scholarly journals Speed control of DC motor using fractional order PID controller based on particle swarm optimization

2021 ◽  
Vol 22 (3) ◽  
pp. 1345
Author(s):  
Ghassan A. Sultan ◽  
Amer F. Sheet ◽  
Satar M. Ibrahim ◽  
Ziyad K. Farej
Keyword(s):  
Particle Swarm Optimization ◽  
Fractional Order ◽  
Pid Controller ◽  
High Efficiency ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Performance Improvements ◽  
Controlling System ◽  
And Performance

Due to the required different speeds and important role of direct current (DC) motors in laboratories, production factories and industrial application, speed controlling of these motors becomes an essential matter for proper operation with high efficiency and performance accuracy. This paper presents a new speed controlling technique that is based on particle swarm optimization (PSO) algorithm in the optimization process of the parameters for the fractional order proportional–integral–derivative (FOPID) controller. The FOPID is an advanced and modern controlling system in which the two more added parameters (the derivative μ and integral λ orders) are fractional rather than integer. Through the process of minimizing the fitness functions, the obtained results show that the designed controller system can excellently set the best controller parameters due to the fractions of these additional parameters. With respect to the PSO-PID controller, the simulation results for the proposed PSO-FOPID controller show performance improvements of 14%, 21%, 24.5%, 78%, and 19.3% in the values of the parameters Kp, Ki, Kd, Tr, and Ts respectively.

Quantitative Feedback Theory control design using particle swarm optimization method

2011 ◽  
Vol 34 (4) ◽  
pp. 463-476 ◽  
Author(s):  
Hazem I Ali ◽  
Samsul Bahari B Mohd Noor ◽  
SM Bashi ◽  
Mohammad Hamiruce Marhaban
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Design Procedure ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Quantitative Feedback Theory ◽  
Swarm Optimization ◽  
Servo Actuator ◽  
Actuator System ◽  
And Performance

In this paper, a particle swarm optimization (PSO) method is proposed to design Quantitative Feedback Theory (QFT) control. This method minimizes a proposed cost function subject to appropriate robust stability and performance QFT constraints. The PSO algorithm is simple and easy to implement, and can be used to automate the loop shaping procedures of the standard QFT. The proposed method is applied to the high uncertainty pneumatic servo actuator system as an example to illustrate the design procedure of the proposed algorithm. The proposed method is compared with the standard QFT control. The results show that the superiority of the proposed method in that it can achieve the same robustness requirements of standard QFT control with simple structure and low order controller.

scholarly journals Comparison of Particle Swarm Optimization and Ziegler Nichols Techniques

2020 ◽  
pp. 184-188
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Optimization Technique ◽  
Heuristic Method ◽  
Particle Swarm ◽  
Absolute Error ◽  
Pso Algorithm ◽  
Computational Method ◽  
Swarm Optimization ◽  
Tuning Method

Two tuning techniques namely: Particle Swarm Optimization (PSO) and Ziegler Nichols (ZN) technique are compared. PSO is an optimization technique based on the movement and intelligence of swarms. PSO applies the concept of social interaction to problem solving. It is a computational method that optimizes a problem by iteratively trying to improve a candidate solution about a given measure of quality. Ziegler Nichols tuning method is a heuristic method of tuning a PID controller. The ZN close loop tuning is performed by setting the I (integral) and D (derivative) gains to zero and increasing proportional gain to obtain sustained oscillations. The DC Motor is represented by second order transfer function is used as a plant, which is controlled using PID controller. The PID controller parameters are chosen by tuning the controller using PSO algorithm and ZN method. The response of the system to unit step input is plotted and performance measures are evaluated for comparing PSO algorithm and ZN technique. Here we have compared the two tuning methods based upon the settling time (Ts), peak overshoot (Mp) and the two performance indices namely Integral square error (ISE) and Integral Absolute error (IAE).

scholarly journals Solar System Array by Fuzzy Logic based on MPPT Algorithm

2020 ◽  
Vol 9 (6) ◽  
pp. 1132-1139
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Solar System ◽  
Pid Controller ◽  
Performance Enhancement ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Point Tracking ◽  
Power Point

In recent times a huge attention has been given on development of proper planning In this paper we present a top dimension perspective on forefront status of Closed circle ID system the use of PID Controller from explicit creators. The proportional– integral– subsidiary (PID) controller is the most extreme comprehensively ordinary controller inside the business bundles, specifically in strategy enterprises in light of fabulous expense to profit proportion. In this paper we focus on MPPT based solar system performance enhancement by use of fuzzy logic controller’s designs optimized by particle swarm optimization (PSO). We have described about different latest A.I. techniques that has been hybrid with fuzzy logic for improving PV array based solar plants performance in recent time. The artificial intelligence technique applied in this work is the Particle Swarm Optimization (PSO) algorithm and is used to optimize the membership functions for maximum power point tracking rule set of the FLC. By using PSO algorithm, the optimized FLC is able to maximize energy to the system loads while also maintaining a higher stability and speed as compared to P& O based MPPT algorithm

scholarly journals Design Tunable Robust Controllers for Unmanned Aerial Vehicle Based on Particle Swarm Optimization Algorithm

2019 ◽  
Vol 15 (2) ◽  
pp. 89-100
Author(s):  
Baqir Abdul-Samed ◽  
Ammar Aldair
Keyword(s):  
Particle Swarm Optimization ◽  
High Efficiency ◽  
Low Cost ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Pid Controllers ◽  
Swarm Optimization ◽  
Controlled System ◽  
Main Challenge ◽  
Optimal Values

PID controller is the most popular controller in many applications because of many advantages such as its high efficiency, low cost, and simple structure. But the main challenge is how the user can find the optimal values for its parameters. There are many intelligent methods are proposed to find the optimal values for the PID parameters, like neural networks, genetic algorithm, Ant colony and so on. In this work, the PID controllers are used in three different layers for generating suitable control signals for controlling the position of the UAV (x,y and z), the orientation of UAV (θ, Ø and ψ) and for the motors of the quadrotor to make it more stable and efficient for doing its mission. The particle swarm optimization (PSO) algorithm is proposed in this work. The PSO algorithm is applied to tune the parameters of proposed PID controllers for the three layers to optimize the performances of the controlled system with and without existences of disturbance to show how the designed controller will be robust. The proposed controllers are used to control UAV, and the MATLAB 2018b is used to simulate the controlled system. The simulation results show that, the proposed controllers structure for the quadrotor improve the performance of the UAV and enhance its stability.

Robustness Study of Fractional Order PID Controller Optimized by Particle Swarm Optimization in AVR System

2016 ◽  
Vol 6 (5) ◽  
pp. 2033 ◽  
Author(s):  
N. Ramesh Raju ◽  
P. Linga Reddy
Keyword(s):  
Particle Swarm Optimization ◽  
Fractional Order ◽  
Pid Controller ◽  
Design Method ◽  
Particle Swarm ◽  
Superior Performance ◽  
Swarm Optimization ◽  
Modeling And Control ◽  
Avr System ◽  
Fractional Order Pid

<p>In this paper a novel design method for determining fractional order PID (PI<sup>λ</sup>D<sup>µ</sup>) controller parameters of an AVR system using particle swarm optimization algorithm is presented. This paper presents how to employ the particle swarm optimization to seek efficiently the optimal parameters of PI<sup>λ</sup>D<sup>µ</sup> controller. The robustness study is made for this controller against parameter variation of AVR system. This work has been simulated in MATLAB environment with FOMCON (Fractional Order Modeling and Control) tool box.The proposed PSOPI<sup>λ</sup>D<sup>µ</sup> controller has superior performance and robust compared to GA tuned PI<sup>λ</sup>D<sup>µ</sup> controller. The results are also compared with PSO tuned PID controller.</p>

Sign in / Sign up

Export Citation Format

Share Document