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.

scholarly journals A New Opinion Mining Method based on Fuzzy Classifier and Particle Swarm Optimization (PSO) Algorithm

2018 ◽  
Vol 18 (2) ◽  
pp. 36-50
Author(s):  
Samira Bordbar ◽  
Pirooz Shamsinejad
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Opinion Mining ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Fuzzy Rules ◽  
Training Data ◽  
Fuzzy Classifier ◽  
Swarm Optimization ◽  
Main Challenge

Abstract Opinion Mining or Sentiment Analysis is the task of extracting people final opinion about something through their unstructured sentiments. The Opinion Mining process is as follows: first, product features which are most important to a user are extracted from his/her comments. Then, sentiments will be emotionally classified using their emotional implications. In this paper we propose an opinion classification method based on Fuzzy Logic. Up to now, a few methods have taken advantage of fuzzy logic in opinion classification and all of them have imported fuzzy rules into system as background knowledge. But the main challenge here is finding the fuzzy rules. Our contribution is to automatically extract fuzzy rules and their parameters from training data. Here we have used the Particle Swarm Optimization (PSO) algorithm to extract fuzzy rules from training data. Also, for better results we have devised a mutation-based PSO. All proposed methods have been implemented and tested on relevant data. Results confirm that our method can reach better accuracy than current state of the art methods in this domain.

scholarly journals Design and simulation of a 2DOF PID controller based on particle swarm optimization algorithms for a thermal phase of hybrid vehicle

2018 ◽  
Vol 7 (4) ◽  
pp. 4644
Author(s):  
Mohamed Hedi Hmidi ◽  
Ines Ben Salem ◽  
Lilia El Amraoui
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Hybrid Vehicle ◽  
Pid Controllers ◽  
Stochastic Algorithm ◽  
Systematic Design ◽  
Swarm Optimization ◽  
Vehicle Velocity ◽  
Two Degree Of Freedom

This paper deals with the systematic design of a PID regulators with two degree of freedom 2DOF for a Hybrid vehicle driving cycle based on different variants of the Particle Swarm Optimization (PSO) algorithm. The PID 2DOF problem for the stabilization of the velocity dynamics of the hybrid vehicle are formulated as a constrained optimization problem and solved thanks to improved PSO algorithms. Both PSO algorithm with variable inertia weight (PSO-In), PSO with Constriction factor (PSO-Co), PSO with possibility updating strategies (PSO-gbest) are proposed. Such variants of the PSO algorithm aim to further improve the exploration and exploitation capabilities of such a stochastic algorithm as well as its convergence fastness. All optimized 2DOF PID controllers are then simulated within a Matlab Simulink. Demonstrative simulation results are presented, compared and discussed in order to improve the effectiveness of the proposed PSO-based 2 DOF controllers for the hybrid Vehicle velocity stabilization.  

scholarly journals Comparison between butterfly optimization algorithm and particle swarm optimization for tuning cascade PID control system of PMDC motor

2021 ◽  
Vol 12 (2) ◽  
pp. 736
Author(s):  
Kareem G. Abdulhussein ◽  
Naseer M. Yasin ◽  
Ihsan J. Hasan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Pso Algorithm ◽  
Modern Method ◽  
Pid Controllers ◽  
Swarm Optimization ◽  
Permanent Magnet Dc Motor ◽  
Tracking Trajectory

In this paper, two optimization methods are used to adjust the gain values for the cascade PID controller. These algorithms are the butterfly optimization algorithm (BOA), which is a modern method based on tracking the movement of butterflies to the scent of a fragrance to reach the best position and the second method is particle swarm optimization (PSO). The PID controllers in this system are used to control the position, velocity, and current of a permanent magnet DC motor (PMDC) with an accurate tracking trajectory to reach the desired position. The simulation results using the Matlab environment showed that the butterfly optimization algorithm is better than the particle swarming optimization (PSO) in terms of performance and overshoot or any deviation in tracking the path to reach the desired position. While an overshoot of 2.557% was observed when using the PSO algorithm, and a position deviation of 7.82 degrees was observed from the reference position.

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.

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.

scholarly journals Chaotic theory incorporated with PSO algorithm for solving optimal reactive power dispatch problem of power system

2021 ◽  
Vol 22 (3) ◽  
pp. 1739
Author(s):  
Shaima Hamdan Shri ◽  
Ayad Fadhil Mijbas
Keyword(s):  
Particle Swarm Optimization ◽  
High Efficiency ◽  
Reactive Power ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Optimal Reactive Power Dispatch ◽  
Light Load ◽  
Power Dispatch ◽  
Reactive Power Dispatch

In this paper, the chaotic particle swarm optimization (CPSO) algorithm is combined with MATPOWER toolbox and used as an optimization tool for attaining solving the optimal reactive power dispatch (RPD) problem, by finding the optimal adjustment of reactive power control variables like a voltage of generator buses (VG), capacitor banks (QC) and transformer taps (Tap) while satisfying some of equality and inequality constraints at the same time. CPSO and Simple PSO algorithms will be checked in a large system such as IEEE node -118. CPSO and Simple PSO algorithms have been implemented and simulated in the MATLAB program, version (R2013b/m-file). Then compassion these results with the results obtained in the other algorithms in the literature like the comprehensive learning particle swarm optimization (CLPSO) algorithm. The simulation results confirm that the CPSO algorithm has high efficiency and ability in terms of decrease real power losses (P loss), and improve voltage profile compared with the obtained by using the simple (PSO) algorithm and (CLPSO) at light load.

Design and co-simulation of a fuzzy gain-scheduled PID controller based on particle swarm optimization algorithms for a quad tilt wing unmanned aerial vehicle

2018 ◽  
Vol 40 (14) ◽  
pp. 3933-3952 ◽  
Author(s):  
Khaled Ben Khoud ◽  
Soufiene Bouallègue ◽  
Mounir Ayadi
Keyword(s):  
Particle Swarm Optimization ◽  
Computer Aided Design ◽  
Pid Controller ◽  
Particle Swarm ◽  
Data Communication ◽  
Pso Algorithm ◽  
Pid Controllers ◽  
Stochastic Algorithm ◽  
Swarm Optimization ◽  
Gain Scheduled

This paper deals with the systematic design and hardware co-simulation of a fuzzy gain-scheduled proportional–integral–derivative (GS-PID) controller for a quad tilt wing (QTW) type of unmanned aerial vehicles (UAVs) based on different variants of the particle swarm optimization (PSO) algorithm. The fuzzy PID gains scheduling problem for the stabilization of the roll, pitch and yaw dynamics of the QTW vehicle is formulated as a constrained optimization problem and solved thanks to improved PSO algorithms. PSO algorithms with variable inertia weight (PSO-In), PSO with constriction factor (PSO-Co) and PSO with possibility updating strategies (PSO-gbest) are proposed. Such variants of the PSO algorithm aim further to improve the exploration and exploitation capabilities of such a stochastic algorithm as well as its convergence fastness. The robustness of the designed PSO-based fuzzy GS-PID controllers under actuators faults is shown on the non-linear model of the QTW. All optimized fuzzy GS-PID controllers are then co-simulated within a processor-in-the-loop (PIL) framework based on an embedded NI myRIO-1900 board and a host PC. Such a proposed software (SW) and hardware (HW) computer aided design (CAD) platform is based on the Control Design and Simulation (CDSim) module of the LabVIEW environment as well as a set-up Network Streams-based data communication protocol. Demonstrative simulation results are presented, compared and discussed in order to improve the effectiveness of the proposed PSO-based fuzzy gains scheduled PID controllers for the QTW’s attitude flight stabilization.

scholarly journals Virtual Angle Boundary-Aware Particle Swarm Optimization to Maximize the Coverage of Directional Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2868
Author(s):  
Gong Cheng ◽  
Huangfu Wei
Keyword(s):  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
The Internet ◽  
Directional Sensor Networks ◽  
Swarm Optimization ◽  
Coverage Optimization ◽  
Boundary Constraint ◽  
Directional Sensor

With the transition of the mobile communication networks, the network goal of the Internet of everything further promotes the development of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs). Since the directional sensor has the performance advantage of long-term regional monitoring, how to realize coverage optimization of Directional Sensor Networks (DSNs) becomes more important. The coverage optimization of DSNs is usually solved for one of the variables such as sensor azimuth, sensing radius, and time schedule. To reduce the computational complexity, we propose an optimization coverage scheme with a boundary constraint of eliminating redundancy for DSNs. Combined with Particle Swarm Optimization (PSO) algorithm, a Virtual Angle Boundary-aware Particle Swarm Optimization (VAB-PSO) is designed to reduce the computational burden of optimization problems effectively. The VAB-PSO algorithm generates the boundary constraint position between the sensors according to the relationship among the angles of different sensors, thus obtaining the boundary of particle search and restricting the search space of the algorithm. Meanwhile, different particles search in complementary space to improve the overall efficiency. Experimental results show that the proposed algorithm with a boundary constraint can effectively improve the coverage and convergence speed of the algorithm.

Adaptive particle swarm optimization algorithm based long short-term memory networks for sentiment analysis

2021 ◽  
pp. 1-17
Author(s):  
J. Shobana ◽  
M. Murali
Keyword(s):  
Particle Swarm Optimization ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Short Term Memory ◽  
Contextual Information ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Proposed Model

Text Sentiment analysis is the process of predicting whether a segment of text has opinionated or objective content and analyzing the polarity of the text’s sentiment. Understanding the needs and behavior of the target customer plays a vital role in the success of the business so the sentiment analysis process would help the marketer to improve the quality of the product as well as a shopper to buy the correct product. Due to its automatic learning capability, deep learning is the current research interest in Natural language processing. Skip-gram architecture is used in the proposed model for better extraction of the semantic relationships as well as contextual information of words. However, the main contribution of this work is Adaptive Particle Swarm Optimization (APSO) algorithm based LSTM for sentiment analysis. LSTM is used in the proposed model for understanding complex patterns in textual data. To improve the performance of the LSTM, weight parameters are enhanced by presenting the Adaptive PSO algorithm. Opposition based learning (OBL) method combined with PSO algorithm becomes the Adaptive Particle Swarm Optimization (APSO) classifier which assists LSTM in selecting optimal weight for the environment in less number of iterations. So APSO - LSTM ‘s ability in adjusting the attributes such as optimal weights and learning rates combined with the good hyper parameter choices leads to improved accuracy and reduces losses. Extensive experiments were conducted on four datasets proved that our proposed APSO-LSTM model secured higher accuracy over the classical methods such as traditional LSTM, ANN, and SVM. According to simulation results, the proposed model is outperforming other existing models.

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