scholarly journals Automatic Tuning Of Proportional-Integral-Derivative (Pid) Controller Using Particle Swarm Optimization (Pso) Algorithm

2011 ◽  
Vol 2 (4) ◽  
pp. 25-34 ◽  
Author(s):  
Bassi ◽  
Mishra ◽  
Omizegba
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Automatic Tuning ◽  
Proportional Integral Derivative ◽  
Swarm Optimization ◽  
Proportional Integral

OPTIMAL TUNING OF A PID CONTROLLER FOR EMDAP-CVT USING PARTICLE SWARM OPTIMIZATION

2015 ◽  
Vol 75 (11) ◽  
Author(s):  
M. Azwarie Mat Dzahir ◽  
Mohamed Hussein ◽  
Bambang Supriyo ◽  
Kamarul Baharin Tawi ◽  
Mohd Shafiek Yaakob ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Proportional Integral Derivative ◽  
Swarm Optimization ◽  
Controlled System ◽  
Optimal Tuning ◽  
Proportional Integral ◽  
Variable Transmission

This paper looked into optimal tuning of a Proportional-Integral-Derivative (PID) controller used in Electro-mechanical Dual Acting Pulley Continuously Variable Transmission (EMDAP-CVT) system for controlling the output obtained, and hence, to minimize the integral of absolute errors (IAE). The main objective was to obtain a stable, robust, and controlled system by tuning the PID controller by using Particle Swarm Optimization (PSO) algorithm. The incurred value was compared with the traditional tuning techniques like Ziegler-Nichols and it had been proven better. Hence, the results established that tuning the PID controller using PSO technique offered less overshoot, a less sluggish system, and reduced IAE.

Optimum Setting of DC Drives PID Controller via Particle Swarm Optimization

2012 ◽  
Vol 157-158 ◽  
pp. 88-93 ◽  
Author(s):  
Guang Hui Chang ◽  
Jie Chang Wu ◽  
Chao Jie Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Position Control ◽  
Particle Swarm ◽  
Dc Motor ◽  
Motor Drive ◽  
Proportional Integral Derivative ◽  
Stable Convergence ◽  
Swarm Optimization ◽  
Simulation Results

In this paper, an intelligent controller of PM DC Motor drive is designed using particle swarm optimization (PSO) method for tuning the optimal proportional-integral-derivative (PID) controller parameters. The proposed approach has superior feature, including easy implementation, stable convergence characteristics and very good computational performances efficiency.To show the validity of the PID-PSO controller, a DC motor position control case is considered and some simulation results are shown. The DC Motor Scheduling PID-PSO controller is modeled in MATLAB environment.. It can be easily seen from the simulation results that the proposed method will have better performance than those presented in other studies.

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 PMSM Speed Control Based on Particle Swarm Optimization and Deep Deterministic Policy Gradient under Load Disturbance

Machines ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 343
Author(s):  
Chiao-Sheng Wang ◽  
Chen-Wei Conan Guo ◽  
Der-Min Tsay ◽  
Jau-Woei Perng
Keyword(s):  
Particle Swarm Optimization ◽  
Permanent Magnet Synchronous Motor ◽  
Particle Swarm ◽  
Speed Control ◽  
Pso Algorithm ◽  
Computational Time ◽  
Swarm Optimization ◽  
Proportional Integral ◽  
Estimation Equations ◽  
Policy Gradient

Proportional integral-based particle swarm optimization (PSO) and deep deterministic policy gradient (DDPG) algorithms are applied to a permanent-magnet synchronous motor to track speed control. The proposed methods, based on notebooks, can deal with time delay challenges, imprecise mathematical models, and unknown disturbance loads. First, a system identification method is used to obtain an approximate model of the motor. The load and speed estimation equations can be determined using the model. By adding the estimation equations, the PSO algorithm can determine the sub-optimized parameters of the proportional-integral controller using the predicted speed response; however, the computational time and consistency challenges of the PSO algorithm are extremely dependent on the number of particles and iterations. Hence, an online-learning method, DDPG, combined with the PSO algorithm is proposed to improve the speed control performance. Finally, the proposed methods are implemented on a real platform, and the experimental results are presented and discussed.

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

Landsman Converter Based Particle Swarm Optimization Technique

2017 ◽  
Vol 8 (3) ◽  
pp. 619
Author(s):  
Ramesh P. ◽  
V. Mathivanan
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Dynamic Performance ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Control Technique ◽  
Swarm Optimization ◽  
Particle Swarm Optimization Technique ◽  
Comparative Results

This paper proposes a novel control technique for landsman converter using particle swarm optimization. The controller parameters are optimized by pso algorithm,the proposed algorithm is compared with pid controller and the comparative results are presented. Simulation results shows the dynamic performance of pso controller. landsman converter reduction in output voltage ripple in the order of mV along with reduced settling time as compared to the conventional pid controller . The simulated results are executed in MATLAB/SIMULINK.

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