PSO-based PID Research Articles

Load Frequency Control of Interconnected Power System with non-linearities using PSO-based Fuzzy PID Control Approach

Load Frequency Control of Interconnected Power System with non-linearities using PSO-based Fuzzy PID Control Approach

Particle swarm optimization solution for roll-off control in radiofrequency ablation of liver tumors: Optimal search for PID controller tuning.

The study investigates the efficacy of a bioinspired Particle Swarm Optimization (PSO) approach for PID controller tuning in Radiofrequency Ablation (RFA) for liver tumors. Ex-vivo experiments were conducted, yielding a 9th order continuous-time transfer function. PSO was applied to optimize PID parameters, achieving outstanding simulation results: 0.605% overshoot, 0.314 seconds rise time, and 2.87 seconds settling time for a unit step input. Statistical analysis of 19 simulations revealed PID gains: Kp (mean: 5.86, variance: 4.22, standard deviation: 2.05), Ki (mean: 9.89, variance: 0.048, standard deviation: 0.22), Kd (mean: 0.57, variance: 0.021, standard deviation: 0.14) and ANOVA analysis for the 19 experiments yielded a p-value ≪ 0.05. The bioinspired PSO-based PID controller demonstrated remarkable potential in mitigating roll-off effects during RFA, reducing the risk of incomplete tumor ablation. These findings have significant implications for improving clinical outcomes in hepatocellular carcinoma management, including reduced recurrence rates and minimized collateral damage. The PSO-based PID tuning strategy offers a practical solution to enhance RFA effectiveness, contributing to the advancement of radiofrequency ablation techniques.

Design and Implementation of PID Controller for the Cooling Tower's pH Regulation Based on Particle Swarm Optimization PSO Algorithm

The PH regulation of cooling tower plant in southern fertilizers company (SCF) in Iraq is important for industry pipes protection and process continuity. According to the Mitsubishi standard, the PH of cooling water must be around (7.1 to 7.8). The deviation in PH parameter affects the pipes, such as corrosion and scale. Acidic water causes pipes to corrode, and alkaline water causes pipes to scale. The sulfuric acid solution is used for PH neutralization. The problem is that the sulfuric acid is pumped manually in the cooling tower plant every two or three hours for PH regulation. The manual operation of the sulfuric acid pump makes deviations in the PH parameter. It is very difficult to control the PH manually. To solve this problem, a PID controller for PH regulation was used. The reason for using the PID controller is that the PH response is irregular through the neutralization process. The methodology is to calculate the transfer function of the PH loop using the system identification toolbox of MATLAB, to design and implement a PID controller, to optimize the PID controller response using particle swarm optimization PSO algorithm, and to make a comparison among several tuning methods such as Ziegler Nichols (ZN) tuning method, MATLAB tuner method, and PSO algorithm tuning method. The results showed that the PSO-based PID controller tuning gives a better overshoot, less rise time, and an endurable settling time than the other tuning methods. Hence, the PH response became according to the target range. The experimental results showed that the PH regulation improved using the PSO-based PID controller tuning.

Perancangan Sistem Kontrol Tekanan Pada Reaktor Biogas Berbasis PID Controller dengan Ziegler-Nichols dan Auto Tuning PSO

Biogas, an eco-friendly energy source, significantly reduces greenhouse gas emissions by converting organic waste or biomass into methane. However, the quality of biogas, primarily determined by methane content and pressure variables, directly impacts its efficiency and safety. This study, which aims to design a pressure control system and monitor methane gas levels in a biogas reactor using a PID Controller with Particle Swarm Optimization (PSO), provides crucial insights into improving biogas production efficiency and safety. Tests were conducted on a fixed dome-type biogas reactor in the East Java region, Indonesia. The PSO-based PID tuning was compared with two other PID tuning methods: Trial-error and Ziegler-Nichols (ZN). Data on pressure values and biogas flow rates from the biogas reactor were collected, and system modeling was carried out to produce a system transfer function. The PSO algorithm was developed to optimize PID control parameters. The results of this research demonstrate that the PID tuning method using PSO produces a stable state error value of 1.40%, rise time of 0.09 s, settling time of 2.1 s, maximum overshoot of 0.986404, and ITAE of 0.0010801. The implementation of PID-PSO can significantly enhance PID control performance in rise time, settling time, maximum overshoot, and ITAE compared to Trial-Error and ZN, while the stable state error value in PID-PSO tuning is higher than tuning using Trial-Error and ZN.

Development of a Control System for Double-Pendulum Active Spray Boom Suspension Based on PSO and Fuzzy PID

During the operation of boom sprayers in the field, it is crucial to ensure that the entire boom is maintained at an optimal height relative to the ground or crop canopy. Active suspension is usually used to adjust the height. A control system for double-pendulum active suspension was developed in this paper. The control system consisted of a main control node, two distance measurement nodes, a vehicle inclination detection node, and an execution node. Communication between nodes was carried out using a CAN bus. The hardware was selected, and the interface circuits of the sensors and the actuator were designed. The transfer functions of the active suspension and electric linear actuator were established. In order to enhance the efficiency of the control system, the particle swarm optimization (PSO) algorithm was employed to optimize the initial parameters of the fuzzy PID controller. The simulation results demonstrated that the PSO-based fuzzy PID controller exhibited improvements in terms of reduced overshoot and decreased settling time when compared to conventional PID and fuzzy PID controllers. The experimental results showed that the active suspension system equipped with the control system could effectively isolate high-frequency disturbances and follow low-frequency ground undulations, meeting the operational requirements.

Tuning Pid Controller for A Textile Web Tension Control System Using Particle Swarm Optimization Technique (Dept.T)

In this paper, a new method for tuning the parameters of PID controller for Textile Web Tension Control (TWTC) using Particle Swarm Optimization (PSO) technique is presented. The robustness of the proposed controller is investigated through parameter variations and changing the textile web cross-section area. The simulation results show that the applied PSO-based PID controller has achieved good performance even in wide range of cross-section area. A comparative study is made between a traditional PID controller and the proposed one. The performance is shown to be better in case of the PSO-based PID controller.

PSO-based PID Speed Control of Traveling Wave Ultrasonic Motor under Temperature Disturbance

Traveling wave ultrasonic motors (TWUSMs) have a time varying dynamics characteristics. Temperature rise in TWUSMs remains a problem particularly in sustaining optimum speed performance. In this study, a PID controller is used to control the speed of TWUSM under temperature disturbance. Prior to developing the controller, a linear approximation model which relates the speed to the temperature is developed based on the experimental data. Two tuning methods are used to determine PID parameters: conventional Ziegler-Nichols(ZN) and particle swarm optimization (PSO). The comparison of speed control performance between PSO-PID and ZN-PID is presented. Modelling, simulation and experimental work is carried out utilizing Fukoku-Shinsei USR60 as the chosen TWUSM. The results of the analyses and experimental work reveal that PID tuning using PSO-based optimization has the advantage over the conventional Ziegler-Nichols method.

PSO-Based PID Controller for AVR Systems Concerned with Design Specification

PSO-Based PID Controller for AVR Systems Concerned with Design Specification

PSO-based PID controller design for an energy conversion system using compressed air

PSO-based PID controller design for an energy conversion system using compressed air

PSO-Based PID Controller Design for a Class of Stable and Unstable Systems

Nonlinear processes are very common in process industries, and designing a stabilizing controller is always preferred to maximize the production rate. In this paper, tuning of PID controller for a class of time delayed stable and unstable process models using Particle Swarm Optimization (PSO) algorithm is discussed. The dimension of the search space is only three (, , and ); hence, a fixed weight is assigned for the inertia parameter. A comparative study is presented between various inertia weights such as 0.5, 0.75, and 1. From the result, it is evident that the proposed method helps to attain better controller settings with reduced iteration number. The efficacy of the proposed scheme has been validated through a comparative study with classical controller tuning methods and heuristic methods such as Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Finally, a real-time implementation of the proposed method is carried on a nonlinear spherical tank system. From the simulation and real-time results, it is evident that the PSO algorithm performs well on the stable and unstable process models considered in this work. The PSO tuned controller offers enhanced process characteristics such as better time domain specifications, smooth reference tracking, supply disturbance rejection, and error minimization.

Tuning of PID Controller Using Particle Swarm Optimization (PSO)

The aim of this research is to design a PID Controller using PSO algorithm. The model of a DC motor is used as a plant in this paper. The conventional gain tuning of PID controller (such as Ziegler-Nichols (ZN) method) usually produces a big overshoot, and therefore modern heuristics approach such as genetic algorithm (GA) and particle swarm optimization (PSO) are employed to enhance the capability of traditional techniques. However, due to the computational efficiency, only PSO will be used in this paper. The comparison between PSO-based PID (PSO-PID) performance and the ZN-PID is presented. The results show the advantage of the PID tuning using PSO-based optimization approach.

PSO-based robust PID control for flexible manipulator systems

In this paper, a new control law is designed for flexible manipulator systems. Kharitonov theorem is used as a design tool to derive the robust control in robotic systems. Stability is guaranteed not only at one operating condition but also for a wide range of system uncertainty. Both the non-linear behaviour of the gearbox stiffness and the end load variations are tackled as interval uncertainties in the model. Particle swarm optimisation (PSO) is used to tune controller parameters, such that, the greatest real parts of closed loop eigenvalues among Kharitonov-extreme polynomials is minimised to assure the highest possible relative stability. The flexible manipulator is a realistic industrial benchmark robot manipulator. The proposed controller is tested as well for rejecting disturbances injected at the motor and the tool. The robustness performance of the controller is evaluated in terms of reference tool position tracking in the presence of the mentioned disturbances and uncertainties. The results of ...

PID controller design of nonlinear systems using an improved particle swarm optimization approach

In this paper, an improved particle swarm optimization is presented to search for the optimal PID controller gains for a class of nonlinear systems. The proposed algorithm is to modify the velocity formula of the general PSO systems in order for improving the searching efficiency. In the improved PSO-based nonlinear PID control system design, three PID control gains, i.e., the proportional gain K p , integral gain K i , and derivative gain K d are required to form a parameter vector which is called a particle. It is the basic component of PSO systems and many such particles further constitute a population. To derive the optimal PID gains for nonlinear systems, two principle equations, the modified velocity updating and position updating equations, are employed to move the positions of all particles in the population. In the meanwhile, an objective function defined for PID controller optimization problems may be minimized. To validate the control performance of the proposed method, a typical nonlinear system control, the inverted pendulum tracking control, is illustrated. The results testify that the improved PSO algorithm can perform well in the nonlinear PID control system design.