Proportional-integral-derivative Parameters Research Articles

Self-tuning speed and flow control of micro turbojet engines based on an improved evolutionary strategy

At present, the controllable parameters of micro turbojet engines in engineering applications are mainly speed-fuel flow (hereinafter referred to as flow) control, in which closed-loop proportional–integral–derivative (PID) control is mostly used to achieve a stable control of engine speed under slow engine conditions. For the optimal adjustment of PID parameters, this paper designs an improved evolutionary strategy for the self-tuning of control parameters in the engine speed and flow control system and formulates an improved PI controller based on a neural network. The simulation experimental results show that the method can realistically achieve stable and fast control of the engine under above slow conditions.

Design of adaptive-PID-Smith control in the two-mixing tank series

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

Deep reinforcement learning for PID parameter tuning in greenhouse HVAC system energy Optimization: A TRNSYS-Python cosimulation approach

The control of indoor temperature in greenhouses is crucial as it directly impacts the crop's thermal comfort and the performance of heating, ventilation, and air-conditioning (HVAC) systems. Conventional feedback controllers, like on/off, can sometimes make HVAC system work at full capacity when only half that capacity is needed. In contrast, the proportional-integral-derivative (PID) controller, provides precise control based on its P, I, and D parameters. However, it lacks a formal design procedure for optimizing a specified objective function. Previous studies have utilized conventional PID tuning approaches to track room setpoint temperature for residential buildings, data centers, and office buildings, with limited research in greenhouse applications. To address this gap, this study proposes a flexible PID controller that employs a deep reinforcement learning (DRL) algorithm to optimize its parameters, by tracking the setpoints and energy consumption of a greenhouse planted with tomatoes. This approach is different from the typical method of using the trained RL agent directly in HVAC controls. Through a self-made TRNSYS-Python cosimulation framework, the DRL agent interacts directly and in real time with the greenhouse and its plants. Consequently, optimized PID parameters were established and tested in the simulated environment. The resulting performance, in terms of both energy consumption and its ability to maintain the crop’s comfort temperature, was compared with the simulated on/off and manually tuned PID controllers. Compared to the on/off baseline control, the proposed PID optimized parameters reduce energy use by 8.81% to 12.99% and the manually tuned PID parameters with the Ziegler-Nichols tuning method reduce energy use by 7.17 %. Additionally, the proposed method had a deviation of 2.07% to 3.13%, while the manually tuned PID controller and the on/off controller had deviations of 7.27% and 3.27%, respectively, from the minimum comfortable temperature. This study serves as a framework for improving the energy efficiency of greenhouse HVAC system operations.

A Temperature Control Method of Lysozyme Fermentation Based on LRWOA-LSTM-PID

In order to overcome the difficulty of parameter tuning caused by the large lag and time-varying nonlinearity of the tank for lysozyme fermentation, a temperature control method based on LRWOA-LSTM-PID is proposed in this paper. Firstly, according to the intrinsic mechanism of the fermenter, a temperature mechanism model based on a dynamic equation is designed, which can better reflect the temperature changes in the fermenter. Secondly, a Proportional Integral Derivative (PID) parameter tuning method based on a Long-Short Term Memory Network (LSTM) is proposed, which takes advantage of the ability of LSTM to learn time sequence information and obtains the variation trend between error sequences under continuous time sampling, thereby adjusting network weights more reasonably and accelerating PID parameter tuning. Finally, a Whale Optimization Algorithm (WOA) based on the Lévy flight and random walk strategy (LRWOA) is proposed for the initialization of LSTM parameters; this algorithm has excellent optimization capabilities and overcomes the problem of LSTM falling into local optimal solutions prematurely during parameter randomization. The results show that the method proposed in this paper can achieve rapid tuning of PID parameters, thereby improving the convergence speed of the system and reducing system overshoot.

Advancements and Challenges in Underwater Soft Robotics: Materials, Control and Integration

This article focuses on the progress of underwater robots and the importance of software architectures in building robust and autonomous systems. The researchers underscore the challenges linked to implementation and stress the need for comprehensive validation of both reliability and efficacy. Their argument is on the extensive implementation of a globally applicable architectural framework that complies with established standards and guarantees interoperability within the field of robotics. The research also covers advancements in underwater soft robotics, which include the development of models, materials, sensors, control systems, power storage, and actuation techniques. This article explores the challenges and potential applications of underwater soft robotics, highlighting the need of collaboration across many fields and advancements in mechanical design and control methods. In the last section of the paper, the control approach and algorithms used to underwater exploration robots are reviewed. Particular attention is given to the application of Proportional Integral Derivative (PID) control and the incorporation of Backpropagation Neural Network (BPNN) for PID parameter determination.

Elitism-crossover barnacle mating optimization and its application to PID controller design for a buck converter

This paper presents an elitism-crossover barnacle mating optimization (ECBMO). It is an improvement of barnacle mating optimization (BMO). The original BMO suffers from local optima problem leading to a low accurate solution. A new method of offspring generation is adopted into the original BMO structure. Some features of the best-so-far solution are incorporated into the generated offspring. The accuracy performance of the proposed algorithm is tested on several IEEE functions. A statistical analysis is conducted to compare its performance over the original BMO. It is also applied to optimize proportional integral derivative (PID) parameters for controlling output voltage of a buck converter. Result of benchmark functions test shows the proposed algorithm has attained higher accuracy for all functions compared to BMO algorithm. Application on the real problem shows both algorithms control the converter voltage satisfactorily. However, the ECBMO has achieved more optimal PID parameters and leading to a better output voltage response.

Control of coupled nonaffine multiagent stochastic systems by classical PID

AbstractThe classical PID (proportional‐integral‐derivative) control is the most basic and widely used control algorithm in engineering systems by far. Whether PID control can effectively deal with coupled multiagent uncertain nonlinear dynamics is well worth investigating. In this article, a class of PID controlled multiagent stochastic systems with coupled uncertain dynamics is considered, where the control input is nonaffine. Each agent has its own regulation objective, and only has access to its own regulation error information for PID feedback. First, a PID parameter set will be constructed explicitly based on some prior knowledge about the nonlinear functions, such that the overall multiagent system can be stabilized globally in the mean square sense, as long as the PID parameters are chosen from the constructed set. Moreover, the regulation error will be proven to have an upper bound proportional to the noise intensity along the reference signal. Furthermore, it will be shown that the regulation error of each agent can be arbitrarily small by choosing the PID parameters suitably large. Finally, the theoretical results will be verified by simulations.

Review Work on Modelling & Simulation of Car Cruise Control System using PID Controller

This work focuses on the modeling and simulation of car cruise control systems with the implementation of a Proportional-Integral-Derivative (PID) controller. The primary objective is to develop a comprehensive model that integrates a PID controller for maintaining car speed under varying conditions. The methodology involves detailed modeling techniques to simulate car dynamics and the tuning of PID parameters for optimal control

Suppression method of ultralow-frequency oscillation under ambient excitation in hydro-dominant power systems

Ultralow-frequency oscillations (ULFOs) have frequently occurred in hydro-dominant power systems in recent years. These phenomena seriously threaten the safe and stable operation of power systems. Therefore, measures that can effectively suppress ULFO need to be taken. Currently, the hydropower unit governor can enhance the dynamic stability and dampening oscillations of a system through adjusting the proportional–integral–derivative parameters. However, modifying these parameters can affect the primary frequency regulation performance, which compromises the assurance of grid frequency quality. This study proposes a switching type of hydro generator governor based on the local damping contribution rate to address the aforementioned problems. The approach incorporates the energy structure into the classical governor model, calculates the local damping contribution rate, and enables automatic switching of the hydro generator governor parameters based on positive and negative local damping contributions. This way allows for segmented control with different parameter settings, which enables the system to effectively consider the damping characteristics of ULFO while maintaining primary frequency regulation performance. It can also accurately and efficiently suppress the ULFO. The proposed method is verified through arithmetic analysis conducted on a 16-machine 5-zone system and an actual grid system. Simulation results demonstrate the effectiveness of the method.

IMPLEMENTASI SISTEM KONTROL PENGGERAK MOTOR STEPPER PADA PROSES MOLDING MICROPLASTIC BERBASIS PID MENGGUNAKAN PLC DAN ARDUINO

The current industry has entered an era where every production process incorporates automation and data exchange in its manufacturing. This era is known as Industry 4.0. Industry 4.0 has had a significant impact on various industrial sectors, particularly in the production of plastic goods using techniques such as injection molding. This process is capable of producing more precise products. In the injection molding process, a well-controlled system is needed to guide plastic pellets according to the intended mold design, utilizing actuators like stepper motors. This research aims to apply the PID (Proportional-Integral-Derivative) method to control stepper motors in microplastic molding processes. The proposed system has been successfully implemented and can produce products according to the planned specifications. The most suitable PID parameter values for controlling the stepper motor are Kp = 14, Ki = 0.007, and Kd = 7000. Changing the setpoint value from 2500 to 2700 has an impact on the molding results, with the best results achieved at a setpoint value of 2700. A setpoint value of 2700 ensures that the mold and nozzle are in the correct, tight position, allowing the molding process to proceed smoothly in line with the provided design.

Automatic irrigation system with a fiber-optic pressure sensor regulating intrapelvic pressure for flexible ureteroscopy

Increased intrapelvic pressure (IPP) due to irrigation during flexible ureteroscopy (f-URS) can pose a risk of postoperative severe urinary tract infection associated with pyelovenous backflow. An automatic regulation system for maintaining safe IPP levels could enable surgeons to perform f-URS safely without postoperative complications. This study aimed to assess the measurement accuracy of an ultra-miniature fiber-optic pressure sensor incorporated into a small-caliper ureteroscope for assessing IPP and to develop an automatic irrigation system linked to this sensor. A porcine kidney was used for the ex vivo experiment. The nephrostomy catheter, connected to the conventional pressure transducer, was placed on the renal pelvis to evaluate the actual IPP (a-IPP). For measuring IPP using the fiber-optic pressure sensor (fo-IPP) built into the f-URS, a diaphragm pressure sensor of Φ250 μm was used. To establish an irrigation system, the optimal proportional–integral–derivative (PID) controller was explored to accurately adjust the irrigation pump flow rate. A high correlation between a-IPP and fo-IPP was confirmed across irrigation pressure values of 60–180 mbar (all, r ≥ 0.7, p < 0.001). When performing bolus irrigation, although fo-IPP showed relatively a higher peak value than a-IPP, the response time of fo-IPP was equivalent to that of a-IPP. After PID parameter optimization, our automatic irrigation system based on fo-IPP smoothly and accurately regulated the intended IPP set in the 5–20 mmHg range without overshooting. We successfully developed and demonstrated an automatic irrigation system regulating IPP based on the PID controller for f-URS, utilizing a fiber-optic pressure sensor. Further research, including in vivo studies, will be needed to assess clinical feasibility.

Research on Temperature Control of Plastic Laser Welding Based on Improved Snake Optimization Algorithm

Aiming at the nonlinear and time-varying characteristics of plastic laser welding temperature control system, and the problems of low accuracy and large overshoot when using the traditional proportional-integral-derivative (PID) controller, an improved snake optimization (ISO) is proposed to optimize the PID parameters. Firstly, the elite reverse learning strategy is introduced into the snake optimization (SO) to generate the reverse solution, and new individuals are generated through comparison to improve the convergence speed of the algorithm; Simultaneously introducing the water wave dynamic adaptive factor to enhance the algorithm’s iterative optimization ability. By iterating the algorithm, a set of suitable PID parameters are obtained to control the temperature control system. The simulation results show that compared to traditional PID controllers, ISO-PID reduces the adjustment time by approximately 0.42 seconds and does not cause overshoot.

Optimal control of DC motor using leader-based Harris Hawks optimization algorithm

Direct Current (DC) motor is considered a very critical component of various industrial drive equipment. This is due to their unique advantages including reasonable cost, speed-torque characteristics, ease of control, etc. While most DC motor drive applications often employ PID controllers to regulate the speed of the machine, selecting the optimal design parameters for the controllers used in these applications often posed a serious challenge. Moreover, as the complexity of the industrial process increases, the need for precise speed and position tracking becomes necessary. This current study proposes a novel Leader-based Harris Hawks Optimization (LHHO) algorithm for the design of Proportional-Integral-Derivative (PID) and Fractional Order Proportional-Integral-Derivative (FOPID) controllers to achieve optimal speed regulation of DC motors. The LHHO algorithm is an innovative meta-heuristic algorithm that draw inspiration from the cooperative hunting behavior and leadership prowess of the Harris Hawks called the “seven pounds”. While several error functions were tested in this study, the integral of time multiplied absolute error (ITAE) has been adopted as the error function for obtaining the parameters of PID and FOPID controllers using the LHHO algorithm. Through quantitative evaluations and comparisons with existing techniques, the proposed controllers (LHHO-PID and LHHO-FOPID) have revealed significant improvements in key performance metrics, including rise time, settling time, and maximum overshoot during transient periods when ITAE is used as the error function. Furthermore, the stability response and robustness analyses were carried out by varying the parameters of the DC motor under eight scenarios, which confirmed competitive performance in system response and transient behavior.

Two Feedback PID Controllers Tuned with Teaching–Learning-Based Optimization Algorithm for Ball and Beam System

The proportional integral derivative (PID) controller continues to be the most popular and widely used in the industry despite the development of various artificial intelligence-based controllers due to its simplicity and ease of use. Setting PID parameters, especially in non-linear systems, is still a significant problem. This study presents a cascade PID tuning technique based on the teaching–learning-based optimization (TLBO) algorithm. The proposed method is tested for the position control of a non-linear ball and beam system. A comparative analysis of the proposed method is done with the conventional tuning method and particle swarm optimization-tuned cascade PID controller. The optimization was carried out using integral time absolute error, integral time error, and integral square error as objective functions. It was observed that the evolutionary algorithm-based controller tuned by TLBO gives a much better response regarding rise time, settling time, and overshoot. To test the effectiveness and validity of the proposed controller robust analysis of the proposed controller is carried out with a step disturbance applied at t = 3 s. The comparative study proves that TLBO-tuned response is better than other controllers. Sensitivity analysis of the proposed controller is also performed by varying system parameters.

Ant colony optimization-based adjusted PID parameters: a proposed method

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.

Semiglobal stability of PID for uncertain nonaffine systems

Proportional–integral–derivative (PID) controller is the most widely used form of feedback control by far. However, a satisfactory theory is still lacking to explain the rationale of the widespread PID control, and the gap between theory and practice of PID needs to be filled by control scientists. It is worth noting that a recent work (Zhao and Guo, 2022) proved that the classical PID controller has the ability to globally stabilize a basic class of uncertain nonaffine nonlinear systems, while the system nonlinear function is required to have a linear growth rate with respect to the state variables. In this paper, we will consider the more general case where the system nonlinear function may have super-linear growth rates, and show that the classical PID control can always be designed to stabilize such systems in the semiglobal sense, provided that the smallest eigenvalue of the control gain matrix has a positive lower bound. Moreover, a three dimensional parameter set for the stabilizing PID parameters will be constructed. Furthermore, under some additional knowledge about the system uncertain functions, we will propose a simpler PD controller with desired compensation, and show that the proposed controllers can still be designed to achieve semiglobal stability and desired tracking targets.

Optimal tuning proportional integral derivative controller on direct current motor using reptile search algorithm

&lt;span lang="EN-US"&gt;This paper presents the reptile search algorithm (RSA) method to optimize the proportional integral derivative (PID) parameters on direct current (DC) motors. RSA was adopted from crocodile hunting behavior. Crocodile behavior is modeled in two important steps: surrounding and attacking prey. The RSA method was applied using twenty-three classical test functions. The search method of the proposed RSA method with other existing algorithms such as particle swarm optimization (PSO), and differential evolution (DE). Integral multiplied by absolute error (ITAE) and integral of time multiplied squared error (ITSE) were used as comparisons in measuring the performance of the RSA method. The results show that the proposed method, namely RSA, has better efficiency. Optimization of PID parameters with RSA on DC motor control shows superior performance. From the experiment, the ITSE average value of the RSA method is 4.17% better than the conventional PID method.&lt;/span&gt;

Genetic Algorithm based PID Tuning Software Design and Implementation for a DC Motor Control System

This study presents the software and implementation for proportional-integral-derivative (PID) tuning of a DC motor control system using genetic algorithm (GA). The PID parameters for a specific control structure are optimized using GA in the proposed tuning procedure. Also, integral time absolute error (ITAE) is used as a fitness function to optimize the parameters. The robustness of the control system is compared with conventional mathematical method. Simulations are carried out in MATLAB/Simulink to compare the results of a DC motor control system. Simulation results show that in terms of overshoot, steady-state error, and settling time, GA-based PID tuning approach performed better than conventional method. Additionally, a sensitivity analysis is performed to evaluate how robust the proposed approach is to parameter variations. The analysis shows that compared to the conventional method, the GA-based PID tuning algorithm is more adaptable to variations in system parameters.

Multi-Phase Focused PID Adaptive Tuning with Reinforcement Learning

The Proportional-Integral-Derivative (PID) controller, a fundamental element in industrial control systems, plays a pivotal role in regulating an extensive array of controlled objects. Accurate and rapid adaptive tuning of PID controllers holds significant practical value in fields such as mechatronics, robotics, and automatic control. The three parameters of the PID controller exert a substantial influence on control performance, rendering the tuning of these parameters an area of significant interest within related research fields. Numerous tuning techniques are widely employed to optimize its functionality. Nonetheless, their adaptability and control stability may be constrained in situations where prior knowledge is inadequate. In this paper, a multi-phase focused PID adaptive tuning method is introduced, leveraging the deep deterministic policy gradient (DDPG) algorithm to automatically establish reference values for PID tuning. This method constrains agent actions in multiple phases based on the reward thresholds, allowing the output PID parameters to focus within the stable region, which provides enhanced adaptability and maintains the stability of the PID controller even with limited prior knowledge. To counteract the potential issue of a vanishing gradient following action constraints, a residual structure is incorporated into the actor network. The results of experiments conducted on both first-order and second-order systems demonstrate that the proposed method can reduce the tracking error of a PID controller by 16–30% compared with the baseline methods without a loss in stability.

HYBRID PARTICLE SWARM OPTIMIZATION AND GREY WOLF OPTIMIZER FOR SETTING PID PARAMETERS OF BLDC MOTORS

BLDC (Brushless DC) motors have advantages over asynchronous motors and dc motors in various aspects. Particularly in electric bicycles and flying cars, BLDC motors are utilized widely. Electric bicycles and flying cars are becoming increasingly popular, and as a result, the significance of BLDC motors and their cost-effective and efficient utilization has been growing rapidly. PID (Proportional Integral Derivative) controllers are generally used in motor control because they are cheap and perform well. Many methods have been used to adjust PID parameters. Although methods such as Ziegler-Nichols, Cohen-Coon etc. are widely used, there are also new methods such as optimization algorithms PSO (Particle Swarm Optimization), Whale Optimization Technique, Gray Wolf Optimization technique etc. The hybrid method: HPSOGWO (Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer) is a combination of PSO and GWO (Grey Wolf Optimizer) techniques, and it can be used for tuning PID parameters. As associated with this, the aim of this study is to show the superiority of HPSOGWO algorithm in optimizing the PID parameters. In the content of this study, the essentials regarding the optimization background, and details of the BLDC motor modeling was explained first. After that, the methodology of the hybrid solution was expressed and then the application phase was explained in detail, by including the results generally. In the context of the intelligent optimization approach of this study, the results were obtained in the MATLAB Simulink environment. The application of the used solution method revealed its superiority over the study conducted solely with GWO in various parameters.