issues, the proposed mJS incorporates quasi‐dynamic opposed‐based learning and a Weibull probability distribution. The objective function minimizes the integral of the time‐weighted absolute error (ITAE). The effectiveness of mJS was validated by solving the 23rd classical benchmark function, followed by a comparative analysis with contemporary optimization algorithms using stringent statistical criteria. Then mJS is then applied to optimize the PID parameters of three different DC motor models. Results show that the mJS outperforms the standard JS, gray wolf optimization (GWO), JAYA, and golden jackal optimization (GJO) for various performance indicators. Specifically, the best ITAE values were 3.274e6 for DC motor 1 using the JS algorithm, 0.002737 for DC motor 2 using the GJO algorithm, and 0.02785 for DC motor 3 using the mJS algorithm. Additionally, the mJS and JS algorithms reduced the settling time to 0.005 s for DC motor 1, whereas the best settling time of 0.325 s for DC motor 2 was achieved by the mJS algorithm, and a settling time of 1.19 s was recorded for DC motor 3 using both the mJS and JS algorithms. These findings underscore the practical importance of the developed optimizer in engineering applications and its significant contribution to the literature.

Automatic Landing Control for Fixed-Wing UAV in Longitudinal Channel Based on Deep Reinforcement Learning

The objective is to address the control problem associated with the landing process of unmanned aerial vehicles (UAVs), with a particular focus on fixed-wing UAVs. The Proportional–Integral–Derivative (PID) controller is a widely used control method, which requires the tuning of its parameters to account for the specific characteristics of the landing environment and the potential for external disturbances. In contrast, neural networks can be modeled to operate under given inputs, allowing for a more precise control strategy. In light of these considerations, a control system based on reinforcement learning is put forth, which is integrated with the conventional PID guidance law to facilitate the autonomous landing of fixed-wing UAVs and the automated tuning of PID parameters through the use of a Deep Q-learning Network (DQN). A traditional PID control system is constructed based on a fixed-wing UAV dynamics model, with the flight state being discretized. The landing problem is transformed into a Markov Decision Process (MDP), and the reward function is designed in accordance with the landing conditions and the UAV’s attitude, respectively. The state vectors are fed into the neural network framework, and the optimized PID parameters are output by the reinforcement learning algorithm. The optimal policy is obtained through the training of the network, which enables the automatic adjustment of parameters and the optimization of the traditional PID control system. Furthermore, the efficacy of the control algorithms in actual scenarios is validated through the simulation of UAV state vector perturbations and ideal gliding curves. The results demonstrate that the controller modified by the DQN network exhibits a markedly superior convergence effect and maneuverability compared to the unmodified traditional controller.

Research on constant tension yarn transfer control technology based on AGA-PID and FOC algorithm

To solve the problems of poor real-time tension tracking, large overshoot, long adjustment time, and large tension fluctuation error that exist in the current control system for yarn feeding in the circular weft knitting machine, this paper presents an optimized and improved adaptive genetic algorithm (AGA) for adjusting the PID (Proportional Integral Derivative) parameters of the tension control part of the yarn-conveying system (AGA-PID). The algorithm is combined with the field-oriented control technology of the permanent magnet synchronous motor to design a closed-loop control strategy for the yarn-conveying system of a circular weft knitting machine. The MATLAB-Simulink simulation model was used to analyze the external tension loop-based AGA-PID controller and permanent magnet synchronous motor dual closed-loop control strategy of the yarn transport control system. The experimental verification was conducted by constructing an experimental platform that simulates the real process of yarn transportation. The outcomes of system simulations and experiments have consistently demonstrated that the AGA-PID control algorithm outperforms the GA-PID and PID control algorithms in terms of overshooting, response speed, stability, and anti-jamming ability. The yarn transport control system, which is designed to operate based on an AGA-PID control algorithm, is capable of reducing the fluctuations in tension that occur during the transportation of yarn and exhibits enhanced control accuracy, effectively stabilizing yarn tension control during the conveying process. The application of this algorithm can enhance the overall elasticity uniformity and surface flatness of knitted fabrics, thereby facilitating the realization of adjustable and controllable local elasticity in fabrics.

Optimizing PID controller parameters for robust automatic voltage regulator system through indirect design approach-2

Automatic voltage regulators (AVR) are designed to manipulate a synchronous generator’s voltage level automatically. Proportional integral derivative (PID) controllers are typically used in AVR systems to regulate voltage. Although advanced PID tuning methods have been proposed, the actual voltage response differs from the theoretical predictions due to modeling errors and system uncertainties. This requires continuous fine tuning of the PID parameters. However, manual adjustment of these parameters can compromise the stability and robustness of the AVR system. This study focuses on the online self-tuning of PID controllers called indirect design approach-2 (IDA-2) in AVR systems while preserving robustness. In particular, we indirectly tune the PID controller by shifting the frequency response. The new PID parameters depend on the frequency-shifting constant and the previously optimized PID parameters. Adjusting the frequency-shifting constant modifies all the PID parameters simultaneously, thereby improving the control performance and robustness. We evaluate the robustness of the proposed online PID tuning method by comparing the gain margins (GMs) and phase margins (PMs) with previously optimized PID parameters during parameter uncertainties. The proposed method is further evaluated in terms of disturbance rejection, measurement noise, and frequency response analysis during parameter uncertainty calculations against existing methods. Simulations show that the proposed method significantly improves the robustness of the controller in the AVR system. In summary, online self-tuning enables automated PID parameter adjustment in an AVR system, while maintaining stability and robustness.

FOPDT model and CHR method based control of flywheel energy storage integrated microgrid

The main causes of frequency instability or oscillations in islanded microgrids are unstable load and varying power output from distributed generating units (DGUs). An important challenge for islanded microgrid systems powered by renewable energy is maintaining frequency stability. To address this issue, a proportional integral derivative (PID) controller is designed in this article. Firstly, islanded microgrid model is constructed by incorporating various DGUs and flywheel energy storage system (FESS). Further, considering first order transfer function of FESS and DGUs, a linearized transfer function is obtained. This transfer function is further approximated into first order plus time delay (FOPTD) form to design PID control strategy, which is efficient and easy to analyze. PID parameters are evaluated using the Chien-Hrones-Reswick (CHR) method for set point tracking and load disturbance rejection for 0% and 20% overshoot. The CHR method for load disturbance rejection for 20% overshoot emerges as the preferred choice over other discussed tuning methods. The effectiveness of the discussed method is demonstrated through frequency analysis and transient responses and also validated through real time simulations. Moreover, tabulated data presenting tuning parameters, time domain specifications and comparative frequency plots, support the validity of the proposed tuning method for PID control design of the presented islanded model.

Design and Implementation of Remote Lab PID Controller Experiment Based on IoT

The education sector has increasingly embraced distance education to ensure a safe and uninterrupted learning process, especially in wars or epidemics. This paper focuses on designing and implementing a PID (Proportional-Integral-Derivative) controller experiment in remote laboratories utilizing the Internet of Things (IoT). Also, a laboratory experiment was developed using a naturally unstable system, specifically a cart-inverted pendulum. The experiments aim to enhance students' understanding of PID controller tuning within a real-world context. This system was chosen due to its dual motion characteristics, with a linear motion for the cart and a circular motion for the pendulum. Two controllers were designed and implemented for the system to enable control and feedback. Additionally, the Blynk platform was integrated into the experiment setup, allowing for real-time visualization of the system's response, control of PID parameters, and the ability to view the video stream via platforms like Skype. For remote connectivity, NodeMCU, an IoT development board, controlled the pendulum, collected system parameters, and transmitted them to the cloud through the Internet. Moreover, the sensors and the system were mathematically modeled, and their transfer functions were extracted. That allows the students to do the PID experiment theoretically and practically and compare the results. This complete setup enables local and remote access to the experiment, ensuring students can experiment regardless of their physical location. As a result of the study, designing and implementing a remote laboratory PID controller experiment utilizing IoT technology provides students with an innovative and immersive learning experience.

Metaheuristic tuned decentralized PID controller based active suspension system for railway vehicle

AbstractA high‐speed railway system is one of the sustainable alternatives to other modes of transportation and may connect the most congested urban cities with minimum carbon emissions. However, the vibration intensity increases as the train's operating speed increases, resulting in deteriorated ride comfort and stability. Hence, this article investigates a 27‐degree‐of‐freedom (DOF) dynamic model of railway vehicles with an improved active suspension system. The decentralized control structure performs the controlling action with five optimized Proportional Integral Derivative (PID) controllers that suppress the vehicle body's vertical, lateral, pitch, roll, and yaw motions. Further, to optimize the PID parameters, three metaheuristic optimization techniques, Genetic algorithm (GA), Grey Wolf Optimization (GWO), and Flower Pollination Algorithm (FPA), are utilized, and their simulated results are compared with the passive system as well as other conventional tuning technique. Moreover, the performance of the proposed control strategy is evaluated in the frequency domain under random track irregularities, and the results are characterized in terms of power spectral densities (PSDs). The simulated results show that among the proposed metaheuristic algorithms, FPA outperforms with a significant reduction in vehicle vibration compared to other tuning methods. The percentage reduction of the vertical, lateral, pitch, rolls, and yaw accelerations is 71.4%, 35.1%, 52.8%, 48.1%, and 38.2%, respectively, ensuring enhanced vehicle ride comfort.

Design and multiobjective optimization of a two‐point contact ladder‐climbing robot using a genetic algorithm

AbstractThis 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.

Optimization of the different controller parameters via OBL approaches based artificial ecosystem optimization involving fitness distance balance guiding mechanism for efficient motor speed regulation of DC motor

This study proposes a new optimization approach, which is called as artificial ecosystem optimization algorithm with fitness-distance balance guiding mechanism by using opposite based learning methods (FDBAEO_OBLs) for the speed regulation of direct current (DC) motor. The performance of the proposed FDBAEO_OBL algorithm is tested in two different experimental studies. In the first experimental study, the proposed approach is tested in the CEC2020 benchmark test functions and the FDBAEO algorithm, which included the best OBL approach, is determined using non-parametric Wilcoxon and Friedman statistical analysis methods. Second, the parameters of proportional integral derivative (PID), tilt integral derivative (TID), proportional integral derivative with filter (PIDF), tilt integral derivative with filter (TIDF), fractional-order proportional integral derivative (FOPID), fractional-order proportional integral derivative with filter (FOPIDF), proportional integral derivative with fractional-order filter (PIDFF) and fractional-order proportional integral derivative with fractional-order filter (FOPIDFF) controller structures to be used in DC motor closed loop speed control are determined with FDBAEO_OBL, and the performances of the controllers are investigated. Integral absolute error (IAE), integral time absolute error (ITAE), integral time squared error (ITSE) and integral squared error (ISE) performance indices are used as the objective function of the operation process in which the control parameters are determined. According to the comparative step response results of the controller structures, the four best controller structures for DC motor speed regulation are determined. The performances of these controllers are examined under different simulation conditions and according to the results obtained, it is seen that the best controller structure is FOPIDFF. The FDBAEO_OBL algorithm, which is used in both benchmark test functions and DC motor speed regulation, shows an effective, durable and superior performance in finding the optimal solution values during the optimization.

Simulation and Arduino Hardware Implementation of ACO, PSO, and FPA Optimization Algorithms for Speed Control of a DC Motor

This article proposes implementing and comparing the effectiveness of three optimization algorithms (ACO, PSO, and FPA) for tuning a proportional-integral-derivative (PID) controller on an Arduino Mega 2560 board. This relatively unexplored approach aims to evaluate these algorithms through practical experiments. The choice of PID control is due to its design simplicity and widespread industrial use. Similarly, the permanent magnet DC motor (PMDC) was selected because of its crucial role in various industrial sectors. Tuning PID parameters using optimization algorithms has garnered increasing interest due to its demonstrated efficiency. Several studies have validated the stability of ACO, PSO, and FPA algorithms, justifying their selection. In this article, simulation results showed that ACO, with a response time of 0.322s and an overshoot of 0.68%, was more effective than PSO, which had a response time of 0.768s and an overshoot of 13%. FPA had a response time of 0.347s, close to ACO, but a higher overshoot of 6%. In practice, several factors come into play, such as speed ripples caused by the speed sensor, and machine saturation, which must be considered to ensure practical implementation. After adjusting the PID parameters and integrating a low-pass filter in the feedback loop, ACO, with a response time of 0.596s and an overshoot of 1.68%, was very close to FPA, which had a response time of 0.644s and an overshoot of 0.81%. This comparison highlighted the advantages of the FPA algorithm, which is simple to use, requires fewer parameters to adjust, and takes less time than ACO. This study suggests the potential for implementing a hybrid FPA-ACO algorithm, leveraging the strengths of both algorithms.