PID Controller Parameters Research Articles

Organizing the automated system of dispatch control over pump units at water pumping stations

The design and operation of modern dispatch control systems for pumping units involves comprehensive solution to separate engineering, technical, and scientific problems. The object of research is information processes enabling the operational modes of electrically driven pumping units at water pumping stations. This paper solves the scientific and technical task related to developing topology, hardware, and software tools, control algorithms, dispatch interface, and researching the modes of operation of pumping units in dispatch control systems at water pumping stations. Algorithms for controlling pumping units have been implemented, the advantage of which is the possibility of automated calculation of parameters for technological PID-controllers, taking into account the current electrical parameters of the frequency-controlled electric drive of pumping units and operating conditions. The WEB-oriented dispatch interface of the SCADA-based pumping unit control system was designed and tested, which enables control process in real time. Features of the developed dispatch control system are improved topology, expanded functionality, energy-saving control modes, and the possibility of further modernization based on the principles of standardization and unification of hardware and software tools and design procedures. The developed dispatch control system for pumping units at water pumping stations has been implemented and is successfully operated at an industrial water supply enterprise. The results provided for an increase in technical and economic indicators during the operation of technological equipment due to the efficiency of control and management procedures, energy-saving operational modes of the frequency-controlled electric drive of pumping units.

A Fuzzy Adaptive PID Coordination Control Strategy Based on Particle Swarm Optimization for Auxiliary Power Unit

Range extender hybrid vehicles have the advantages of better dynamics and longer driving range while reducing pollution and fuel consumption. This work focuses on the control strategy of an Auxiliary Power Unit (APU) operating in power generation mode for a range-extender mixer truck. When an operating point is switched, the engine speed and generator torque of the APU will switch accordingly. In order to ensure APU fast and stable adjustment to meet the power demand of the vehicle as well as operate at the lowest fuel consumption, a fuzzy adaptive PID coordination control strategy based on particle swarm optimization (PSO) is proposed to control the APU. The optimal operating curve of APU is calculated by coupling the engine and generator first. Then, the adaptive PID algorithm is used to control the speed and torque of the APU in a dual closed loop. The PSO is used to optimize the PID control parameter. Through hardware-in-the-loop tests under different working conditions, the control strategy is verified to be effective and real-time. The results show that the proposed control strategy can coordinate the operating of engine and generator and control the APU to track target power stably and quickly under minimum fuel consumption. Compared with traditional PID control strategy, the overshoot, regulation time and steady-state error are reduced by 55.1%, 11.1% and 77.3%, respectively.

Optimal Control of FSBB Converter with Aquila Optimizer-Based PID Controller

This paper presents a new methodology for determining the optimal coefficients of a PID controller for a four-switch buck–boost (FSBB) converter. The main objective of this research is to improve the performance of FSBB converters by fine-tuning the parameters of the PID controller using the newly developed Aquila Optimizer (AO). PID controllers are widely recognized for their simple yet effective control in FSBB converters. However, to further improve the efficiency and reliability of the control system, the PID control parameters must be optimized. In this context, the application of the AO algorithm proves to be a significant advance. By optimizing the PID coefficients, the dynamic responsiveness of the system can be improved, thus reducing the response time. In addition, the robustness of the control system is enhanced, which is essential to ensure stable and reliable operation under varying conditions. The use of AOs plays a key role in maintaining system stability and ensuring the proper operation of the control system even under challenging conditions. In order to demonstrate the effectiveness and potential of the proposed method, the performance of the AO-optimized PID controller was compared with that of PID controllers tuned by other optimization algorithms in the same test environment. The results show that the AO outperforms the other optimization algorithms in terms of dynamic response and robustness, thus validating the efficiency and correctness of the proposed method. This work highlights the advantages of using the Aquila Optimizer in the PID tuning of FSBB converters, providing a promising solution for improving system performance.

PID PATH FOLLOWING CONTROL SYSTEM DESIGN ON UNMANNED AUTONOMOUS FORKLIFT PROTOTYPE

One of the technologies often used in material handling and material transport is forklift. Conventionally forklifts are operated by human operators. For efficiency, to improve security, safety, and occupational health and to minimize the risk of operators, forklifts can be automated. Therefore, the idea of unmanned autonomous forklift innovation was introduced. In this final project, a motion control system was designed for an unmanned autonomous forklift prototype both in simulation and hardware using PID control techniques, and the odometry system was equipped with a rotary encoder. In the simulation, the controlled variable was the distance and the manipulated variable was the force on the vehicle. Meanwhile, in the prototype, the controlled variable was distance and the manipulated variable was the motor pulse. In the simulation stage the PID control parameters were applied . with an error of 0.32% in the simulation. The PID control parameters were applied to the prototype, that is, . The distance tests were done with variation of 50 cm to 200 cm (25 cm intervals). One variation of the distance experiment was done 5 times. The average absolute error resulted was 2.36 cm.

PID Controller Based on Improved DDPG for Trajectory Tracking Control of USV

When navigating dynamic ocean environments characterized by significant wave and wind disturbances, USVs encounter time-varying external interferences and underactuated limitations. This results in reduced navigational stability and increased difficulty in trajectory tracking. Controllers based on deterministic models or non-adaptive control parameters often fail to achieve the desired performance. To enhance the adaptability of USV motion controllers, this paper proposes a trajectory tracking control algorithm that calculates PID control parameters using an improved Deep Deterministic Policy Gradient (DDPG) algorithm. Firstly, the maneuvering motion model and parameters for USVs are introduced, along with the guidance law for path tracking and the PID control algorithm. Secondly, a detailed explanation of the proposed method is provided, including the state, action, and reward settings for training the Reinforcement Learning (RL) model. Thirdly, the simulations of various algorithms, including the proposed controller, are presented and analyzed for comparison, demonstrating the superiority of the proposed algorithm. Finally, a maneuvering experiment under wave conditions was conducted in a marine tank using the proposed algorithm, proving its feasibility and effectiveness. This research contributes to the intelligent navigation of USVs in real ocean environments and facilitates the execution of subsequent specific tasks.

An Optimized PID Controller Desing for BLDC Motor Using Nature-Inspired Algorithms

For the optimal control of speed in a brushless DC motor, it is crucial to appropriately adjust the parameters of the PID controller. This study addresses the determination of PID controller parameters using nature-inspired metaheuristic optimization algorithms. Initially, the dynamic model of the brushless DC motor is formulated in the MATLAB/Simulink environment. The grey wolf optimization algorithm, whale optimization algorithm, and firefly algorithm are successively applied to the simulation model to optimize the PID controller parameters. The integral time absolute error objective function is utilized to compare the error performances of these algorithms. Additionally, performance evaluations are conducted concerning parameters such as rise time, settling time, and maximum overshoot. As a result of the comparison based on the fitness criteria, it was determined that the grey wolf optimization algorithm is 35% more successful than the algorithm that provided the next closest result.

PID Controller Parameter Tuning Based on a Modified Differential Evolutionary Optimization Algorithm for the Intelligent Active Vibration Control of a Combined Single Link Robotics Flexible Manipulator

This paper introduces some of the various techniques of vibration control and optimization for the purpose of vibration reduction and balancing. Here, in this research by comprising three of the most effective variational techniques now, a Modified Differential Evolutionary Optimization Algorithm (MDEOA) method is suggested to handle the challenge of adjusting the PID controller parameters for the Intelligent Active Vibration Control (IAVC) of a Combined Single Link Robotics Flexible Manipulator (CSLRFM) in order to reduce the undesired effects of vibration. The Crossover Probability Factor (CPF) as the Certain Ratio (CR) and the Mutation Factor (MF) of the algorithm are gradually altered during algorithm iteration to enhance the method's performance during optimization. On this foundation, the PID controller parameter tuning and the issue of CSLRFM mechanical vibrations are addressed using the MDEOA method. This research suggests an evolutionary algorithm that incorporates the variational techniques mentioned above, which will be combined by a certain ratio, and the specific computational procedure. In this strategy, a Strictly Bearish Distributed Exponential Function (SBDEF) has been used as the main target and the criteria and indicators for evaluating and measuring the optimal performance of differential evolution are the Integral Absolute Error (IAE) rate and the PID controller parameter values. According to simulation findings, the technique can be used to optimize the PID controller parameters settings for the IAVC of the CSLRFM and a reduction in the mechanical vibrations. Simulation results illustrate the effectiveness of the proposed MDEOA strategy which is significantly and quite satisfactory about 25 to 30 (%) better than comparing to the other algorithms in improvement stabilization and vibration control of CSLRFM.

Research on the Optimization of the PID Control Method for an EOD Robotic Manipulator Using the PSO Algorithm for BP Neural Networks

Large-scale explosive ordnance disposal (EOD) robotic manipulators can replace manual EOD tasks, offering higher efficiency and better safety. This study focuses on the control strategies and response speeds of EOD robotic manipulators. Using Adams to establish the dynamic model of an EOD robotic manipulator and constructing a hydraulic system model in AMEsim, a co-simulation model is integrated. This study proposes a PID control strategy optimized by the particle swarm optimization (PSO) algorithm for a backpropagation (BP) neural network and simulates the system’s step response for analysis. To address the vibration issues arising during the manipulator’s motion, B-spline curves are used for trajectory optimization to reduce vibrations. The PSO algorithm optimizes the connection weight matrix of the BP neural network, solving the potential problem of local minima during the training process of the BP neural network, thereby enhancing the global search capability, learning efficiency, and network performance. Simulation results indicate that compared to traditional BP+PID control, genetic algorithm (GA)+PID control, and whale optimization algorithm (WOA)-BP+PID control, the PSO-BP+PID algorithm control rapidly tunes the PID control parameters Kp, Ki, and Kd. Under the same step function conditions, the overshoot is only 1.37%, significantly lower than other methods, and the settling time is only 14 s. After stabilization, there is almost no error, demonstrating faster response speed, higher control accuracy, and stronger robustness. This research has theoretical value and reference significance for the control methods and improvements in EOD robotic manipulators.

A comparative study of the Zeigler-Nickols PID, and fuzzy-PID based speed controller BLDC motor

Because of their effectiveness, simplicity, and polyvalence, PID controllers are widely used in industrial applications. Within the field of electric motors, namely the brushless direct current motors (BLDC). To maximize the performance of BLDC motors, which are known for their dependability and efficiency, precise speed control is essential. PID must be adjusted each time they are used in an application in order to maximize performance. In this study, we presented a comparison between two PID controllers for Brushless DC motors (BLDC). The first suggested PID controller is based on the Ziegler-Nichols method, a well-known empirical adjustment and testing strategy, resulting in under optimal performance in the presence of optimum local solutions for large research spaces. The fuzzy inference system is used to adjust the controller parameters in the second PID controller. The performance with the proposed Zigler-PID and fuzzy- PID controllers is analyzed and compared using the developed MATLAB/simulink model. These results demonstrate the effectiveness of Fuzzy –PID in improving the precision of speed control and energy efficiency.

Analysis of the PID Controller Parameters for A Surface Mobility Platform Mobile Robot

Surface mobility control is a fundamental challenge in robotics, and its applications range from autonomous vehicles to industrial automation. The main aim of the paper is to analyze the PID controller parameters for the speed control of a surface mobility platform mobile robot. MATLAB simulation toolbox and mathematical model of the DC motor are used to find the optimal PID controller parameters to complete the DC motor speed control system. For the SMP motion control system, the mobile robot kinematic model is supported as a differential drive system. In this paper, the PID controller parameters for the DC motor control system are analyzed with simulation results using various KP, KI, KD gains. The experimental results of the SMP mobile robot with optimal PID controller parameters are analyzed by driving on the rough, slope and even terrains. According to simulation results and experimental tests, the proposed system effectively controls the speed of the SMP mobile robot, demonstrating satisfactory performance in settling time, rise time, and system overshoot. Among PI and PID controllers, the designed PID controller can quickly control to reach the desired DC motor speed. Using the optimal PID coefficient parameters, all motors reached their desired speed in 0.12 seconds during simulations. In the experimental tests on various terrain (even, slope and rough), all motors achieved their desired speed simultaneously within 25 seconds.Keywords: Differential drive, PID controller, motion trajectory, real-time speed control. skid steering, SMP mobile robot, surface mobility control

Optimization of PID control parameters for marine dual-fuel engine using improved particle swarm algorithm

This study presents a comprehensive investigation into the optimization of PID control parameters for marine dual-fuel engines using an improved particle swarm algorithm. Through the development of a Matlab/Simulink simulation model, the thermodynamic behavior of the engine and the functionality of its control system are analyzed. The PID control parameters for air–fuel ratio control and mode switching control systems are fine-tuned utilizing the improved particle swarm algorithm (PSO). Simulation results demonstrate that the proposed improved PID-PSO approach outperforms traditional PID and traditional PSO-PID control methods in terms of reduced overshoot, minimized steady-state error, faster response times, and improved stability across various operating conditions and response modes. In comparison to traditional PID and PSO-PID controllers, the improved PSO-PID controller reduces the response time by 0.47 s and 0.21 s, the maximum overshoot by 98.43% and 96.05%, and decreases the absolute errors by 87.42% and 90.55%, respectively, in air–fuel ratio control using the step response method. The study's findings offer valuable insights into enhancing the performance and efficiency of marine dual-fuel engines through advanced control strategies.

Research on the Application of Neural Network PID in Quadcopter Aircraft

As a highly maneuverable and flexible unmanned aerial vehicle, quadcopters have broad application prospects in both civilian and military fields. However, due to their complex dynamic characteristics, nonlinearity, and strong coupling, achieving stable and precise control of quadcopters is a challenging task. Traditional control methods often fail to meet the control performance requirements for such complex nonlinear systems. Neural networks provide a new approach to solving control problems in complex systems due to their powerful learning and adaptive capabilities. Neural networks can improve the performance of control systems by learning from large amounts of data, capturing the dynamic characteristics of the system, and adjusting control strategies online. Combining neural networks with PID control is expected to fully leverage the advantages of both. Neural networks can adjust the parameters of PID controllers in real-time, enabling them to better adapt to the complex dynamic changes and external disturbances of quadcopter aircraft. This fusion control method brings new possibilities for improving the control performance of quadcopter aircraft. This study explores the application of neural network PID in quadcopter aircraft to achieve stable and precise control, laying the foundation for its widespread promotion in practical applications.

Neural network PID control of spherical permanent magnet based on external magnetic field driving system

Abstract The neural network PID control method of the external magnetic field driving system solves the shortcomings of the traditional control method. The controlled object is a spherical permanent magnet. Other detections or operation equipment can be attached to the sphere. In the process of external magnetic field driving, the model-based controller is prone to large fluctuations when the environmental parameters change, which reduces the control performance. The neural network algorithm has good control ability for the highly nonlinear magnetic levitation driving system. PID control system combined with a neural network algorithm has high stability and strong adaptability in the driving process. The test results show that the response output overshoot is 9.322%, and the system fluctuation is low. Self-tuning and adaptive environment changes of PID controller parameters can be realized by neural network control.

Overview and development of PID control

Proportional integral differential control, referred to as PID control, is one of the earliest developed control methods, which is widely used in industrial process control because of its simple control algorithm, high reliability and good robustness. The classic PID controller is the simplest control system, but the actual control object usually has the characteristics of strong interference, transient uncertainty, nonlinearity, etc, so it is difficult to achieve the ideal control effect with the classic PID controller. In addition, with complex parameter tuning methods, it is often difficult to find an optimal result for the parameters of the classical PID controller. These factors lead to the fact that classical PID control cannot be applied to complex systems and high-performance systems, so people continue to develop PID control methods, introduce new models, and develop fuzzy PID controllers, expert PID controllers, neural network PID controllers, and PID controllers based on genetic algorithms. This paper will review the development history of PID controllers, and introduce fuzzy PID controllers, expert PID controllers, neural network PID controllers, and PID controllers based on genetic algorithms. The research status and future development prospects of these controllers are also prospected.

Distributed drive electric vehicle differential assistance particle swarm optimization PID control

To improve the vehicle’s handling stability and enhance the steering assist effect of distributed drive electric vehicles, a differential assist steering control strategy is proposed based on the Particle Swarm Optimization (PSO) algorithm to optimize PID control parameters. A dynamic model of steering torque considering the influence of tire force is established, based on the reference steering torque to calculate the required torque. The PSO algorithm with the Integral of Time-weighted Absolute Error (ITAE) is used to optimize the parameters in the PID steering control, and wheel torque allocation is performed by using the objective function constraint. Simulation results show that the differential assistance can meet the driving requirements of drivers at both high and low speeds, and the proposed control strategy is effective.

Enhanced Whale Optimization Algorithm for Fuzzy Proportional–Integral–Derivative Control Optimization in Unmanned Aerial Vehicles

The traditional PID controller in quadrotor UAVs has poor performance, a large overshoot, and a long adjustment time, which limit its stability and accuracy in practical applications. In order to solve this problem, an improved whale optimization fuzzy PID control strategy based on CRICLE chaos map initialization is proposed, and a detailed simulation analysis was carried out using MATLAB software (MATLAB R2022B). Firstly, to more realistically reflect quadrotor UAVs’ flight behavior, a dynamic simulation model was established, and the dynamics and kinematic characteristics of the aircraft were considered. Then, CRICLE chaotic mapping initialization was introduced to improve the global search ability of the whale optimization algorithm and to effectively initialize the parameters of the fuzzy PID controller. This improved initialization method helped to speed up the convergence process and improve the stability of the control system. In the simulation experiments, we compared the performance indicators of the improved CRICLE chaotic mapping initialization whale optimization fuzzy PID controller to the traditional PID and fuzzy PID controllers, including overshoot, adjustment time, etc. The results show that the proposed control strategy has better performance than the traditional PID and fuzzy PID controllers, significantly reduces overshoot, and achieves a significant improvement in adjustment time. Therefore, the improved CRICLE chaotic mapping initialization whale optimization fuzzy PID control strategy proposed in this study provides an effective solution for improving the performance of the quadrotor control system and has practical application potential.

Artificial rabbits optimization algorithm based tuning of PID controller parameters for improving voltage profile in AVR system using IoT

The power system is mainly affected by transient situations caused by switching heavy loads. The system may become unstable when transient situations occur. The power system should be able to perform continuous operation to maintain its voltage within acceptable limits. To achieve more stability and increase its speed of response, an Automatic Voltage Regulator (AVR) system requires the inclusion of a controller. The AVR system in the generating station uses the PID controller to adjust the abnormal voltage caused by transient conditions. To maintain the nominal voltage level under all the load conditions in the system, a bio-inspired meta-heuristic algorithm called Artificial Rabbit Optimization (ARO) algorithm is proposed to tune the PID controller gain parameters and obtain the optimal gain, thereby the AVR system adjusts the generator terminal voltage to nominal levels. The ARO algorithm inspires natural survival techniques to improve the AVR performance by reducing errors. To maintain a stable voltage profile in a power system, this research mathematically models survival techniques using the Internet of Things (IoT) to obtain an optimal solution. As a result, all devices connected to the power network receive a stable voltage that ensures their voltage reliability. The effectiveness of the proposed algorithm for the AVR system is verified with the MATLAB R2022a model, and the statistics functions are implemented in the module of Pandas, Scipy and mathematical investigations done in Numpy. The proposed ARO algorithm achieves a better voltage profile with less than 12.63% maximum peak overshoot during the system's transient response. The proposed algorithm provides the fastest response and highest stability comparable to other optimisation algorithms.

Current study on PID control method with optimized BP neural network based on particle swarm optimization

At present, robots show great application value in various industries, and the progress of the industry constantly puts forward higher requirements for the performance of robots, and robot control is an important part of robot application. It is difficult for the traditional PID controller to implement online tuning when it is faced with actual objects. Consequently, the neural network algorithm has been included into robot control methods in recent years; the PID controller based on BP neural network is the subject of this research. This study presents the current state of research, the basic concept behind BP neural networks, how they are used in controller design, and how to optimize the PID parameters of BP neural networks using a particle swarm optimization method. To increase system stability, the neural network is coupled with a PID controller. The adaptive learning capability of the neural network is utilized to modify PID control parameters online in real time. Targeting the trouble that it slips into a local minimum easily during the BP-PID self-learning process and the refined PSO algorithm is used to improve it. It makes sure that the BP-PID system converges to the global optimal solution. The proposed method can effectively improve the system control accuracy and control stability.

Scada Pada Proses Destilasi Dengan Pengendalian Suhu Menggunakan PID

Controlling temperature is crucial because it is the most significant factor in the distillation process. Temperature instability is a result of manual and traditional control, and several errors may occur. Therefore, the low quality of the product that is produced during the distillation process has an additional effect. In order to effectively control or manage the system, an automatic temperature control system is designed using an electronic controller with a closed system control mode that not only delivers setpoint values but also feedback.The procedure of distillation that is being employed involves combining two different kinds of solutions: water and ethanol, which has a boiling point of 78°C.Thermocouple sensors are used to monitor temperature, and PLCs with PID control are used to process the temperature data that are obtained. For plant supervision, control, and data collecting, a SCADA is necessary. A number of tests have been conducted, including near loops with and without PID that are SCADA-monitored. The temperature stability response is then examined by analyzing the outcomes of multiple tests that have been conducted. Values of Kp = 5188, Ti = 182, and Td = 457 were found by using the Ziegler Nichols method to the temperature control of the distillation equipment. Furthermore, the steady state error was no more than 1% and the overshoot characteristics were only 2.5%, according to the system response graph.. From the Ziegler Nichols PID control parameters, it shows a good system response and produces ethanol distillation results with a concentration of 88%.

Application of Improved PSO and BP Hybrid Optimization Algorithm in Electrical Automation Intelligent Control

A fuzzy RBF-PID control strategy based on particle swarm optimization (PSO) algorithm is proposed to solve the problem of large inertia lag in temperature control system of industrial production refuse furnace. In this control system, an improved particle swarm optimization algorithm combined with inertia weight and genetic transformation was used to optimize the initial values of membership functions of fuzzy RBF (radial basis function). Then, BP (error backpropagation) algorithm is used for fine tuning, and fuzzy reasoning and RBF learning ability are combined to adjust the PID control parameters online to achieve the optimal PID control effect. The simulation results show that the algorithm has fast tracking, small overshoot, and is not easily trapped in local minima. At the same time, its robustness and anti-interference performance are better than traditional PID control.