Proportional Integral Derivative Controller Research Articles

Development of a PID-Controlled Refrigeration System for Reduced Power Consumption

Refrigerator systems are used for food preservation as well as other applications. However, the cost of running the system is high due to rising fuel and electricity prices. Traditionally, these systems are controlled by On/Off controllers. This study proposes the use of a proportional-integral-derivative (PID) controller algorithm to reduce costs for domestic and industrial refrigeration without negatively affecting the system performance. To accomplish this, a physical model was developed, comprising a domestic refrigerator, microcontroller, and MATLAB computer software for analysis. A mathematical model of second-order lead and second-order lag transfer function was also developed for a typical refrigerator system. The physical model was connected, and open-loop temperature-time response data was collected for system modeling. In addition, Data were collected from industries namely Fan Milk Industry and Benue State University Teaching Hospital Mortuary for a robust system analysis. All data sets were imported into MATLAB's system identification toolbox to estimate model parameters. The ultimate gains, frequency, and period were determined for each feedback closed-loop model, allowing the application of Ziegler-Nichols and Tyreus-Luyben PID tuning settings. The closed-loop models were then simulated in MATLAB to evaluate system performance. Simulation results showed that the Tyreus-Luyben model performed better, and offered better temperature response, less undershoot, and faster settling time than the Zeigler-Nichols method. Both PID models outperformed the traditional On/Off controller, with energy consumption reduced to less than one-third of the conventional method. The study concludes that PID controllers are a better alternative to On/Off systems when properly tuned.

PID-based control strategies for enhancing stability and precision in electro-hydraulic actuation systems

Electro-hydraulic actuation systems are essential in industrial applications but often face challenges related to nonlinearity, leading to instability and reduced precision. This paper explores the application of Proportional-Integral-Derivative (PID) control strategies to enhance the stability and precision of these systems. A detailed system model is developed, and the PID controller is designed and tuned using optimization techniques. Simulation results demonstrate improvements in performance metrics, such as reduced overshoot, settling time, and steady-state error. The proposed control strategy is validated experimentally on a physical setup, confirming its effectiveness in real-world applications. The findings show that PID-based control significantly enhances both stability and precision in electro-hydraulic actuation systems, offering a practical solution for industrial control challenges.

Active Disturbance Rejection Control for Flux Weakening in Interior Permanent Magnet Synchronous Motor Based on Full Speed Range

To address the impact of load disturbances on the full-speed-range control of an interior permanent magnet synchronous motor (PMSM), an active disturbance rejection control (ADRC) method is proposed. The speed loop employs phased field-weakening control (FW) based on ADRC, while the current loop utilizes proportional-integral-derivative (PID) control. Starting from the motor parameters, the Lagrange multiplier method was used to derive the critical speeds for the maximum torque per ampere (MTPA) and maximum torque per voltage (MTPV) ratios, and the timing for the field-weakening control was analyzed. A full-speed-range control model of the motor was established, and an ADRC-based speed loop controller was designed to achieve smooth transitions between high speeds and anti-disturbance solid capabilities. Based on the proposed control strategy, a 21 kW PMSM was used as the research object, and a full-speed-range control simulation model was developed in MATLAB/SIMULINK to verify the strategy. Compared to the traditional PID control, the simulation results demonstrate that the proposed strategy effectively observes and compensates for load disturbances, significantly reducing initial torque oscillations under three different operating conditions. After a sudden load increase, torque oscillations were reduced by 16%, with the stator current reaching steady state 0.03 s faster, response speed improving by 0.02 s, smooth transitions between speed ranges, and enhanced anti-disturbance performance.

Dynamic load frequency control in Power systems using a hybrid simulated annealing based Quadratic Interpolation Optimizer

Ensuring the stability and reliability of modern power systems is increasingly challenging due to the growing integration of renewable energy sources and the dynamic nature of load demands. Traditional proportional-integral-derivative (PID) controllers, while widely used, often fall short in effectively managing these complexities. This paper introduces a novel approach to load frequency control (LFC) by proposing a filtered PID (PID-F) controller optimized through a hybrid simulated annealing based quadratic interpolation optimizer (hSA-QIO). The hSA-QIO uniquely combines the local search capabilities of simulated annealing (SA) with the global optimization strengths of the quadratic interpolation optimizer (QIO), providing a robust and efficient solution for LFC challenges. The key contributions of this study include the development and application of the hSA-QIO, which significantly enhances the performance of the PID-F controller. The proposed hSA-QIO was evaluated on unimodal, multimodal, and low-dimensional benchmark functions, to demonstrate its robustness and effectiveness across diverse optimization scenarios. The results showed significant improvements in solution quality compared to the original QIO, with lower objective function values and faster convergence. Applied to a two-area interconnected power system with hybrid photovoltaic-thermal power generation, the hSA-QIO-tuned controller achieved a substantial reduction in the integral of time-weighted absolute error by 23.4%, from 1.1396 to 0.87412. Additionally, the controller reduced the settling time for frequency deviations in Area 1 by 9.9%, from 1.0574 s to 0.96191 s, and decreased the overshoot by 8.8%. In Area 2, the settling time was improved to 0.89209 s, with a reduction in overshoot by 4.8%. The controller also demonstrated superior tie-line power regulation, achieving immediate response with minimal overshoot.

Performance analysis of DC-DC Buck converter with innovative multi-stage PIDn(1+PD) controller using GEO algorithm

Power electronic converters are widely used in various fields of electrical equipment. Due to their fast dynamics and non-linear nature, controlling them requires dealing with various complexities. Therefore, having a well-designed, high-speed, and robust controller is critical to ensure the effective operation of these devices. In a DC-DC converter, steady-state performance with minimum error and fast dynamic response relies on controller design. This paper presents the design of a multi-stage PID controller with an N-filter combined with a one plus proportional derivative (1+PD) controller. This controller illustrates fast tracking reference voltage; additionally, it shows incredible results when the DC-DC converter operates in different modes. The parameters of the proposed controller are effectively determined using the golden eagle optimization (GEO) algorithm. Furthermore, a comprehensive comparison between the proposed controller, proportional–integral–derivative (PID), and fractional order PID (FOPID) controllers, as well as different metaheuristic optimization methods in various conditions, has been conducted to demonstrate the effectiveness of the proposed controller. The behavior of the closed-loop system under different conditions has been thoroughly investigated. The superior time and frequency domain characteristics of the closed-loop system with the PIDn(1+PD) controller highlight its superiority over other controllers. The demonstrated enhancements in settling time, voltage regulation accuracy, and transient response emphasize the potential applicability of the proposed control strategy in real-world power electronics systems, particularly in scenarios requiring high efficiency, stability, and dynamic performance.

Analysis and design of wire winding tension control system based on genetic algorithm and fuzzy PID

Wire winding is a key process in transformer manufacturing, and the accuracy of tension control directly affects the quality of the products. To achieve the requirement of constant tension winding of wire, the influence of roll diameter, speed, and other factors on the dynamic performance of tension is analyzed by establishing the dynamic model of unwinding and winding as well as the pendulum part. A control strategy combining proportional-integral-derivative (PID) control with fuzzy control is adopted, and the parameters of the fuzzy PID controller are optimized using an improved genetic algorithm. Simulation results demonstrate that the optimized fuzzy PID controller significantly reduces tension overshoot and effectively suppresses the tension fluctuation when the speed and roll diameter change, and the tension fluctuation is controlled within 2%. Finally, the feasibility of the optimized fuzzy PID controller in the tension control system is experimentally validated.

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.

High-Resolution Infrared Reflectance Distribution Measurement Under Variable Temperature Conditions

The bidirectional reflectance distribution function (BRDF) can effectively characterize the reflectance properties of a target, which can be used to correct infrared remote sensing data and improve the accuracy of remote sensing measurements. When the surface temperature changes, the reflectance characteristics of the target usually change, and it is necessary to carry out BRDF measurements under variable temperature conditions. In this paper, a variable-temperature infrared BRDF measurement system based on a robotic arm is developed to realize high-resolution wide-temperature region measurement of BRDF. To improve the measurement accuracy, the shaping optical path was used to expand the laser beam, combined with the laser level to accurately adjust the three-dimensional coordinates of the robotic arm, and the dichotomy method is used to calibrate the detector nonlinearly. A portable heater suitable for the mechanical arm corner mechanism is developed, and fast and high-precision temperature control is realized by proportional integral derivative (PID) control. The specular and diffuse BRDF distributions were measured at room temperature to verify the effectiveness of the system. The BRDF distribution of SUS314 stainless steel samples with different roughness is measured during two temperature increases from 20 °C to 1000 °C, and the changing rule of BRDF under variable temperature environment is summarized, which provides technical support for evaluating the optical properties of high-temperature materials and improving the measurement accuracy of remote sensing data.

Three-dimensional finite element analysis of biological signal feedback in the mechanical properties of sound production

In response to the problems of low reliability and long time consumption in traditional research on sound production, this article used electromyography (EMG) signals as input signals to build a high-precision sound production mechanics model. A Proportional-Integral-Derivative (PID) controller was used to dynamically adjust the model and develop a real-time feedback system. This article established a detailed three-dimensional (3D) finite element model including the vocal cords and throat, defined the nonlinear elastic properties and linear elastic properties of different tissues, and used tetrahedral grid partitioning technology to improve the computational accuracy of the model. Through a EMG sensor, an individual EMG was collected and filtered to remove noise. The processed EMGs were used as input parameters for the finite element model to drive the muscle units in the model. By using a PID controller to receive real-time EMG input, the error was calculated and the model was adjusted, enabling accurate simulation of the mechanical properties of vocal cord vibration under different vocal states and achieving real-time feedback. Considering the complexity of vocal cord vibration driven by biological signals, this article simulated and analyzed the modal characteristics of vocal cord vibration, and analyzed the differences in vocal cord vibration characteristics under different vocal states. The sound pressure distribution and resonance frequency were simulated and analyzed to understand the propagation characteristics of sound. Finally, by comparing and analyzing the simulated data with the actual collected data, it was found that the maximum relative error rate of the model under different sound states was 6.14%, and the overall error rate of the model was relatively low, which verified the reliability of the model. The findings demonstrated that the feedback delays of the model in different sound states were all within 100 milliseconds, indicating that the system had high real-time performance and accuracy, which was promising in practical applications.

Grey wolf optimization for enhanced performance in wind power system with dual-star induction generators

This study investigates strategies for enhancing the performance of dual-star induction generators in wind power systems by optimizing the full control algorithm. The control mechanisms involved include the PID (Proportional-Integral-Derivative) controller for speed regulation and the PI (Proportional-Integral) controller for flux, DC-link voltage, and grid connection control. The primary objective is to optimize the entire system by fine-tuning PID and PI controllers through the application of meta-heuristic algorithms, specifically Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO). These algorithms play a crucial role in estimating the optimal values of Kp, Ki, and Kd for the PID speed controller, as well as Kp and Ki for the PI controller used in the flux, DC-link voltage, and grid connection for wind energy conversion system based dual-star induction generator. This comprehensive optimization ensures accurate parameter tuning for optimal system performance. A comparative analysis of the optimization results has been conducted, focusing on the outcomes obtained with the GWO algorithm. The findings reveal a notable reduction in steady-state error, signifying improved stability, and an overall enhancement in the wind power system’s performance. This study contributes valuable insights into the effective application of meta-heuristic algorithms for optimizing dual-star induction generators in wind power systems.

Vision-based target localization and online error correction for high-precision robotic drilling

Abstract This article presents a detailed examination of circular target localization techniques for measuring robot pose and performing online pose correction. The investigated target localization methods include centroiding, ellipse fitting with point data and gradient information, and ellipse fitting methods with augmented and corrected input data. The performance of each method is evaluated in terms of accuracy and precision of measurements through experimental comparison with a laser tracker. This study provides technical and practical insights for selecting an appropriate target localization method in robotic applications. It also introduces a vision-based solution for robot relative error correction, comprising the calibration procedure and a closed-loop control with a proportional–integral-derivative controller for pose correction. Results show enhanced accuracy in robot positioning relative to workpiece, highlighting the effectiveness of the proposed solution in robotic drilling applications.

Control of Instantaneous Abnormal Mold Level Fluctuation in Slab Continuous Casting Mold Based on Bidirectional Long Short‐Term Memory Model

The instantaneous abnormal mold level fluctuation (IAMLF) has significant harmful effects on slab quality. This article proposes a prediction and control method for IAMLF. First, the data processed by difference method is used for the IAMLF prediction and stopper‐rod position prediction. Then, the bidirectional long short‐term memory (BI‐LSTM) is introduced to predict the IAMLF; the corresponding stopper‐rod position is predicted according to the mold level prediction result. BI‐LSTM can predict the IAMLF with the mean absolute error of 1.52 mm and the false alarm rate of 1.8%, and also performs well in predicting the stopper‐rod position with a mean absolute error of only 1.92 mm. Furthermore, the two prediction models are combined to form a closed loop, where the mold level fluctuation is predicted according to the industrial data processed by data difference method, and the stopper‐rod position is adjusted in advance to eliminate IAMLF. Finally, the fuzzy proportional‐integral‐derivative (PID) controller is used to control mold level based on the mold level and stopper‐rod position prediction results. The prediction accuracy of IAMLF reaches 98.4%, and the present proposed fuzzy PID controller can effectively prevent the occurrence of IAMLF with a success rate of 95.6%.

Performance Optimization with LPV Synthesis for Disturbance Attenuation in Planar Motors

Optimizing the performance of motion control systems with variations in nonlinear parameters is not an easy task. To accomplish this task, it is important to design the controller using the linear system approach. In this study, a linear parameter varying (LPV) control method is proposed in which nonlinearities are treated as parameter variations for planar motors. The proposed control method consists of the force and torque modulation with the commutation scheme and the nonlinear current controller with H∞ state feedback control in the form of LPV synthesis to improve the position-tracking performance. An interpolated gain-scheduling controller based on LPV synthesis is determined by applying H∞ control to a linear matrix inequality technique. An interpolated gain-scheduling controller can attenuate disturbance without disturbance estimation. The effectiveness of the proposed control method is evaluated using simulation results and compared with the conventional proportional–integral–derivative control to verify both improved position-tracking performance and disturbance attenuation.

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.

Power quality improvement of grid‐connected solar power plant systems using a novel fractional order proportional integral derivative controller technique

AbstractRecently, there has been a push to integrate renewable energy system (RES) into grid‐connected load system in enhancing reliability and reducing losses. However, integrating these systems introduces power quality (PQ) issues, especially with non‐linear, critical, and imbalanced loads. Addressing this, a hybrid mantis search‐reptile search algorithm (HMS‐RSA) combined with a unified power quality conditioner (UPQC) to mitigate PQ problems related to current and voltages in RES systems. In other words, the UPQC, enhanced by fractional order proportional integral derivative controller parameters tuned using the proposed HMS‐RSA assists in enhancing the power quality. The approach has been validated by connecting a non‐linear load to the system, which typically creates PQ issues. The proposed method is implemented in MATLAB/Simulink and their performance is analysed in three scenarios, such as sag, swell, and disturbance, and the total harmonic distortion is evaluated to quantify improvements in PQ. Finally, the proposed method is compared with existing approaches, such as ant colony optimization (ACO), artificial bee colony optimization (ABC), and bacterial foraging optimization (BFO). The method also outperforms ACO, ABC, and BFO in terms of convergence speed and effectiveness in mitigating PQ issues.

Simulation and real-life implementation of UAV autonomous landing system based on object recognition and tracking for safe landing in uncertain environments

The use of autonomous Unmanned Aerial Vehicles (UAVs) has been increasing, and the autonomy of these systems and their capabilities in dealing with uncertainties is crucial. Autonomous landing is pivotal for the success of an autonomous mission of UAVs. This paper presents an autonomous landing system for quadrotor UAVs with the ability to perform smooth landing even in undesirable conditions like obstruction by obstacles in and around the designated landing area and inability to identify or the absence of a visual marker establishing the designated landing area. We have integrated algorithms like version 5 of You Only Look Once (YOLOv5), DeepSORT, Euclidean distance transform, and Proportional-Integral-Derivative (PID) controller to strengthen the robustness of the overall system. While the YOLOv5 model is trained to identify the visual marker of the landing area and some common obstacles like people, cars, and trees, the DeepSORT algorithm keeps track of the identified objects. Similarly, using the detection of the identified objects and Euclidean distance transform, an open space without any obstacles to land could be identified if necessary. Finally, the PID controller generates appropriate movement values for the UAV using the visual cues of the target landing area and the obstacles. To warrant the validity of the overall system without risking the safety of the involved people, initial tests are performed, and a software-based simulation is performed before executing the tests in real life. A full-blown hardware system with an autonomous landing system is then built and tested in real life. The designed system is tested in various scenarios to verify the effectiveness of the system. The code is available at this repository: https://github.com/rnjbdya/Vision-based-UAV-autonomous-landing.

ADRC Control of Ultra-High-Speed Electric Air Compressor Considering Excitation Observation

With the increasing power of fuel cells, ultra-high-speed electric air compressors (UHSEACs) have been widely used. However, due to the ultra-high speeds involved, UHSEACs face problems such as long speed adjustment times and large speed fluctuations. Compared to other control methods, Active Disturbance Rejection Control (ADRC) is well-suited for highly nonlinear systems like UHSEACs. The Extended State Observer (ESO), a key component of the ADRC, struggles to accurately observe high-frequency excitations. To address this, the first step is to add a cascaded structure to the ESO and design a Current State Extended State Observer (CS-ESO) to better observe the electromagnetic and load excitations in the UHSEAC. The second step involves designing the ADRC based on the CS-ESO and performing speed adjustment simulations. The third step is to build a UHSEAC experimental platform and a conduct speed adjustment experiment. The findings indicate that, compared to the Proportional Integral Derivative (PID) control, the ADRC with the ESO, and the Sliding Mode Control (SMC), the use of the ADRC with the CS-ESO results in a significant reduction in overshoot—by at least 760 RPM under load-increasing conditions and 140 RPM under load-reducing conditions. Furthermore, the speed regulation time is notably decreased by at least 0.2 s and 0.1 s under these respective conditions.

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.

DDPG‐based heliostats cluster control of solar tower power plant

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

Real-time non-invasive control of ultrasound hyperthermia using high-frequency ultrasonic backscattered energy in ex vivo tissue and in vivo animal studies

Objective.A reliable, calibrated, non-invasive thermometry method is essential for thermal therapies to monitor and control the treatment. Ultrasound (US) is an effective thermometry modality due to its relatively high sensitivity to temperature changes, and fast data acquisition and processing capabilities.Approach.In this work, the change in backscattered energy (CBE) was used to control the tissue temperature non-invasively using a real-time proportional-integral-derivative (PID) controller. A clinical high-frequency US scanner was used to acquire radio-frequency echo data fromex vivoporcine tissue samples andin vivomice hind leg tissue while the tissue was treated with mild hyperthermia by a focused US applicator. The PID controller maintained the focal temperature at approximately 40 °C for about 4 min.Main results.The results show that the US thermometry based on CBE estimated by a high-frequency US scanner can produce 2D temperature maps of a localized heating region and to estimate the focal temperature during mild hyperthermia treatments. The CBE estimated temperature varied by an average of ±0.85 °C and ±0.97 °C, compared to a calibrated thermocouple, inex vivoandin vivostudies, respectively. The mean absolute deviations of CBE thermometry during the controlled hyperthermia treatment were ±0.45 °C and ±0.54 °C inex vivoandin vivo,respectively.Significance.It is concluded that non-invasive US thermometry via backscattered energies at high frequencies can be used for real-time monitoring and control of hyperthermia treatments with acceptable accuracy. This provides a foundation for an US mediated drug delivery system.